Search

Salicylic esters (SEs), the widely used ultraviolet (UV) absorbers in sunscreen products, have been found to have health risks such as skin sensitization and estrogenic effects. This study aims to design SE substitutes that maintain high UV absorbance while reducing estrogenicity. Using molecular docking and Gaussian09 software for initial assessments and further application of a combination of two-dimensional and three-dimensional quantitative structure-activity relationships (2D-QSAR and 3D-QSAR, respectively) models, we designed 73 substitutes. The best-performing molecules, ethylhexyl salicylate (EHS)-5 and EHS-15, significantly reduced estrogenicity (44.54 % and 17.60 %, respectively) and enhanced UV absorbance (249.56 % and 46.94 %, respectively). Through screening for human health risks, we found that EHS-5 and EHS-15 were free from skin sensitivity and eye irritation and exhibited reduced skin permeability compared with EHS. Furthermore, the photolysis and synthetic pathways of EHS-5 and EHS-15 were deduced, demonstrating their good photodegradability and potential synthesizability. In addition, we analyzed the mechanisms underlying the changes in estrogenic effects and UV absorption properties. We identified covalent hydrogen bond basicity and acidity Propgen value for atomic molecular properties and the highest occupied molecular orbital eigenvalue as the main factors affecting the estrogenic effect and UV absorbance of SEs, respectively. This study focuses on the design and screening of SEs, exhibiting enhanced functionality, reduced health risks, and synthetic feasibility., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The global outbreak of the COVID-19 pandemic caused by the SARS-CoV-2 virus had led to profound respiratory health implications. This study focused on designing organoselenium-based inhibitors targeting the SARS-CoV-2 main protease (M pro ). The ligand-binding pathway sampling method based on parallel cascade selection molecular dynamics (LB-PaCS-MD) simulations was employed to elucidate plausible paths and conformations of ebselen, a synthetic organoselenium drug, within the M pro catalytic site. Ebselen effectively engaged the active site, adopting proximity to H41 and interacting through the benzoisoselenazole ring in a π-π T-shaped arrangement, with an additional π-sulfur interaction with C145. In addition, the ligand-based drug design using the QSAR with GFA-MLR, RF, and ANN models were employed for biological activity prediction. The QSAR-ANN model showed robust statistical performance, with an r 2 training exceeding 0.98 and an RMSE test of 0.21, indicating its suitability for predicting biological activities. Integration the ANN model with the LB-PaCS-MD insights enabled the rational design of novel compounds anchored in the ebselen core structure, identifying promising candidates with favorable predicted IC 50 values. The designed compounds exhibited suitable drug-like characteristics and adopted an active conformation similar to ebselen, inhibiting M pro function. These findings represent a synergistic approach merging ligand and structure-based drug design; with the potential to guide experimental synthesis and enzyme assay testing., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Polycystic kidney disease is the most prevalent hereditary kidney disease globally and is mainly linked to the overexpression of a gene called PKD1. To date, there is no effective treatment available for polycystic kidney disease, and the practicing treatments only provide symptomatic relief. Discovery of the compounds targeting the PKD1 gene by inhibiting its expression under the disease condition could be crucial for effective drug development. In this study, a molecular docking and molecular dynamic simulation, QSAR, and MM/GBSA-based approaches were used to determine the putative inhibitors of the Pkd1 enzyme from a library of 1379 compounds. Initially, fourteen compounds were selected based on their binding affinities with the Pkd1 enzyme using MOE and AutoDock tools. The selected drugs were further investigated to explore their properties as drug candidates and the stability of their complex formation with the Pkd1 enzyme. Based on the physicochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, and toxicity profiling, two compounds including olsalazine and diosmetin were selected for the downstream analysis as they demonstrated the best drug-likeness properties and highest binding affinity with Pkd1 in the docking experiment. Molecular dynamic simulation using Gromacs further confirmed the stability of olsalazine and diosmetin complexes with Pkd1 and establishing interaction through strong bonding with specific residues of protein. High biological activity and binding free energies of two complexes calculated using 3D QSAR and Schrodinger module, respectively further validated our results. Therefore, the molecular docking and dynamics simulation-based in-silico approach used in this study revealed olsalazine and diosmetin as potential drug candidates to combat polycystic kidney disease by targeting Pkd1 enzyme., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Salmons are crucial to ecosystems and economic activities like commercial fishing and aquaculture, while also serving as an important source of nutrients, underscoring their ecological significance and the need for sustainable management. To better understand the toxicity and biological interactions between the salmon and industrial chemicals in the aquatic environment, we utilized the ToxValDB database to develop first ever computational toxicity models for six salmon subspecies (covering Atlantic and Pacific salmon) across two genera, employing Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) methods. For three smaller datasets (Oncorhynchus nerka, Oncorhynchus keta, and Oncorhynchus gorbuscha), we created mathematical models using the entire datasets where QSAR models demonstrated superior statistical quality compared to q-RASAR. Conversely, the three larger datasets (Oncorhynchus kisutch, Oncorhynchus tshawytscha, and Salmon salar) were divided into training and test sets, the q-RASAR models yielded better results compared to QSAR models. Mechanistic interpretations of these models revealed that descriptors such as Burden eigenvalues (BCUT), autocorrelation of topological structure (ATSC), and molecular polarizability were significant predictors of toxicity. For instance, higher polarizability and certain topological features were associated with increased toxicity as per the developed models. Statistically superior models for each subspecies were used to predict the aquatic toxicity of 1085 untested organic chemicals for toxicity data gap filling and risk assessment considering the applicability domain (AD). These insights are pivotal for designing safer chemicals and emphasize the need for sustainable management of salmon populations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Schistosomiasis, also known as bilharzia or snail fever, is a tropical parasitic disease resulting from flatworms of the Schistosoma genus. This often overlooked disease has significant impacts in affected regions, causing enduring morbidity, hindering child development, reducing productivity, and creating economic burdens. Praziquantel (PZQ) is currently the only treatment option for schistosomiasis. Given the potential rise of drug resistance and the limited treatment choices available, there is a need to develop more effective inhibitors for this neglected tropical disease (NTD). In view of this, quantitative structure-activity relationship studies (QSAR), molecular docking, molecular dynamics simulations, drug-likeness, and ADMET predictions were applied to 31 inhibitors of Schistosoma mansoni Dihydroorotate dehydrogenase (SmDHODH). The designed QSAR model demonstrated robust statistical parameters including an R2 of 0.911, R2adj of 0.890, Q2cv of 0.686, R2pred of 0.807, and cR2p of 0.825, confirming its robustness. Compound 26, identified as the most active derivative, emerged as a lead candidate for new potential inhibitors through ligand-based drug design. Subsequently, 12 novel compounds (26A-26L) were designed with enhanced inhibition activity and binding affinity. Molecular docking studies revealed strong and stable interactions, including hydrogen bonding and hydrophobic interactions, between the designed compounds and the target receptor. Molecular dynamics simulations over 100 nanoseconds and MM-PBSA free binding energy (ΔGbind) calculations validated the stability of the two best-designed molecules (26A and 26L). Furthermore, drug-likeness and ADMET prediction analyses affirmed the potential of these designed compounds, suggesting their promise as innovative agents for treating schistosomiasis., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ja’afaru et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

With the exponential progress in the field of cheminformatics, the conventional modeling approaches have so far been to employ supervised and unsupervised machine learning (ML) and deep learning models, utilizing the standard molecular descriptors, which represent the structural, physicochemical, and electronic properties of a particular compound. Deviating from the conventional approach, in this investigation, we have employed the classification Read-Across Structure-Activity Relationship (c-RASAR), which involves the amalgamation of the concepts of classification-based quantitative structure-activity relationship (QSAR) and Read-Across to incorporate Read-Across-derived similarity and error-based descriptors into a statistical and machine learning modeling framework. ML models developed from these RASAR descriptors use similarity-based information from the close source neighbors of a particular query compound. We have employed different classification modeling algorithms on the selected QSAR and RASAR descriptors to develop predictive models for efficient prediction of query compounds' hepatotoxicity. The predictivity of each of these models was evaluated on a large number of test set compounds. The best-performing model was also used to screen a true external data set. The concepts of explainable AI (XAI) coupled with Read-Across were used to interpret the contributions of the RASAR descriptors in the best c-RASAR model and to explain the chemical diversity in the dataset. The application of various unsupervised dimensionality reduction techniques like t-SNE and UMAP and the supervised ARKA framework showed the usefulness of the RASAR descriptors over the selected QSAR descriptors in their ability to group similar compounds, enhancing the modelability of the dataset and efficiently identifying activity cliffs. Furthermore, the activity cliffs were also identified from Read-Across by observing the nature of compounds constituting the nearest neighbors for a particular query compound. On comparing our simple linear c-RASAR model with the previously reported models developed using the same dataset derived from the US FDA Orange Book ( https://www.accessdata.fda.gov/scripts/cder/ob/index.cfm ), it was observed that our model is simple, reproducible, transferable, and highly predictive. The performance of the LDA c-RASAR model on the true external set supersedes that of the previously reported work. Therefore, the present simple LDA c-RASAR model can efficiently be used to predict the hepatotoxicity of query chemicals., (© 2024. The Author(s).)

The escalating introduction of pesticides/veterinary drugs into the environment has necessitated a rapid evaluation of their potential risks to ecosystems and human health. The developmental toxicity of pesticides/veterinary drugs was less explored, and much less the large-scale predictions for untested pesticides, veterinary drugs and bio-pesticides. Alternative methods like quantitative structure-activity relationship (QSAR) are promising because their potential to ensure the sustainable and safe use of these chemicals. We collected 133 pesticides and veterinary drugs with half-maximal active concentration (AC 50 ) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R 2 > 0.6 and Q LOO 2 > 0.6) and external predictivity (R test 2 > 0.7, Q Fn 2 >0.7, and CCC test > 0.85). To further enhance the predictive performance of the model, a quantitative read-across structure-activity relationship (q-RASAR) model was established using the combined set of RASAR and 2D descriptors. Mechanistic interpretation revealed that dipole moment, the presence of C-O fragment at 10 topological distance, molecular size, lipophilicity, and Euclidean distance (ED)-based RA function were main factors influencing toxicity. For the first time, the established QSAR and q-RASAR models were combined to prioritize the developmental toxicity of a vast array of true external compounds (pesticides/veterinary drugs/bio-pesticides) lacking experimental values. The prediction reliability of each query molecule was evaluated by leverage approach and prediction reliability indicator. Overall, the dual computational toxicology models can inform decision-making and guide the design of new pesticides/veterinary drugs with improved safety profiles., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Fluoroquinolone antibiotics (FQs) have been used worldwide due to their extended antimicrobial spectrum. However, the overuse of FQs leads to frequent detection in the environment and cannot be efficiently removed. Microalgae-based constructed wetland systems have been proven to be a relatively proper method to treat FQs, mainly by microalgae, plants, microorganisms, and sediments. To improve the removal efficiency of microalgae-constructed wetland, a systematic molecular design, screening, functional, and risk evaluation method was developed using three-dimensional quantitative structure-activity relationship models, molecular dynamics simulation, molecular docking, and TOPKAT approaches. Five designed ciprofloxacin alternatives with improved bactericidal effects and lower human health risks were found to be more easily degraded by microalgae (16.11-167.88 %), plants (6.72-58.86 %), microorganisms (9.10-15.02 %), and sediments (435.83 %-1763.51 %) compared with ciprofloxacin. According to the mechanism analysis, the removal effect of the FQs can be affected via changes in the number, bond energy, and molecular descriptors of favorable and unfavorable amino acids. To the best of our knowledge, this is the first comprehensive study of improving the microalgae, plants, microorganisms, and sediment removal efficiency of FQs in constructed wetlands, which provides theoretical support for the treatment of FQ pollution., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

A new series of thiazolidine-2,4-dione tethered 1,2,3-triazole derivatives were designed, synthesized and screened for their α-amylase inhibitory potential employing in vitro and in silico approaches. The target compounds were synthesized with the help of Cu (I) catalyzed [3 + 2] cycloaddition of terminal alkyne with numerous azides, followed by unambiguously characterizing the structure by employing various spectroscopic approaches. The synthesized derivatives were assessed for their in vitro α-amylase inhibition and it was found that thiazolidine-2,4-dione derivatives 6e, 6j, 6o, 6u and 6x exhibited comparable inhibition with the standard drug acarbose. The compound 6e with a 7-chloroquinolinyl substituent on the triazole ring exhibited significant inhibition potential with IC 50 value of 0.040 μmol mL -1 whereas compound 6c (IC 50  = 0.099 μmol mL -1 ) and 6h (IC 50  = 0.098 μmol mL -1 ) were poor inhibitors. QSAR studies revealed the positively correlating descriptors that aid in the design of novel compounds. Molecular docking was performed to investigate the binding interactions with the active site of the biological receptor and the stability of the complex over a period of 100 ns was examined using molecular dynamics studies. The physiochemical properties and drug-likeliness behavior of the potent derivatives were investigated by carrying out the ADMET studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Liquid crystal monomers (LCMs) are a new class of emerging pollutants with high octanol-water partition coefficients; however, their transformation behavior and associated risk to environments with high organic matter content has rarely been reported. In this study, we investigated the photodegradation kinetics, mechanism, and toxicity variation of 23 LCMs on leaf wax models (e.g., organic solvents methanol and n-hexane). The order of the photolysis rates of these LCMs were biphenylethyne LCMs > phenylbenzoate LCMs > diphenyl/terphenyl LCMs under simulated sunlight, while the phenylcyclohexane LCMs were resistant to photodegradation. The phenylbenzoate and biphenylethyne LCMs mainly undergo direct photolysis, while the diphenyl/terphenyl LCMs mainly undergo self-sensitized photolysis. The main photolysis pathways are the cleavage of ester bonds for phenylbenzoate LCMs, the addition, oxidation and cleavage of alkynyl groups for biphenylethyne LCMs, and the cleavage/oxidation of chains attached to phenyls and the benzene ring opening for diphenyl/terphenyls LCMs. Most photolysis products remained toxic to aquatic organisms to some degree. Additionally, two quantitative structure-activity relationship models for predicting k obs of LCMs in methanol and n-hexane were developed, and employed to predict k obs of 93 LCMs to fill the k obs data gap in systems mimicking leaf surfaces. These results can be helpful for evaluating the fate and risk of LCMs in environments with high content of organic phase., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Computer-aided drug discovery plays a vital role in developing novel medications for various diseases. The COVID-19 pandemic has heightened the need for innovative approaches to design lead compounds with the potential to become effective drugs. Specifically, designing promising inhibitors of the SARS-CoV-2 main protease (Mpro) is crucial, as it plays a key role in viral replication. Phytochemicals, primarily flavonoids and flavonols from medicinal plants, were screened. Fifty small molecules were selected for molecular docking analysis against SARS-CoV-2 Mpro (PDB ID: 6LU7). Binding energies and interactions were analyzed and compared to those of the anti-SARS-CoV-2 inhibitor Nirmatrelvir. Using these 50 structures as a training set, a QSAR model was built employing simple, reversible topological descriptors. An inverse-QSAR analysis was then performed on 2⁹ = 512 hydroxyl combinations at nine possible positions on the flavone and flavonol scaffold. The model predicted three novel, promising compounds exhibiting the most favorable binding energies (-8.5 kcal/mol) among the 512 possible hydroxyl combinations: 3,6,7,2',4'-pentahydroxyflavone (PF9), 6,7,2',4'-tetrahydroxyflavone (PF11), and 3,6,7,4'-tetrahydroxyflavone (PF15). Molecular dynamics (MD) simulations demonstrated the stability of the PF9/Mpro complex over 300 ns of simulation. These predicted structures, reported here for the first time, warrant synthesis and further evaluation of their biological activity through in vitro and in vivo studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Non-alcoholic fatty liver disease (NAFLD) is a growing global health concern due to its potential to progress into severe liver diseases. Targeting the bile acid receptor FXR has emerged as a promising strategy for managing NAFLD. Building upon our previous research on FXR partial agonism, the present study investigates a series of 1,3,4-trisubstituted-pyrazol amide derivatives as FXR antagonists, aiming to delineate the structural features for antagonism. By means of 2D-QSAR (quantitative structure-activity relationships) modelling techniques, we elucidated the key structural elements responsible for the antagonistic properties of these derivatives. We then employed QPhAR, an open-access software, to identify key molecular features within the compounds that enhance their antagonistic activity. Additionally, 3D-QSAR modelling allowed us to analyse the steric and electrostatic fields of aligned 3D structures, further refining our understanding of structure-activity relationships. Subsequent molecular dynamics simulations provided insights into the binding mode interactions between the compounds and FXR, with varying potencies, confirming and complementing the findings from 2D-QSAR, pharmacophore, and 3D-QSAR modelling. Particularly, our study highlighted the significance of hydrophobic interactions in conferring potent antagonism by the 1,3,4-trisubstituted-pyrazol amide derivatives against FXR. Overall, this work underscores the potential of 1,3,4-trisubstituted-pyrazol amides as FXR antagonists for NAFLD treatment. Notably, our reliance on open-access software fosters reproducibility and broadens the accessibility of our findings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

As a pivotal node in modulating various cell behaviors, Rho-associated kinase-1 (ROCK1) has attracted significant attention as a promising therapeutic target in a variety of diseases. Benzamide has been widely reported as a ROCK1 inhibitors in recent years. To better understand its pharmacological properties and to explore its potential inhibitors, a series of ROCK1 inhibitors derived from N-methyl-4-(4-pyrazolidinyl) benzamides (MPBs) were investigated by using three-dimensional quantitative structure-activity relationship (3D-QSAR) models, pharmacophore models, molecular docking, and molecular dynamics (MD) simulation. The comparative Molecular Field Analysis (CoMFA) model ( q 2 = 0.616, R 2 = 0.972, ONC = 4, and r 2 pred = 0.983) and the best Comparative Molecular Similarity Indices Analysis (CoMSIA) model ( q 2 = 0.740, R 2 = 0.982, ONC = 6, and r 2 pred = 0.824) exhibited reliable predictability with satisfactory validation parameters. In the subsequent virtual screening, VS03 and VS05 were identified to have superior predicted activities and higher docking scores, meanwhile they demonstrated to be reasonably stable in the binding pocket through MD simulations. These results provide a significant theoretical direction for the rational design and development of novel ROCK1 inhibitors.Communicated by Ramaswamy H. Sarma.

Models of toxicity to tadpoles have been developed as single parameters based on special descriptors which are sums of correlation weights, molecular features, and experimental conditions. This information is presented by quasi-SMILES. Fragments of local symmetry (FLS) are involved in the development of the model and the use of FLS correlation weights improves their predictive potential. In addition, the index of ideality correlation ( IIC ) and correlation intensity index ( CII ) are compared. These two potential predictive criteria were tested in models built through Monte Carlo optimization. The CII was more effective than IIC for the models considered here.

A completely green protocol was developed for the synthesis of a series of arylaminonaphthol derivatives in the presence of N-ethylethanolamine (NEEA) as a catalyst under ultrasonic irradiation and solventless conditions. The major assets of this methodology were the use of non-toxic organic medium, available catalyst, mild reaction condition, and good to excellent yield of desired products. All of the synthesized products were screened for their in vitro antioxidant activity using DPPH, ABTS, and Ferric-phenanthroline assays and it was found that most of them are potent antioxidant agents. Also, their butyrylcholinesterase inhibitory activity has been investigated in vitro. All tested compounds exhibited potential inhibitory activity toward BuChE when compared to standard reference drug galantamine, however, compounds 4r, 4u, 4 g and 4x gave higher butyrylcholinesterase inhibitory with IC 50 values of 14.78 ± 0.65 µM, 16.18 ± 0.50 µM, 20.00 ± 0.50 µM, and 20.28 ± 0.08 µM respectively. On the other hand, we employed density functional theory (DFT), calculations to analyze molecular geometry and global reactivity descriptors, and MESP analysis to predict electrophilic and nucleophilic attacks. A quantitative structure-activity relationship (QSAR) investigation was conducted on the antioxidant and butyrylcholinesterase properties of 25 arylaminonaphthol derivatives, resulting in robust and satisfactory models. To evaluate their anti-Alzheimer's activity, compounds 4 g, 4q, 4r, 4u, and 4x underwent docking simulations at the active site of the acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), revealing why these compounds displayed superior activity, consistent with the biological findings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Since dysregulation of the phosphatidylinositol 3-kinase (PI3K) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3K has emerged as an attractive target for drug development. Although copanlisib is the first pan-PI3K inhibitor to be approved for clinical use, the precise mechanism by which it acts on PI3K has not been fully elucidated. To reveal the binding mechanisms and structure-activity relationship between PI3K and copanlisib, a comprehensive modeling approach that combines 3D-quantitative structure-activity relationship (3D-QSAR), pharmacophore model, and molecular dynamics (MD) simulation was utilized. Initially, the structure-activity relationship of copanlisib and its derivatives were explored by constructing a 3D-QSAR. Then, the key chemical characteristics were identified by building common feature pharmacophore models. Finally, MD simulations were performed to elucidate the important interactions between copanlisib and different PI3K subtypes, and highlight the key residues for tight-binding inhibitors. The present study uncovered the principal mechanism of copanlisib's action on PI3K at the theoretical level, and these findings might provide guidance for the rational design of pan-PI3K inhibitors.Communicated by Ramaswamy H. Sarma.

To develop a method for predicting chronic toxicity of pharmaceuticals in Daphnia, we investigated the feasibility of combining the presence of drug-target orthologs in Daphnia magna, classification based on pharmacological effects, and ecotoxicity quantitative structure-activity relationship (QSAR) prediction. We established datasets on the chronic toxicity of pharmaceuticals in Daphnia, including information on therapeutic categories, target proteins, and the presence or absence of drug-target orthologs in D. magna, using literature and databases. Chronic toxicity was predicted using ecotoxicity prediction QSAR (Ecological Structure Activity Relationship and Kashinhou Tool for Ecotoxicity), and the differences between the predicted and measured values and the presence or absence of drug-target orthologs were examined. For pharmaceuticals without drug-target orthologs in D. magna or without expected specific actions, the ecotoxicity prediction QSAR analysis yielded acceptable predictions of the chronic toxicity of pharmaceuticals. In addition, a workflow model to assess the chronic toxicity of pharmaceuticals in Daphnia was proposed based on these evaluations and verified using an additional dataset. The addition of biological aspects such as drug-target orthologs and pharmacological effects would support the use of QSARs for predicting the chronic toxicity of pharmaceuticals in Daphnia., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hiroshi Yamamoto reports financial support was provided by Japan Agency of Medical Research and Development. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Among various matrix metalloproteinases (MMPs), MMP-12 is one of the potential targets for cancer and other diseases. However, none of the MMP-12 inhibitors has passed the clinical trials to date. Therefore, designing potential MMP-12 inhibitors as new drug molecules can provide effective therapeutic strategies for several diseases. In this study, a series of dibenzofuran and dibenzothiophene derivatives were subjected to different 2D and 3D-QSAR techniques to point out the crucial structural contributions highly influential toward the MMP-12 inhibitory activity. These techniques identified some structural attributes of these compounds that are responsible for influencing their MMP-12 inhibition. The carboxylic group may enhance proper binding with catalytic Zn 2+ ion at the MMP-12 active site. Again, the i -propyl sulfonamido carboxylic acid function contributed positively toward MMP-12 inhibition. Moreover, the dibenzofuran moiety conferred stable binding at the S1' pocket for higher MMP-12 inhibition. The steric and hydrophobic groups were found favourable near the furan ring substituted at the dibenzofuran moiety. Besides these ligand-based approaches, molecular docking and molecular dynamic (MD) simulation studies not only elucidated the importance of several aspects of these MMP-12 inhibitors while disclosing the significance of the finding of these QSAR studies and their influences toward MMP-12 inhibition. The MD simulation study also revealed stable and compact binding between such compounds at the MMP-12 active site. Therefore, the findings of these validated ligand-based and structure-based molecular modeling studies can aid the development of selective and potent lead molecules that can be used for the treatment of MMP-12-associated diseases.Communicated by Ramaswamy H. Sarma.

Background: Developmental toxicity tests are extremely expensive, require a large number of animals, and are time-consuming. It is necessary to develop a new approach to simplify the analysis of developmental endpoints. One of these endpoints is malformation, and one group of ongoing methods for simplifying is in silico models. In this study, we aim to develop a quantitative structure-activity relationship (QSAR) model and identify the best algorithm for predicting malformations, as well as the most important and effective physicochemical properties associated with malformation., Methods: The dataset was extracted from a reliable database called COMPTOX. Physicochemical properties (descriptors) were calculated using Mordred and RDKit chemoinformatics software. The data were cleaned, preprocessed, and then split into training and testing sets. Machine learning algorithms, such as gradient boosting model (GBM) and logistic regression (LR), as well as deep learning models, including multilayer perceptron (MLP) and neural networks (NNs) trained with train set data and different sets of descriptors. The models were then validated with test set and various statistical parameters, such as Matthew's correlation coefficient (MCC) and balanced accuracy (BAC) score, were used to compare the models., Results: A set of descriptors containing with 78% AUC was identified as the best set of descriptors. Gradient boosting was determined to be the best algorithm with 78% predictive power., Conclusions: The descriptors that were the most effective for developing models directly impact the mechanism of malformation, and GBM is the best model due to its MCC and BAC.

In this study, we used phenylpyrimidine derivatives with known biological activity against JAK3, a critical tyrosine kinase enzyme involved in signaling pathways, to find similar compounds as potential treatments for rheumatoid arthritis. These inhibitors inhibited JAK3 activity by forming a covalent bond with the Cys909 residue, which resulted in a strong inhibitory effect. Phenylpyrimidine is considered a promising therapeutic target. For pharmacophore modeling, 39 phenylpyrimidine derivatives with high pIC50 (Exp) values were chosen. The best pharmacophore model produced 28 molecules, and the five-point common pharmacophore hypothesis from P HASE (DHRRR_1) revealed the requirement for a hydrogen bond donor feature, a hydrophobic group feature, and three aromatic ring features for further design. The validation of the pharmacophore model phase was performed through 3D-QSAR using partial least squares (P LS). The 3D-QSAR study produced two successful models, an atom-based model (R2 = 0.95; Q2 = 0.67) and a field-based model (R2 = 0.93; Q2 = 0.76), which were used to predict the biological activity of new compounds. The pharmacophore model successfully distinguished between active and inactive medications, discovered potential JAK3 inhibitors, and demonstrated validity with a ROC of 0. 77. ADME-Tox was used to eliminate compounds that might have adverse effects. The best pharmacokinetics and affinity derivatives were selected for covalent docking. A molecular dynamics simulation of the selected molecules and the protein complex was performed to confirm the stability of the interaction with JAK3, whereas MM/GBSA simulations further confirmed their binding affinity. By using the principle of retrosynthesis, we were able to map out a pathway for synthesizing these potential drug candidates. This study has the potential to offer valuable and practical insights for optimizing novel derivatives of phenylpyrimidine.Communicated by Ramaswamy H. Sarma.

We aimed to further explore the relationship between heterocyclic molecules and their associated biological activities for acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes. A dataset of 36 heterocycles was used to predict the activity of AChE and BChE inhibitors (the pIC50 values ranged from 7.84 to 12.49). A quantitative structure-activity relationship (QSAR) study was generated with the help of four different models (BMA, MNLR, MLR, and ANN). Four of the models were statistically acceptable based on both internal and external validation. The descriptors used in the models were similar to the X-ray structures of the target-ligand complexes, which made it possible to predict the pIC50 for AChE and BChE enzymes. Five selected molecules (compounds 6 (C 21 H 21 F 3 N 4 O), compound 7 (C 22 H 23 F 3 N 4 O), and compound 8 (C 22 H 23 F 3 N 4 O 2 ) belong to the oxadiazole derivative group; compound 16 (C 17 H 13 ClN 2 O 3 ) is classified into the chemical structures of different N, O, and S-based heterocycle groups; and compound 25 (C 19 H 17 NO 2 ) pertains to the pyrimidine derivative group) possessed high pIC50 values for AChE and BChE enzymes (pIC50 values for AChE and BChE ranged from 9.01 to 10.32). The range of docking scores between the AChE and BChE receptors and their respective candidates was from -8.1 to -9.2 kcal/mol. The pharmacokinetics, biological activities, and physicochemical properties of five selected compounds supported their ability to protect against AD because they are not toxic, have a cholinergic effect, can cross the blood-brain barrier, and are well absorbed by the gastrointestinal tract.Communicated by Ramaswamy H. Sarma.

Background: Veterinary antibiotics are chemical compounds used to kill or inhibit the growth of pathogenic bacteria associated with animal diseases. These molecules can be defined by their retention times (t R ) in liquid chromatography-mass spectrometry (LC-MS). One strategy to predict the t R of new veterinary antibiotics is the development of predictive quantitative structure-property relationships (QSPRs), which were used in this study., Results: A database of 122 antibiotics was selected in which the t R was measured using a Hypersil GOLD column. An optimal three-feature model was developed by integrating the unsupervised variable reduction, replacement method variable subset selection, and multiple linear regression. The negligible differences among the coefficient of determination and the root-mean-square error for the training set (R 2  = 0.902 and RMSEC = 0.871) and test set (Q 2  = 0.854 and RMSEP = 1.064) indicate a stable and predictive model. In a further step, a more in-depth explanation of the mechanism of action of each descriptor in predicting the t R is provided, with the construction of the theoretical chemical space for accurate predictions of new antibiotics., Conclusion: The in silico model developed in this work identified three molecular descriptors associated with aqueous solubility, octanol-water partition coefficient, and the presence of negative and lipophilic atom pairs. The QSPR developed here could be implemented by agricultural and food chemists to identify and monitor existing and new antibiotics within the framework of LC-MS. The computational model was developed in accordance with five principles outlined by the Organization for Economic Co-operation and Development. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)

The pyrimidine heterocycle plays an important role in anticancer research. In particular, the pyrimidine derivative families of uracil show promise as structural scaffolds relevant to cervical cancer. This group of chemicals lacks data-driven machine learning quantitative structure-activity relationships (QSARs) that allow for generalization and predictive capabilities in the search for new active compounds. To achieve this, a dataset of pyrimidine and uracil compounds from ChEMBL were collected and curated. A workflow was developed for data-driven machine learning QSAR using an intuitive dataset design and forwards selection of molecular descriptors. The model was thoroughly externally validated against available data. Blind validation was also performed by synthesis and antiproliferative evaluation of new synthesized uracil-based and pyrimidine derivatives. The most active compound among new synthesized derivatives, 2,4,5-trisubstituted pyrimidine was predicted with the QSAR model with differences of 0.02 compared to experimentally tested activity.

Nucleophilic index ( N Nu ) as a significant parameter plays a crucial role in screening of amine catalysts. Indeed, the quantity and variety of amines are extensive. However, only limited amines exhibit an N values by using Machine Learning (ML) and high-throughput Density Functional Theory (DFT) calculations. Our approach commenced by training ML models and the exploration of Molecular Fingerprint methods as well as the development of quantitative structure-activity relationship (QSAR) models for the well-known amines based on Nu value exceeding 4.0 eV, rendering them potential nucleophiles in chemical reactions. To address this issue, we proposed a computational method to quickly identify amines with high N Nu values by using Machine Learning (ML) and high-throughput Density Functional Theory (DFT) calculations. Our approach commenced by training ML models and the exploration of Molecular Fingerprint methods as well as the development of quantitative structure-activity relationship (QSAR) models for the well-known amines based on N Nu values derived from DFT calculations. Utilizing explainable Shapley Additive Explanation plots, we were able to determine the five critical substructures that significantly impact the N Nu values of amine. The aforementioned conclusion can be applied to produce and cultivate 4920 novel hypothetical amines with high N Nu values. The QSAR models were employed to predict the N Nu values of 259 well-known and 4920 hypothetical amines, resulting in the identification of five novel hypothetical amines with exceptional N Nu values (>4.55 eV). The enhanced N Nu values of these novel amines were validated by DFT calculations. One novel hypothetical amine, H1, exhibits an unprecedentedly high N Nu value of 5.36 eV, surpassing the maximum value (5.35 eV) observed in well-established amines. Our research strategy efficiently accelerates the discovery of the high nucleophilicity of amines using ML predictions, as well as the DFT calculations.

The search for anticancer drugs that target ferroptosis is a promising avenue of research. SLC7A11, a key protein involved in ferroptosis, has been identified as a potential target for drug development. Through screening efforts, novel inhibitors of SLC7A11 have been designed with the aim of promoting ferroptosis and ultimately eliminating cancer cells. We initially screened 563 small molecules using pharmacophore and 2D-QSAR models. Molecular docking and ADMET toxicity predictions, with Erastin as a positive control, identified the small molecules 42711 and 27363 as lead compounds with strong inhibitory activity against SLC7A11. Further optimization resulted in the development of a new inhibitor structure (42711_11). Molecular docking and ADMET re-screening demonstrated successful fragment substitution for this small molecule. Final molecular dynamics simulations also confirmed its stable interaction with the protein. These findings represent a significant step towards the development of new therapeutic strategies for ferroptosis-related diseases.

In response to the pressing global challenge of antibiotic resistance, time efficient design and synthesis of novel antibiotics are of immense need. Polycyclic polyprenylated acylphloroglucinols (PPAP) were previously reported to effectively combat a range of gram-positive bacteria. Although the exact mode of action is still not clear, we conceptualized a late-stage divergent synthesis approach to expand our natural product-based PPAP library by 30 additional entities to perform SAR studies against methicillin-resistant Staphylococcus aureus (MRSA). Although at this point only data from cellular assays are available and understanding of molecular drug-target interactions are lacking, the experimental data were used to generate 3D-QSAR models via an artificial intelligence training and to identify a common pharmacophore model. The experimentally validated QSAR model enabled the estimation of anti-MRSA activities of a virtual compound library consisting of more than 100,000 in-silico generated B PPAPs, out of which the 20 most promising candidates were synthesized. These novel PPAPs revealed significantly improved cellular activities against MRSA with growth inhibition down to concentrations less than 1 μm., (© 2024 The Authors. Chemistry - A European Journal published by Wiley-VCH GmbH.)

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. An emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

In this study, a series of novel hydrazide-containing flavonol derivatives was designed, synthesized, and evaluated for antifungal activity. In the in vitro antifungal assay, most of the target compounds exhibited potent antifungal activity against seven tested phytopathogenic fungi. In particular, compound C32 showed the best antifungal activity against Rhizoctonia solani (EC 50 = 0.170 μg/mL), outperforming carbendazim (EC 50 = 0.360 μg/mL) and boscalid (EC 50 = 1.36 μg/mL). Compound C24 exhibited excellent antifungal activity against Valsa mali , Botrytis cinerea , and Alternaria alternata with EC 50 values of 0.590, 0.870, and 1.71 μg/mL, respectively. The in vivo experiments revealed that compounds C32 and C24 were potential novel agricultural antifungals. 3D quantitative structure-activity relationship (3D-QSAR) models were used to analyze the structure-activity relationships of these compounds. The analysis results indicated that introducing appropriate electronegative groups at position 4 of a benzene ring could effectively improve the anti- R. solani activity. In the antifungal mechanism study, scanning electron microscopy and transmission electron microscopy analyses revealed that C32 disrupted the normal growth of hyphae by affecting the structural integrity of the cell membrane and cellular respiration. Furthermore, compound C32 exhibited potent succinate dehydrogenase (SDH) inhibitory activity (IC 50 = 8.42 μM), surpassing that of the SDH fungicide boscalid (IC 50 = 15.6 μM). The molecular dynamics simulations and docking experiments suggested that compound C32 can occupy the active site and form strong interactions with the key residues of SDH. Our findings have great potential for aiding future research on plant disease control in agriculture.

Growing restrictions and bans on animal testing for chemical safety assessment under different regulations have led to an increasing use of alternative methods. Read-across is one of the major approaches used for this purpose, which relies on the identification of toxicological hazards of a data-poor or untested (target) chemical from data on other already-tested (source) similar chemicals. This requires the target substance to be first assigned to a group or category of 'similar' chemicals. The 'similarity' may be in terms of structural features alone, or in combination with certain rules that are based on mechanistic and/or toxicological aspects. In this regard, the OECD QSAR toolbox-a major free-access in silico platform-is widely used to derive toxicity predictions for a range of (eco) toxicological endpoints. The Toolbox allows the user to identify a set of similar chemicals (analogues) by computational 'profilers' that incorporate different structural alerts, or a combination of structural alerts and physicochemical and/or toxicokinetic rules relevant to a specific toxicological endpoint. The overall aim of this study was to assess the performance of the in silico profilers provided in the OECD QSAR Toolbox for reliability for identifying chemical analogues for category formation in a number of high-quality databases on mutagenicity, carcinogenicity, and skin sensitisation. The study also aimed to identify the reasons for any limitations in the performance of the profilers, and propose ways to improve their overall accuracy. The results showed that whilst some structural alerts are fit-for-purpose as such within the acceptable limits, others need refinement or a consideration for their possible exclusion from the profiler. Such refinements are imperative for a reliable use of the profilers in read-across and grouping/categorisation for classification, labelling and risk assessment of chemicals., (© 2024. The Author(s).)

Global health concerns persist due to the multifaceted nature of heart diseases, which include lifestyle choices, genetic predispositions, and emerging post-COVID complications like myocarditis and pericarditis. This broadens the spectrum of cardiovascular ailments to encompass conditions such as coronary artery disease, heart failure, arrhythmias, and valvular disorders. Timely interventions, including lifestyle modifications and regular medications such as antiplatelets, beta-blockers, angiotensin-converting enzyme inhibitors, antiarrhythmics, and vasodilators, are pivotal in managing these conditions. In drug development, topological indices play a critical role, offering cost-effective computational and predictive tools. This study explores modified reverse degree topological indices, highlighting their adjustable parameters that actively shape the degree sequences of molecular drugs. This feature makes the approach suitable for datasets with unique physicochemical properties, distinguishing it from traditional methods that rely on fixed degree approaches. In our investigation, we examine a dataset of 30 drug compounds, including sotagliflozin, dapagliflozin, dobutamine, etc., which are used in the treatment of cardiovascular diseases. Through the structural analysis, we utilize modified reverse degree indices to develop quantitative structure-property relationship (QSPR) models, aiming to unveil essential understandings of their characteristics for drug development. Furthermore, we compare our QSPR models against the degree-based models, clearly demonstrating the superior effectiveness inherent in our proposed method., (© 2024. The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature.)

Antioxidants agents play an essential role in the food industry for improving the oxidative stability of food products. In the last years, the search for new natural antioxidants has increased due to the potential high toxicity of chemical additives. Therefore, the synthesis and evaluation of the antioxidant activity in peptides is a field of current research. In this study, we performed a Quantitative Structure Activity Relationship analysis (QSAR) of cysteine-containing 19 dipeptides and 19 tripeptides. The main objective is to bring information on the relationship between the structure of peptides and their antioxidant activity. For this purpose, 1D and 2D molecular descriptors were calculated using the PaDEL software, which provides information about the structure, shape, size, charge, polarity, solubility and other aspects of the compounds. Different QSAR model for di- and tripeptides were developed. The statistic parameters for di-peptides model (R 2 train  = 0.947 and R 2 test  = 0.804) and for tripeptide models (R 2 train  = 0.923 and R 2 test  = 0.847) indicate that the generated models have high predictive capacity. Then, the influence of the cysteine position was analyzed predicting the antioxidant activity for new di- and tripeptides, and comparing them with glutathione. In dipeptides, excepting SC, TC and VC, the activity increases when cysteine is at the N-terminal position. For tripeptides, we observed a notable increase in activity when cysteine is placed in the N-terminal position., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Human neutrophil elastase (HNE) plays a key role in initiating inflammation in the cardiopulmonary and systemic contexts. Pathological auto-proteolysed two-chain (tc) HNE exhibits reduced binding affinity with inhibitors. Using AutoDock Vina v1.2.0, 66 flavonoid inhibitors, sivelestat and alvelestat were docked with single-chain (sc) HNE and tcHNE. Schrodinger PHASE v13.4.132 was used to generate a 3D-QSAR model. Molecular dynamics (MD) simulations were conducted with AMBER v18. The 3D-QSAR model for flavonoids with scHNE showed r 2  = 0.95 and q 2  = 0.91. High-activity compounds had hydrophobic A/A2 and C/C2 rings in the S1 subsite, with hydrogen bond donors at C5 and C7 positions of the A/A2 ring, and the C4' position of the B/B1 ring. All flavonoids except robustaflavone occupied the S1'-S2' subsites of tcHNE with decreased AutoDock binding affinities. During MD simulations, robustaflavone remained highly stable with both HNE forms. Principal Component Analysis suggested that robustaflavone binding induced structural stability in both HNE forms. Cluster analysis and free energy landscape plots showed that robustaflavone remained within the sc and tcHNE binding site throughout the 100 ns MD simulation. The robustaflavone scaffold likely inhibits both tcHNE and scHNE. It is potentially superior to sivelestat and alvelestat and can aid in developing therapeutics targeting both forms of HNE.

Dengue fever, prevalent in Southeast Asian countries, currently lacks effective pharmaceutical interventions for virus replication control. This study employs a strategy that combines machine learning (ML)-based quantitative-structure-activity relationship (QSAR), molecular docking, and molecular dynamics simulations to discover potential inhibitors of the NS3 protease of the dengue virus. We used nine molecular fingerprints from PaDEL to extract features from the NS3 protease dataset of dengue virus type 2 in the ChEMBL database. Feature selection was achieved through the low variance threshold, F-Score, and recursive feature elimination (RFE) methods. Our investigation employed three ML models - support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) - for classifier development. Our SVM model, combined with SVM-RFE, had the best accuracy (0.866) and ROC_AUC (0.964) in the testing set. We identified potent inhibitors on the basis of the optimal classifier probabilities and docking binding affinities. SHAP and LIME analyses highlighted the significant molecular fingerprints (e.g. ExtFP69, ExtFP362, ExtFP576) involved in NS3 protease inhibitory activity. Molecular dynamics simulations indicated that amphotericin B exhibited the highest binding energy of -212 kJ/mol and formed a hydrogen bond with the critical residue Ser196. This approach enhances NS3 protease inhibitor identification and expedites the discovery of dengue therapeutics.

In the search for natural and non-toxic products alternatives to synthetic pesticides, the fumigant and repellent activities of 35 essential oils are predicted in the human head louse ( Pediculus humanus capitis ) through the Quantitative Structure-Activity Relationships (QSAR) theory. The number of constituents of essential oils with weight percentage composition greater than 1% varies from 1 to 15, encompassing up to 213 structurally diverse compounds in the entire dataset. The 27,976 structural descriptors used to characterizing these complex mixtures are calculated as linear combinations of non-conformational descriptors for the components. This approach is considered simple enough to evaluate the effects that changes in the composition of each component could have on the studied bioactivities. The best linear regression models found, obtained through the Replacement Method variable subset selection method, are applied to predict 13 essential oils from a previous study with unknown property data. The results show that the simple methodology applied here could be useful for predicting properties of interest in complex mixtures such as essential oils.

Global pesticide usage reaching 2.7 million metric tons annually, brings a grave threat to non-target organisms, especially aquatic organisms, resulting in serious concerns. Predicting aquatic toxicity of pesticides towards Daphnia magna is significant. In this work, random forest (RF) algorithm, together with ten Dragon molecular descriptors, was successfully utilized to develop a quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for the toxicity pEC 50 of 745 pesticides towards Daphnia magna. The optimal QSTR model (RF Model I) based on the RF parameters of ntree = 50, mtry = 3 and nodesize = 5, yielded R 2  = 0.877, MAE = 0.570, rms = 0.739 (training set of 596 pEC 50 ), R 2  = 0.807, MAE = 0.732, rms = 0.902 (test set of 149 pEC 50 ), and R 2  = 0.863, MAE = 0.602, rms = 0.774 (total set of 745 pEC 50 ), which are accurate and satisfactory. The optimal RF model is comparable to other published QSTR models for Daphnia magna, although the optimal RF model possessed a small descriptor subset and dealt with a large dataset of pesticide toxicity pEC 50 . Thus, the investigation in this work provides a reliable, applicable QSTR model for predicting the toxicity pEC 50 of pesticides towards Daphnia magna., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Botanicals contain complex mixtures of chemicals most of which lack pharmacokinetic data in humans. Since physicochemical and pharmacokinetic properties dictate the in vivo exposure of botanical constituents, these parameters greatly impact the pharmacological and toxicological effects of botanicals in consumer products. This study sought to use computational (i.e., in silico) models, including quantitative structure-activity relationships (QSAR) and physiologically based pharmacokinetic (PBPK) modeling, to predict properties of botanical constituents. One hundred and three major constituents (e.g., withanolides, mitragynine, and yohimbine) in 13 botanicals (e.g., ashwagandha, kratom, and yohimbe) were investigated. The predicted properties included biopharmaceutical classification system (BCS) classes based on aqueous solubility and permeability, oral absorption, liver microsomal clearance, oral bioavailability, and others. Over half of these constituents fell into BCS classes I and II at dose levels no greater than 100 mg per day, indicating high permeability and absorption (%F a  > 75%) in the gastrointestinal tract. However, some constituents such as glycosides in ashwagandha and Asian ginseng showed low bioavailability after oral administration due to poor absorption (BCS classes III and IV, %F a  < 40%). These in silico results fill data gaps for botanical constituents and could guide future safety studies. For example, the predicted human plasma concentrations may help select concentrations for in vitro toxicity testing. Additionally, the in silico data could be used in tiered or batteries of assays to assess the safety of botanical products. For example, highly absorbed botanical constituents indicate potential high exposure in the body, which could lead to toxic effects., (© 2024 The Authors. Journal of Applied Toxicology published by John Wiley & Sons Ltd.)

MMP-2 overexpression is strongly related to several diseases including cancer. However, none of the MMP-2 inhibitors have been marketed as drug candidates due to various adverse effects. Here, a set of sulphonyl pyrrolidines was subjected to validation of molecular modelling followed by binding mode analysis to explore the crucial structural features required for the discovery of promising MMP-2 inhibitors. This study revealed the importance of hydroxamate as a potential zinc-binding group compared to the esters. Importantly, hydrophobic and sterical substituents were found favourable at the terminal aryl moiety attached to the sulphonyl group. The binding interaction study revealed that the S1' pocket of MMP-2 similar to ' a basketball passing through a hoop ' allows the aryl moiety for proper fitting and interaction at the active site to execute potential MMP-2 inhibition. Again, the sulphonyl pyrrolidine moiety can be a good fragment necessary for MMP-2 inhibition. Moreover, some novel MMP-2 inhibitors were also reported. They showed the significance of the 3 rd position substitution of the pyrrolidine ring to produce interaction inside S2' pocket. The current study can assist in the design and development of potential MMP-2 inhibitors as effective drug candidates for the management of several diseases including cancers in the future.

QSAR studies on the number of compounds tested as S. aureus inhibitors were performed using an interactive Online Chemical Database and Modeling Environment (OCHEM) web platform. The predictive ability of the developed consensus QSAR model was q 2 =0.79±0.02. The consensus prediction for the external evaluation set afforded high predictive power (q 2 =0.82±0.03). The models were applied to screen a virtual chemical library with anti-S. aureus activity. Six promising new bicyclic trifluoromethylated pyrroles were identified, synthesized and evaluated in vitro against S. aureus, E. coli, and A. baumannii for their antibacterial activity and against C. albicans, C. krusei and C. glabrata for their antifungal activity. The synthesized compounds were characterized by 1 H, 19 F, and 13 C NMR and elemental analysis. The antimicrobial activity assessment indicated that trifluoromethylated pyrroles 9 and 11 demonstrated the greatest antibacterial and antifungal effects against all the tested pathogens, especially against multidrug-resistant strains. The acute toxicity of the compounds to Daphnia magna ranged from 1.21 to 33.39 mg/L (moderately and slightly toxic). Based on the docking results, it can be suggested that the antibacterial and antifungal effects of the compounds can be explained by the inhibition of bacterial wall component synthesis., (© 2024 Wiley-VHCA AG, Zurich, Switzerland.)

In searching for a new and efficient therapeutic agent against Alzheimer's disease, a Quantitative structure-activity relationship (QSAR) was derived for 45 Flavonoid derivatives recently synthesized and evaluated as cholinesterase inhibitors. The multiple linear regression method (MLR) was adopted to develop an adequate mathematical model that describes the relationship between a variety of molecular descriptors of the studied compounds and their biological activities (cholinesterase inhibitors). Golbraikh and Tropsha criteria were applied to verify the validity of the built model. The built MLR model was statistically reliable, robust, and predictive (R 2 = 0.801, Q 2 cv = 0.876, R 2 test = 0.824). Dreiding energy and Molar Refractivity were the major factors that govern the Anti-cholinesterase activity. These results were further exploited to design a new series of Flavonoid derivatives with higher Anti-cholinesterase activities than the existing ones. Thereafter, molecular docking and molecular dynamic studies were performed to predict the binding types of the designed compounds and to investigate their stability at the active site of the Butyrylcholinestérase BuChE protein. The negative and low binding affinity calculated for all designed compounds shows that designed compound 1 has a favorable affinity for the 4TPK. Moreover, molecular dynamics simulation studies confirmed the stability of designed compound 1 in the active pocket of 4TPK over 100 ns. Finally, the ADMET analysis was incorporated to analyze the pharmacokinetics and toxicity parameters. The designed compounds were found to meet the ADMET descriptor criteria at an acceptable level having respectable intestinal permeability and water solubility and can reach the intended destinations.Communicated by Ramaswamy H. Sarma.

The latest variant of coronavirus is omicron. The World Health Organization (WHO) designated variation 'B.1.1.529' named omicron as a variant of concern (VOC) on 26 November 2021. By September 2020, it will have infected over 16 million patients and killed over 600,000 people over the world. This very infectious viral illness still poses a danger to world health; it has also become the greatest problem the world is facing and become the main area of research. The development of vaccines is insufficient to stop their spread and serious effects. Despite several reputable pharmaceutical firms claiming to have developed a cure for COVID-19. For that purpose, the field-based 3D-QSAR model has been used to analyze a series of anti-diabetic drugs to repurpose them against COVID-19. The LOO verified partial least square (PLS) model generates satisfactory q 2 (0.4) and r 2 (0.5) values. By using this model 10 compounds were screened out of 55 FDA approved anti-diabetic drugs (built-up library). Additionally, these substances were examined using molecular docking screening and ADMET. Finally, the drugs L8 , and L23 were discovered to be the lead drugs. Density functional theory at the B3LYP/6-311G* technique was used to examine structural geometries, electronic characteristics, and molecular electrostatic potential (MEP). This work will greatly assist in the detection and development of leads for early drug development to control COVID-19.Communicated by Ramaswamy H. Sarma.

As the United States and the European Union continue their steady march towards the acceptance of new approach methodologies (NAMs), we need to ensure that the available tools are fit for purpose. Critics will be well-positioned to caution against NAMs acceptance and adoption if the tools turn out to be inadequate. In this paper, we focus on Quantitative Structure Activity-Relationship (QSAR) models and highlight how the training database affects quality and performance of these models. Our analysis goes to the point of asking, "are the endpoints extracted from the experimental studies in the database trustworthy, or are they false negatives/positives themselves?" We also discuss the impacts of chemistry on QSAR models, including issues with 2-D structure analyses when dealing with isomers, metabolism, and toxicokinetics. We close our analysis with a discussion of challenges associated with translational toxicology, specifically the lack of adverse outcome pathways/adverse outcome pathway networks (AOPs/AOPNs) for many higher tier endpoints. We recognize that it takes a collaborate effort to build better and higher quality QSAR models especially for higher tier toxicological endpoints. Hence, it is critical to bring toxicologists, statisticians, and machine learning specialists together to discuss and solve these challenges to get relevant predictions., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: FMK, AH, and MF work for companies that develop crop protection products, including pesticides. LDB provides toxicology, computational toxicology, and biostatistical consulting services to companies that develop crop protection products., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

In this study, the power of machine learning was harnessed to probe the link between molecular structures of peptide-based hydrogels and their viscoplastic properties. The selection of compounds was attempted in accordance with the prescribed full list of peptide-based materials exhibiting hydrogel functionality in the literature. In this pursuit, a complete set of molecular descriptors and fingerprints was considered - accounting for an entry of size 17,968 for each peptide-based structure analyzed. The elastic and viscous moduli response of materials were mapped over a wide frequency spectrum in the range [0.1-100] (rad/s). In general, the results indicate that the frequency-dependent mechanical response of peptide-based hydrogels is statistically correlated with its (inter)molecular attributes, such as charge, first ionization potential (or equivalently electronegativity), surface area, number of chemical substrates, bond type, and intermolecular interactions. The performance of several (supervised) soft computing techniques was measured, for our quantitative structure property relationships model. In addition, the hypothesis of mapping our databank to a new system of principal components was tested, by using an unsupervised methodology, which resulted in enhancement of the prediction accuracy. In terms of significance, the present article provides the first report of frequency-dependent elastic and viscous moduli, for a set of 70 peptide-based formulations with hydrogel functionality.Communicated by Ramaswamy H. Sarma.

In this work, a quantitative structure-antioxidant activity relationship of flavonoids was performed using a machine learning (ML) method. To achieve lipid-soluble, highly antioxidant flavonoids, 398 molecular structures with various substitute groups were designed based on the flavonoid skeleton. The hydrogen dissociation energies (Δ G 1 , Δ G 2 , and Δ G 3 ) related to multiple hydrogen atom transfer processes and the solubility parameter (δ) of flavonoids were calculated using molecular simulation. The group decomposition results and the calculated antioxidant parameters constituted the ML data set. The artificial neural network and random forest models were constructed to predict and analyze the contribution of the substitute groups and positions to the antioxidant activity. The results showed the hydroxyl group at positions B4', B5', and B6' and the branched alkyl group at position C3 in the flavonoid skeleton were the optimal choice for improving antioxidant activity and compatibility with apolar organic materials. Compared to the pyrogallol group-grafted flavonoid, the designed potent flavonoid decreased Δ G 1 and δ by 2.2 and 15.1%, respectively, while Δ G 2 and Δ G 3 kept the favorable lower values. These findings suggest that an efficient flavonoid prefers multiple ortho-phenolic hydroxyl groups and suitable sites with hydrophobic groups. The combination of molecular simulation and the ML method may offer a new research approach for the molecular design of novel antioxidants.

In the modern era, chemicals and their products have been used everywhere like agriculture, healthcare, food, cosmetics, pharmaceuticals, household products, clothing industry, etc. These chemicals find their way to reach the aquatic ecosystem (directly/indirectly) and cause severe chronic and prolonged toxic effects to aquatic species which is also then translated to human beings. Prolonged and chronic toxicity data of many chemicals that are used daily is not available due to high experimentation testing costs, time investment, and the requirement of a large number of animal sacrifices. Thus, in silico approaches (e.g., QSAR (quantitative structure-activity relationship)) are the best alternative for chronic and prolonged toxicity predictions. The present work offers multi-endpoint (five endpoints: chronic&#95;LOEC, prolonged&#95;14D&#95;LC 50 , prolonged&#95;14D&#95;NOEC, prolonged&#95;21D&#95;LC 50 , prolonged&#95;21D&#95;NOEC) QSAR models for addressing the prolonged and chronic aquatic toxicity of chemicals toward fish (O. latipes). The statistical results (R 2 =0.738-0.869, Q LOO 2 =0.618-0.731) of the developed models show that they were robust, reliable, reproducible, accurate, and predictive. Some of the features that are responsible for prolonged and chronic toxicity of chemicals towards O. latipes are as follows: the presence of substituted benzene, hydrophobicity, unsaturation, electronegativity, the presence of long-chain fragments, the presence of a greater number of atoms at conjugation, and the presence of halogen atoms. On the other hand, hydrophilicity and graph density descriptors retard the aquatic chronic and prolonged toxicity of chemicals toward O. latipes. The PPDB (pesticide properties database) and experimental and investigational classes of drugs from the DrugBank database were also screened using the developed model. Thus, these multi-endpoint models will be helpful for data-gap filling and provide a broad range of applicability. Therefore, this research will aid in the in silico QSAR (quantitative structure-activity relationship) prediction (non-animal testing) of the prolonged and chronic toxicity of untested and new toxic chemicals/drugs/pesticides, design and development of eco-friendly, novel, and safer chemicals, and help to protect the aquatic ecosystem from exposure to toxic and hazardous chemicals.(F1) 2 =0.618-0.731) of the developed models show that they were robust, reliable, reproducible, accurate, and predictive. Some of the features that are responsible for prolonged and chronic toxicity of chemicals towards O. latipes are as follows: the presence of substituted benzene, hydrophobicity, unsaturation, electronegativity, the presence of long-chain fragments, the presence of a greater number of atoms at conjugation, and the presence of halogen atoms. On the other hand, hydrophilicity and graph density descriptors retard the aquatic chronic and prolonged toxicity of chemicals toward O. latipes. The PPDB (pesticide properties database) and experimental and investigational classes of drugs from the DrugBank database were also screened using the developed model. Thus, these multi-endpoint models will be helpful for data-gap filling and provide a broad range of applicability. Therefore, this research will aid in the in silico QSAR (quantitative structure-activity relationship) prediction (non-animal testing) of the prolonged and chronic toxicity of untested and new toxic chemicals/drugs/pesticides, design and development of eco-friendly, novel, and safer chemicals, and help to protect the aquatic ecosystem from exposure to toxic and hazardous chemicals., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Food-derived angiotensin-converting enzyme-inhibitory (ACE-I) peptides have attracted extensive attention. Herein, the ACE-I peptides from Scomber japonicus muscle hydrolysates were screened, and their mechanisms of action and inhibition stability were explored. The quantitative structure-activity relationship (QSAR) model based on 5z-scale metrics was developed to rapidly screen for ACE-I peptides. Two novel potential ACE-I peptides (LTPFT, PLITT) were predicted through this model coupled with in silico screening, of which PLITT had the highest activity (IC 50 : 48.73 ± 7.59 μM). PLITT inhibited ACE activity with a mixture of non-competitive and competitive mechanisms, and this inhibition mainly contributed to the hydrogen bonding based on molecular docking study. PLITT is stable under high temperatures, pH, glucose, and NaCl. The zinc ions (Zn 2+ ) and copper ions (Cu 2+ ) enhanced ACE-I activity. The study suggests that the QSAR model is effective in rapidly screening for ACE-I inhibitors, and PLITT can be supplemented in foods to lower blood pressure., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Machine-learning (ML) and deep-learning (DL) approaches to predict the molecular properties of small molecules are increasingly deployed within the design-make-test-analyze (DMTA) drug design cycle to predict molecular properties of interest. Despite this uptake, there are only a few automated packages to aid their development and deployment that also support uncertainty estimation, model explainability, and other key aspects of model usage. This represents a key unmet need within the field, and the large number of molecular representations and algorithms (and associated parameters) means it is nontrivial to robustly optimize, evaluate, reproduce, and deploy models. Here, we present QSARtuna, a molecule property prediction modeling pipeline, written in Python and utilizing the Optuna, Scikit-learn, RDKit, and ChemProp packages, which enables the efficient and automated comparison between molecular representations and machine learning models. The platform was developed by considering the increasingly important aspect of model uncertainty quantification and explainability by design. We provide details for our framework and provide illustrative examples to demonstrate the capability of the software when applied to simple molecular property, reaction/reactivity prediction, and DNA encoded library enrichment classification. We hope that the release of QSARtuna will further spur innovation in automatic ML modeling and provide a platform for education of best practices in molecular property modeling. The code for the QSARtuna framework is made freely available via GitHub.

Transient receptor potential vanilloid 1 (TRPV1) was reported to be a putative target for recovery from chronic pain, producing analgesic effects after its inhibition. A series of drug candidates were previously developed, without the ability to ameliorate the therapeutic outcome. Starting from previously designed compounds, derived from the hybridization of antagonist SB-705498 and partial agonist MDR-652, we performed a virtual screening on a pharmacophore model built by exploiting the Cryo-EM 3D structure of a nanomolar antagonist in complex with the human TRPV1 channel. The pharmacophore model was described by three pharmacophoric features, taking advantage of both the bioactive pose of the antagonist and the receptor exclusion spheres. The results of the screening were implemented inside a 3D-QSAR model, correlating with the negative decadic logarithm of the inhibition rate of the ligands. After the validation of the obtained 3D-QSAR model, we designed a new series of compounds by introducing key modifications on the original scaffold. Again, we determined the compounds' binding poses after alignment to the pharmacophoric model, and we predicted their inhibition rates with the validated 3D-QSAR model. The obtained values resulted in being even more promising than parent compounds, demonstrating that ongoing research still leaves much room for improvement.

Breast cancer remains a leading cause of cancer-related deaths among women globally, necessitating the development of more effective therapeutic agents with minimal side effects. This study explores novel 1,2,4-triazine-3(2H)-one derivatives as potential inhibitors of Tubulin, a pivotal protein in cancer cell division, highlighting a targeted approach in cancer therapy. Using an integrated computational approach, we combined quantitative structure-activity relationship (QSAR) modeling, ADMET profiling, molecular docking, and molecular dynamics simulations to evaluate and predict the efficacy and stability of these compounds. Our QSAR models, developed through rigorous statistical analysis, revealed that descriptors such as absolute electronegativity and water solubility significantly influence inhibitory activity, achieving a predictive accuracy (R 2 ) of 0.849. Molecular docking studies identified compounds with high binding affinities, particularly Pred28, which exhibited the best docking score of - 9.6 kcal/mol. Molecular dynamics simulations conducted over 100 ns provided further insights into the stability of these interactions. Pred28 demonstrated notable stability, with the lowest root mean square deviation (RMSD) of 0.29 nm and root mean square fluctuation (RMSF) values indicative of a tightly bound conformation to Tubulin. The novelty of this work lies in its methodological rigor and the integration of multiple advanced computational techniques to pinpoint compounds with promising therapeutic potential. Our findings advance the current understanding of Tubulin inhibitors and open avenues for the synthesis and experimental validation of these compounds, aiming to offer new solutions for breast cancer treatment., (© 2024. The Author(s).)

The study aims at assessing the potential of graphene-based adsorbents to reduce environmental impacts of Iodinated Contrast Media Agents (ICMs). We analyze an extensive collection of ICMs. A modeling approach resting on molecular docking and Density Functional Theory simulations is employed to examine the adsorption process at the molecular level. The study also relies on a Quantitative Structure-Activity Relationship (QSAR) modeling framework to correlate molecular properties with the adsorption energy (E ad ) of ICMs, thus enabling identification of the key mechanisms underpinning adsorption and of the key factors contributing to it. A collection of distinct QSAR-based models is developed upon relying on Multiple Linear Regression and a standard genetic algorithm method. Having at our disposal multiple models enables us to take into account the uncertainty associated with model formulation. Maximum Likelihood and formal model identification/discrimination criteria (such as Bayesian and/or information theoretic criteria) are then employed to complement the traditional QSAR modeling phase. This has the advantage of (a) providing a rigorous ranking of the alternative models included in the selected set and (b) quantifying the relative degree of likelihood of each of these models through a weight or posterior probability. The resulting workflow of analysis enables one to seamlessly embed DFT and QSAR studies within a theoretical framework of analysis that explicitly takes into account model and parameter uncertainty. Our results suggest that graphene-based surfaces constitute a promising adsorbent for ICMs removal, π-π stacking being the primary mechanism behind ICM adsorption. Furthermore, our findings offer valuable insights into the potential of graphene-based adsorbent materials for effectively removing ICMs from water systems. They contribute to ascertain the significance of various factors (such as, e.g., the distribution of atomic van der Waals volumes, overall molecular complexity, the presence and arrangement of Iodine atoms, and the presence of polar functional groups) on the adsorption process., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Cardiovascular diseases, including heart failure, stroke, and hypertension, affect 608 million people worldwide and cause 32% of deaths. Combination therapy is required in 60% of patients, involving concurrent Renin-Angiotensin-Aldosterone-System (RAAS) and Neprilysin inhibition. This study introduces a novel multi-target in-silico modeling technique (mt-QSAR) to evaluate the inhibitory potential against Neprilysin and Angiotensin-converting enzymes. Using both linear (GA-LDA) and non-linear (RF) algorithms, mt-QSAR classification models were developed using 983 chemicals to predict inhibitory effects on Neprilysin and Angiotensin-converting enzymes. The Box-Jenkins method, feature selection method, and machine learning algorithms were employed to obtain the most predictive model with ~ 90% overall accuracy. Additionally, the study employed virtual screening of designed scaffolds (Chalcone and its analogues, 1,3-Thiazole, 1,3,4-Thiadiazole) applying developed mt-QSAR models and molecular docking. The identified virtual hits underwent successive filtration steps, incorporating assessments of drug-likeness, ADMET profiles, and synthetic accessibility tools. Finally, Molecular dynamic simulations were then used to identify and rank the most favourable compounds. The data acquired from this study may provide crucial direction for the identification of new multi-targeted cardiovascular inhibitors., (© 2024. The Author(s).)