Search

Background/Objectives: HMG-CoA reductase is an enzyme that regulates the initial stage of cholesterol synthesis, and its inhibitors are widely used in the treatment of cardiovascular diseases. Methods: We have created a set of quantitative structure-activity relationship (QSAR) models for human HMG-CoA reductase inhibitors using nested cross-validation as the primary validation method. To develop the QSAR models, we employed various machine learning regression algorithms, feature selection methods, and fingerprints or descriptor datasets. Results: We built and evaluated a total of 300 models, selecting 21 that demonstrated good performance (coefficient of determination, R2 ≥ 0.70 or concordance correlation coefficient, CCC ≥ 0.85). Six of these top-performing models met both performance criteria and were used to construct five ensemble models. We identified the descriptors most important in explaining HMG-CoA inhibition for each of the six best-performing models. We used the top models to search through over 220,000 chemical compounds from a large database (ZINC 15) for potential new inhibitors. Only a small fraction (237 out of approximately 220,000 compounds) had reliable predictions with mean pIC50 values ≥ 8 (IC50 values ≤ 10 nM). Our svm-based ensemble model predicted IC50 values < 10 nM for roughly 0.08% of the screened compounds. We have also illustrated the potential applications of these QSAR models in understanding the cholesterol-lowering activities of herbal extracts, such as those reported for an extract prepared from the Iris × germanica rhizome. Conclusions: Our QSAR models can accurately predict human HMG-CoA reductase inhibitors, having the potential to accelerate the discovery of novel cholesterol-lowering agents and may also be applied to understand the mechanisms underlying the reported cholesterol-lowering activities of herbal extracts.

Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. Much knowledge is missing for current use chemicals and thus computational methodologies complemented with fast screening techniques will be critical. This paper reviews current computational tools, emphasizing those that are accessible and suitable for the screening of new and emerging risk chemicals (NERCs). The initial step in a computational EWS is an automatic and systematic search for NERCs in literature and database sources including grey literature, patents, experimental data, and various inventories. This step aims at reaching curated molecular structure data along with existing exposure and hazard data. Next, a parallel assessment of exposure and effects will be performed, which will input information into the weighting of an overall hazard score and, finally, the identification of a potential NERC. Several challenges are identified and discussed, such as the integration and scoring of several types of hazard data, ranging from chemical fate and distribution to subtle impacts in specific species and tissues. To conclude, there are many computational systems, and these can be used as a basis for an integrated computational EWS workflow that identifies NERCs automatically.

Antimicrobial resistance is a global threat, leading to an alarming increase in the prevalence of bacterial infections that can no longer be treated with available antibiotics. The World Health Organization estimates that by 2050 up to 10 million deaths peryear could be associated with antimicrobial resistance, which would equal the annual number of cancer deaths worldwide. To overcome this emerging crisis, novel anti-bacterial compounds are urgently needed. There are two possible approaches in the fight against bacterial infections: a) targeting structures within bacterial cells, similar to existing antibiotics; and/or b) targeting virulence factors rather than bacterial growth. Here, for the first time, we provide a comprehensive overview of the key steps in the evaluation of potential new anti-bacterial and/or anti-virulence compounds. The methods described in this review include: a) in silico methods for the evaluation of novel compounds; b) anti-bacterial assays (MIC, MBC, Time-kill); b) anti-virulence assays (anti-biofilm, anti-quorum sensing, anti-adhesion); and c) evaluation of safety aspects (cytotoxicity assay and Ames test). Overall, we provide a detailed description of the methods that are an essential tool for chemists, computational chemists, microbiologists, and toxicologists in the evaluation of potential novel antimicrobial compounds. These methods are cost-effective and have high predictive value. They are widely used in preclinical studies to identify new molecular candidates, for further investigation in animal and human trials. [ABSTRACT FROM AUTHOR]

Excessive use of chemicals is the outcome of the industrialization of agricultural sectors which leads to disturbance of ecological balance. Various agrochemicals are widely used in agricultural fields, urban green areas, and to protect from various pest-associated diseases. Due to their long-term health and environmental hazards, chronic toxicity assessment is crucial. Since in vivo and in vitro toxicity assessments are costly, lengthy, and require a large number of animal experiments, in silico toxicity approaches are better alternatives to save time, cost, and animal experimentation. We have developed the first regression-based 2D-QSAR models using different sub-chronic and chronic toxicity data of pesticides against dogs employing 2D descriptors. From the statistical results ($n{\rm{train}} = 53 - 62,{\rm{ }}{r^2}$ n train = 53 − 62 , r 2 = 0.614 to 0.754, $Q_{{\rm{LOO}}}^2$ Q L O O 2 = 0.501 to 0.703 and ${\rm{ }}Q_{\left({{\rm{F}}1} \right)}^2$ Q F 1 2 = 0.531 to 0.718, $Q_{\left({{\rm{F}}2} \right)}^2 = 0.523 - 0.713$ Q F 2 2 = 0.523 − 0.713), it was concluded that the models are robust, reliable, interpretable, and predictive. Similarity-based read-across algorithm was also used to improve the predictivity ($Q_{\left({{\rm{F}}1} \right)}^2 = 0.595 - 0.813, Q_{\left({{\rm{F}}2} \right)}^2 = 0.573 - 0.809$ Q F 1 2 = 0.595 − 0.813 , Q F 2 2 = 0.573 − 0.809) of the models. 5132 chemicals obtained from the CPDat and 1694 pesticides obtained from the PPDB database were also screened using the developed models, and their predictivity and reliability were checked. Thus, these models will be helpful for eco-toxicological data-gap filling, toxicity prediction of untested pesticides, and development of novel, safer & eco-friendly pesticides. [ABSTRACT FROM AUTHOR]

Transfer learning is a machine learning technique that works well with chemical endpoints, with several papers confirming its efficiency. Although effective, because the choice of source/assistant tasks is non-trivial, the application of this technique is severely limited by the domain knowledge of the modeller. Considering this limitation, we developed a purely data-driven approach for source task selection that abstracts the need for domain knowledge. To achieve this, we created a supervised learning setting in which transfer outcome (positive/negative) is the variable to be predicted, and a set of six transferability metrics, calculated based on information from target and source datasets, are the features for prediction. We used the ChEMBL database to generate 100,000 transfers using random pairing, and with these transfers, we trained and evaluated our transferability prediction model (TP-Model). Our TP-Model achieved a 135-fold increase in precision while achieving a sensitivity of 92%, demonstrating a clear superiority against random search. In addition, we observed that transfer learning could provide considerable performance increases when applicable, with an average Matthews Correlation Coefficient (MCC) increase of 0.19 when using a single source and an average MCC increase of 0.44 when using multiple sources. [ABSTRACT FROM AUTHOR]

Purpose: In order to ensure that drug administration is safe during pregnancy, it is crucial to have the possibility to predict the placental permeability of drugs in humans. The experimental method which is most widely used for the said purpose is in vitro human placental perfusion, though the approach is highly expensive and time consuming. Quantitative structure–activity relationship (QSAR) modeling represents a powerful tool for the assessment of the drug placental transfer, and can be successfully employed to be an alternative in in vitro experiments. Methods: The conformation-independent QSAR models covered in the present study were developed through the use of the SMILES notation descriptors and local molecular graph invariants. What is more, the Monte Carlo optimization method, was used in the test sets and the training sets as the model developer with three independent molecular splits. Results: A range of different statistical parameters was used to validate the developed QSAR model, including the standard error of estimation, mean absolute error, root-mean-square error (RMSE), correlation coefficient, cross-validated correlation coefficient, Fisher ratio, MAE-based metrics and the correlation ideality index. Once the mentioned statistical methods were employed, an excellent predictive potential and robustness of the developed QSAR model was demonstrated. In addition, the molecular fragments, which are derived from the SMILES notation descriptors accounting for the decrease or increase in the investigated activity, were revealed. Conclusion: The presented QSAR modeling can be an invaluable tool for the high-throughput screening of the placental permeability of drugs. [ABSTRACT FROM AUTHOR]

Food-derived angiotensin-I-converting enzyme (ACE)-inhibitory peptides have gained attention for their potent and safe treatment of hypertensive disorders. However, there are some limitations of conventional methods for preparing ACE-inhibitory peptides. In this study, in silico hydrolysis, the quantitative structure–activity relationship (QSAR) model, LC-MS/MS, inhibition kinetics, and molecular docking were used to investigate the stability, hydrolyzability, in vitro activity, and inhibition mechanism of bioactive peptides during the actual hydrolysis process. Six novel ACE-inhibitory peptides were screened from the Larimichthys crocea protein (LCP) and had low IC50 values (from 0.63 ± 0.09 µM to 10.26 ± 0.21 µM), which were close to the results of the QSAR model. After in vitro gastrointestinal simulated digestion activity of IPYADFK, FYEPFM and NWPWMK were found to remain almost unchanged, whereas LYDHLGK, INEMLDTK, and IHFGTTGK were affected by gastrointestinal digestion. Meanwhile, the inhibition kinetics and molecular docking results were consistent in that ACE-inhibitory peptides of different inhibition forms could effectively bind to the active or non-central active centers of ACE through hydrogen bonding. Our proposed method has better reproducibility, accuracy, and higher directivity than previous methods. This study can provide new approaches for the deep processing, identification, and preparation of Larimichthys crocea. [ABSTRACT FROM AUTHOR]

COVID-19 has claimed around 7 million lives (from December 2019–November 2023) worldwide and continues to impact global health. SARS-CoV-2, the virus causing COVID-19 disease, is characterized by a high rate of mutations, which contributes to its rapid spread, virulence, and vaccine escape. While several vaccines have been produced to minimize the severity of the coronavirus, and diverse treatment regimens have been approved by the US FDA under Emergency Use Authorization (EUA), SARS-CoV-2 viral mutations continue to derail the efforts of scientists as the emerging variants evade the recommended therapies. Nonetheless, diverse computational models exist that offer an opportunity for the swift development of new drugs or the repurposing of old drugs. In this review, we focus on the use of various virtual screening techniques like homology modeling, molecular docking, molecular dynamics simulations, QSAR, pharmacophore modeling, etc., in repurposing SARS-CoV-2 therapeutics against major variants of SARS-CoV-2 (Alpha, Beta, Gamma, Delta, and Omicron). The results have been promising from the computer-aided drug design (CADD) studies in suggesting potential compounds for the treatment of COVID-19 variants. Hence, in silico therapeutic studies represent a transformative approach that holds great promise in advancing our fight against the ever-evolving landscape of SARS-CoV-2 and its variants. [ABSTRACT FROM AUTHOR]

BACKGROUND: γ‐Aminobutyric acid (GABA) receptors (GABARs) are validated targets of insecticides. Bicyclophosphorothionates are a group of insecticidal compounds that act as noncompetitive antagonists of GABARs. We previously reported that the analogs exhibit various degrees of selectivity for housefly versus rat GABARs, depending on substitutions at the 3‐ and 4‐positions. We here sought to elucidate the unsolved mechanisms of the receptor selectivity using quantitative structure–activity relationship (QSAR), molecular docking, and molecular dynamics approaches. RESULTS: Three‐dimensional (3D)‐QSAR studies using Topomer comparative molecular field analysis quantitatively demonstrated how the introduction of a small alkyl group at the 3‐position of bicyclophosphorothionates contributes to the housefly versus rat GABAR selectivity. To investigate the molecular mechanisms of the selective action, bicyclophosphorothionates were docked into housefly Resistance to dieldrin (RDL) GABAR and rat α1β2γ2 GABAR homology models built using the published 3D‐structures of human GABARs as templates. The results of molecular docking and molecular dynamics simulations revealed that the 2'Ala and 6'Thr residues of the RDL subunit within the channel are the key amino acids for binding to the housefly GABARs, whereas the 2'Ser residue of γ2 subunit plays a crucial role in binding to rat GABARs. CONCLUSION: We revealed the molecular mechanisms underlying the selective antagonistic action of bicyclophosphorothionates on housefly versus rat GABARs. The information presented should help design and develop novel, safe GABAR‐targeting insecticides. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

In recent years, blocking the interaction of programmed cell death receptor‐1 (PD‐1) and programmed cell death ligand‐1 (PD‐L1) has been recognized as one of the promising cancer therapies. Due to the many limitations of monoclonal antibodies, the development of small molecule inhibitors targeting PD‐1/PD‐L1 is imminent. This study aims to systematically investigate the interaction mechanisms of novel PD‐1/PD‐L1 inhibitors containing indoline backbone structures using 3D/2D‐QSAR modeling, molecular docking, molecular dynamics (MD) simulation, and ADMET prediction. The performance and predictive ability of the constructed QSAR models were evaluated by internal and external validation techniques. Topomer CoMFA and HQSAR models showed good reliability and predictive capability. By searching for R‐groups in the Topomer search module and combining the existing molecules with high activity contribution values, 24 new compounds with theoretically high activity were obtained, which showed desirable docking scores, appropriate ADMET properties, and good stability. This work not only provides a reliable QSAR model as a valuable screening tool for the development of future drugs targeting PD‐1/PD‐L1, but also makes the newly designed inhibitors expected to be potential PD‐1/PD‐L1 inhibitors that can be used for further synthesis and characterization to obtain novel anti‐cancer drugs targeting PD‐1/PD‐L1. [ABSTRACT FROM AUTHOR]

A set of 5-(substituted benzylidene) thiazolidine-2,4-dione derivatives was explored to study the main structural requirement for the design of protein tyrosine phosphatase 1B (PTP1B) inhibitors. Utilizing multiple linear regression (MLR) analysis, we constructed a robust quantitative structure–activity relationship (QSAR) model to predict inhibitory activity, resulting in a noteworthy correlation coefficient (R2) of 0.942. Rigorous cross-validation using the leave-one-out (LOO) technique and statistical parameter calculations affirmed the model's reliability, with the QSAR analysis revealing 10 distinct structural patterns influencing PTP1B inhibitory activity. Compound 7e(ref) emerged as the optimal scaffold for drug design. Seven new PTP1B inhibitors were designed based on the QSAR model, followed by molecular docking studies to predict interactions and identify structural features. Pharmacokinetics properties were assessed through drug-likeness and ADMET studies. After that density functional theory (DFT) was conducted to assess the stability and reactivity of potential diabetes mellitus drug candidates. The subsequent dynamic simulation phase provided additional insights into stability and interactions dynamics of the top-ranked compound 11c. This comprehensive approach enhances our understanding of potential drug candidates for treating diabetes mellitus. [ABSTRACT FROM AUTHOR]

The quantitative structure–activity relationship (QSAR) is one of the most popular methods for the virtual screening of new drug leads and optimization. Herein, we collected a dataset of 955 MIC values of pleuromutilin derivatives to construct a 2D-QSAR model with an accuracy of 80% and a 3D-QSAR model with a non-cross-validated correlation coefficient (r2) of 0.9836 and a cross-validated correlation coefficient (q2) of 0.7986. Based on the obtained QSAR models, we designed and synthesized pleuromutilin compounds 1 and 2 with thiol-functionalized side chains. Compound 1 displayed the highest antimicrobial activity against both Staphylococcus aureus ATCC 29213 (S. aureus) and Methicillin-resistant Staphylococcus aureus (MRSA), with minimum inhibitory concentrations (MICs) < 0.0625 μg/mL. These experimental results confirmed that the 2D and 3D-QSAR models displayed a high accuracy of the prediction function for the discovery of lead compounds from pleuromutilin derivatives. [ABSTRACT FROM AUTHOR]

The European Union requires environmental monitoring of the antidepressant drug venlafaxine. Advanced oxidation processes provide a remedy against the spread of micropollutants. In this study, the photoinduced and electrochemical decompositions of venlafaxine were investigated in terms of mechanism and efficacy using high-performance liquid chromatography coupled to high-resolution multifragmentation mass spectrometry. Kinetic analysis, structure elucidation, matrix variation, and radical scavenging indicated the dominance of a hydroxyl-mediated indirect mechanism during photodegradation and hydroxyl and direct electrochemical oxidation for electrochemical degradation. Oxidants, sulfate, and chloride ions acted as accelerants, which reduced venlafaxine half-lives from 62 to 25 min. Humic acid decelerated degradation during ultra-violet irradiation up to 50%, but accelerated during electrochemical oxidation up to 56%. In silico quantitative structure activity relationship analysis predicted decreased environmental hazard after advanced oxidation process treatment. In general, photoirradiation proved more efficient due to faster decomposition and slightly less toxic transformation products. Yet, matrix effects would have to be carefully evaluated when potential applications as a fourth purification stage were to be considered. [ABSTRACT FROM AUTHOR]

In the modern fast-paced lifestyle, time-efficient and nutritionally rich foods like corn and oat have gained popularity for their amino acids and antioxidant contents. The increasing demand for these cereals necessitates higher production which leads to dependency on agrochemicals, which can pose health risks through residual present in the plant products. To first report the phytotoxicity for corn and oat, our study employs QSAR, quantitative Read-Across and quantitative RASAR (q-RASAR). All developed QSAR and q-RASAR models were equally robust (R2 = 0.680–0.762, Q2Loo = 0.593–0.693, Q2F1 = 0.680–0.860) and find their superiority in either oat or corn model, respectively, based on MAE criteria. AD and PRI had been performed which confirm the reliability and predictability of the models. The mechanistic interpretation reveals that the symmetrical arrangement of electronegative atoms and polar groups directly influences the toxicity of compounds. The final phytotoxicity and prioritization are performed by the consensus approach which results into selection of 15 most toxic compounds for both species. [ABSTRACT FROM AUTHOR]

To explore novel terpenoid repellents, 22 candidate terpenoid derivatives were synthesized and tested for their electroantennogram (EAG) responses and repellent activities against Aedes albopictus. The results from the EAG experiments revealed that 5-(2-hydroxypropan-2-yl)-2-methylcyclohex-2-en-1-yl formate (compound 1) induced distinct EAG responses in female Aedes albopictus. At concentrations of 0.1, 1, 10, 100, and 1000 mg/L, the EAG response values for compound 1 were 179.59, 183.99, 190.38, 193.80, and 196.66 mV, demonstrating comparable or superior effectiveness to DEET. Repellent activity analysis indicated significant repellent activity for compound 1, closest to the positive control DEET. The in silico assessment of the ADMET profile of compound 1 indicates that it successfully passed the ADMET evaluation. Molecular docking studies exhibited favourable binding of compound 1 to the active site of the odorant binding protein (OBP) of Aedes albopictus, involving hydrophobic forces and hydrogen bond interactions with residues in the OBP pocket. The QSAR model highlighted the influential role of hydrogen-bonding receptors, positively charged surface area of weighted atoms, polarity parameters of molecules, and maximum nuclear-nuclear repulsion force of carbon-carbon bonds on the relative EAG response values of the tested compounds. This study holds substantial significance for the advancement of new terpenoid repellents. [ABSTRACT FROM AUTHOR]

In this investigation, we undertook a comprehensive quantitative structure-activity relationship (QSAR) analysis using both modern artificial neural network (ANN) methods and classical multiple linear regression methods (MLR). Our primary focus was on revealing the intricate connection between antidiabetic activity and the molecular structure of thirty-nine imidazolidine-2,4-dione derivatives. The B3LYP hybrid functional and 6-31G (d) basis set computed electronic properties at the quantum level. Rigorous benchmarking against experimental data validated the reliability of our quantum theory approach. Our statistical model effectively predicted activities closely aligned with experimental antidiabetic activities, quantified by the IC50 values. They also revealed a significant superiority of the ANN architecture (6-4-1) over the MLR method. This study stands as a meaningful contribution to the field, providing valuable insights for designing new antidiabetic drugs, particularly as potential inhibitors of tyrosine phosphate 1B (PTP1B). The explicit articulation of our primary aim and emphasis on the study's significance underscore its potential impact on advancing drug development within the realm of antidiabetic therapeutics. [ABSTRACT FROM AUTHOR]

Recent research has uncovered a promising approach to addressing the growing global health concern of obesity and related disorders. The inhibition of inositol hexakisphosphate kinase 1 (IP6K1) has emerged as a potential therapeutic strategy. This study employs multiple ligand-based in silico modeling techniques to investigate the structural requirements for benzisoxazole derivatives as IP6K1 inhibitors. Firstly, we developed linear 2D Quantitative Structure–Activity Relationship (2D-QSAR) models to ensure both their mechanistic interpretability and predictive accuracy. Then, ligand-based pharmacophore modeling was performed to identify the essential features responsible for the compounds' high activity. To gain insights into the 3D requirements for enhanced potency against the IP6K1 enzyme, we employed multiple alignment techniques to set up 3D-QSAR models. Given the absence of an available X-ray crystal structure for IP6K1, a reliable homology model for the enzyme was developed and structurally validated in order to perform structure-based analyses on the selected dataset compounds. Finally, molecular dynamic simulations, using the docked poses of these compounds, provided further insights. Our findings consistently supported the mechanistic interpretations derived from both ligand-based and structure-based analyses. This study offers valuable guidance on the design of novel IP6K1 inhibitors. Importantly, our work exclusively relies on non-commercial software packages, ensuring accessibility for reproducing the reported models. [ABSTRACT FROM AUTHOR]

In response to the increasing prevalence of diabetes mellitus and the limitations associated with the current treatments, there is a growing need to develop novel medications for this disease. This study is focused on creating new compounds that exhibit a strong inhibition of alpha-glucosidase, which is a pivotal enzyme in diabetes control. A set of 33 triazole derivatives underwent an extensive QSAR analysis, aiming to identify the key factors influencing their inhibitory activity against α-glucosidase. Using the multiple linear regression (MLR) model, seven promising compounds were designed as potential drugs. Molecular docking and dynamics simulations were employed to shed light on the mode of interaction between the ligands and the target, and the stability of the obtained complexes. Furthermore, the pharmacokinetic properties of the designed compounds were assessed to predict their behavior in the human body. The binding free energy was also calculated using MMGBSA method and revealed favorable thermodynamic properties. The results highlighted three novel compounds with high biological activity, strong binding affinity to the target enzyme, and suitability for oral administration. These results offer interesting prospects for the development of effective and well-tolerated medications against diabetes mellitus. Dataset License: License under which the dataset is made available (CC0, CC-BY, CC-BY-SA, CC-BY-NC, etc.) [ABSTRACT FROM AUTHOR]

In the contemporary era, the exploration of machine learning (ML) has gained widespread attention and is being leveraged to augment traditional methodologies in quantitative structure–activity relationship (QSAR) investigations. The principal objective of this research was to assess the anticancer potential of colchicine-based compounds across five distinct cell lines. This research endeavor ultimately sought to construct ML models proficient in forecasting anticancer activity as quantified by the I C 50 value, while concurrently generating innovative colchicine-derived compounds. The resistance index (RI) is computed to evaluate the drug resistance exhibited by LoVo/DX cells relative to LoVo cancer cell lines. Meanwhile, the selectivity index (SI) is computed to determine the potential of a compound to demonstrate superior efficacy against tumor cells compared to its toxicity against normal cells, such as BALB/3T3. We introduce a novel ML system adept at recommending novel chemical structures predicated on known anticancer activity. Our investigation entailed the assessment of inhibitory capabilities across five cell lines, employing predictive models utilizing various algorithms, including random forest, decision tree, support vector machines, k-nearest neighbors, and multiple linear regression. The most proficient model, as determined by quality metrics, was employed to predict the anticancer activity of novel colchicine-based compounds. This methodological approach yielded the establishment of a library encompassing new colchicine-based compounds, each assigned an I C 50 value. Additionally, this study resulted in the development of a validated predictive model, capable of reasonably estimating I C 50 values based on molecular structure input. [ABSTRACT FROM AUTHOR]

A hypoxic environment occurs predominantly in tumors. During the growth phase of a tumor, it grows until it exceeds its blood supply, leaving regions of the tumor in which the oxygen pressure is dramatically low. They are virtually absent in normal tissues, thus creating perfect conditions for selective bioreductive therapy of tumors. To this aim, a novel series of cytotoxic radiosensitizer agents were synthesized by linking the nitroimidazole scaffold with oxadiazole or triazole rings. The majority of the compounds exhibited moderate to excellent antiproliferative activities toward HCT116 cell line under normoxic and hypoxic conditions. The structure–activity relationship study revealed that compounds containing the free thiol group either in the oxadiazoles 11a,b or the triazoles 21a,b–23a,b demonstrated the strongest antiproliferative activity, which proves that the free thiol group plays a crucial role in the antiproliferative activity of our compounds under both normoxic (half‐maximal inhibitory concentration [IC50] = 12.50–24.39 µM) and hypoxic conditions (IC50 = 4.69–11.56 µM). Radiosensitizing assay of the four most active cytotoxic compounds 11b and 21–23b assured the capability of the compounds to enhance the sensitivity of the tumor cells to the DNA damaging activity of γ‐radiation (IC50 = 2.23–5.18 µM). To further investigate if the cytotoxicity of our most active compounds was due to a specific signaling pathway, the online software SwissTargetPrediction was exploited and a molecular docking study was done that proposed cyclin‐dependent kinase 2 (CDK2) enzyme to be the most promising target. The CDK2 inhibitory assay assured this assumption as five out of six compounds demonstrated a comparable inhibitory activity with roscovitine, among which compound 21b showed threefold more potent inhibitory activity in comparison with the reference compound. A further biological evaluation proved compound 21b to have an apoptotic activity and cell cycle arrest activity at the G1 and S phases. During the AutoQSAR analysis, the model demonstrated excellent regression between the predicted and experimental activity with r2 = 0.86. Subsequently, we used the model to predict the activity of the test set compounds that came with r2 = 0.95. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) technology has recently used to predict substance properties based on their Simplified Molecular-Input Line-Entry System (SMILES). We aimed to develop a model predicting human skin sensitizers by integrating text features derived from SMILES with in vitro test outcomes. The dataset on SMILES, physicochemical properties, in vitro tests (DPRA, KeratinoSensTM, h-CLAT, and SENS-IS assays), and human potency categories for 122 substances sourced from the Cosmetics Europe database. The ChemBERTa model was employed to analyze the SMILES of substances. The last hidden layer embedding of ChemBERTa was tested with other features. Given the modest dataset size, we trained five XGBoost models using subsets of the training data, and subsequently employed bagging to create the final model. Notably, the features computed from SMILES played a pivotal role in the model for distinguishing sensitizers and non-sensitizers. The final model demonstrated a classification accuracy of 80% and an AUC-ROC of 0.82, effectively discriminating sensitizers from non-sensitizers. Furthermore, the model exhibited an accuracy of 82% and an AUC-ROC of 0.82 in classifying strong and weak sensitizers. In summary, we demonstrated that the integration of NLP of SMILES with in vitro test results can enhance the prediction of health hazard associated with chemicals. [ABSTRACT FROM AUTHOR]

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery. [ABSTRACT FROM AUTHOR]

Lysine-specific demethylase 1 (LSD1/KDM1A) has emerged as a promising therapeutic target for treating various cancers (such as breast cancer, liver cancer, etc.) and other diseases (blood diseases, cardiovascular diseases, etc.), owing to its observed overexpression, thereby presenting significant opportunities in drug development. Since its discovery in 2004, extensive research has been conducted on LSD1 inhibitors, with notable contributions from computational approaches. This review systematically summarizes LSD1 inhibitors investigated through computer-aided drug design (CADD) technologies since 2010, showcasing a diverse range of chemical scaffolds, including phenelzine derivatives, tranylcypromine (abbreviated as TCP or 2-PCPA) derivatives, nitrogen-containing heterocyclic (pyridine, pyrimidine, azole, thieno[3,2-b]pyrrole, indole, quinoline and benzoxazole) derivatives, natural products (including sanguinarine, phenolic compounds and resveratrol derivatives, flavonoids and other natural products) and others (including thiourea compounds, Fenoldopam and Raloxifene, (4-cyanophenyl)glycine derivatives, propargylamine and benzohydrazide derivatives and inhibitors discovered through AI techniques). Computational techniques, such as virtual screening, molecular docking and 3D-QSAR models, have played a pivotal role in elucidating the interactions between these inhibitors and LSD1. Moreover, the integration of cutting-edge technologies such as artificial intelligence holds promise in facilitating the discovery of novel LSD1 inhibitors. The comprehensive insights presented in this review aim to provide valuable information for advancing further research on LSD1 inhibitors. [ABSTRACT FROM AUTHOR]

TRPV1 channel agonists and antagonists, which have powerful analgesic effects without the addictive qualities associated with traditional analgesics, have become a focus area for the development of novel analgesics. In this study, quantitative structure–activity relationship (QSAR) models for three bioactive endpoints (Ki, IC50, and EC50) were successfully constructed using four machine learning algorithms: SVM, Bagging, GBDT, and XGBoost. These models were based on 2922 TRPV1 modulators and incorporated four types of molecular descriptors: Daylight, E-state, ECFP4, and MACCS. After the rigorous five-fold cross-validation and external test set validation, the optimal models for the three endpoints were obtained. For the Ki endpoint, the Bagging-ECFP4 model had a Q2 value of 0.778 and an R2 value of 0.780. For the IC50 endpoint, the XGBoost-ECFP4 model had a Q2 value of 0.806 and an R2 value of 0.784. For the EC50 endpoint, the SVM-Daylight model had a Q2 value of 0.784 and an R2 value of 0.809. These results demonstrate that the constructed models exhibit good predictive performance. In addition, based on the model feature importance analysis, the influence between substructure and biological activity was also explored, which can provide important theoretical guidance for the efficient virtual screening and structural optimization of novel TRPV1 analgesics. And subsequent studies on novel TRPV1 modulators will be based on the feature substructures of the three endpoints. [ABSTRACT FROM AUTHOR]

Penetration through the blood-brain barrier (BBB) is desirable in the case of potential pharmaceuticals acting on the central nervous system (CNS), but is undesirable in the case of drug candidates acting on the peripheral nervous system because it may cause CNS side effects. Therefore, modeling of the permeability across the blood-brain barrier (i.e., the logarithm of the brain to blood concentration ratio, log BB) of potential pharmaceuticals should be performed as early as possible in the preclinical phase of drug development. Biomimetic chromatography with immobilized artificial membrane (IAM) and the quantitative structure-activity relationship (QSAR) methodology were successful in modeling the blood-brain barrier permeability of 126 drug candidates, whose experimentally-derived lipophilicity indices and computationally-derived molecular descriptors (such as molecular weight (MW), number of rotatable bonds (NRB), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), topological polar surface area (TPSA), and polarizability (α)) varied by class. The QSARs model established by multiple linear regression showed a positive effect of the lipophilicity (log kw, IAM) and molecular weight of the compound, and a negative effect of the number of hydrogen bond donors and acceptors, on the log BB values. The model has been cross-validated, and all statistics indicate that it is very good and has high predictive ability. The simplicity of the developed model, and its usefulness in screening studies of novel drug candidates that are able to cross the BBB by passive diffusion, are emphasized. [ABSTRACT FROM AUTHOR]

Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the "Anti-Dengue" algorithm that predicts dengue virus inhibitors using a quantitative structure–activity relationship (QSAR) and MLTs. Using the "DrugRepV" database, we extracted chemicals (small molecules) and repurposed drugs targeting the dengue virus with their corresponding IC50 values. Then, molecular descriptors and fingerprints were computed for these molecules using PaDEL software. Further, these molecules were split into training/testing and independent validation datasets. We developed regression-based predictive models employing 10-fold cross-validation using a variety of machine learning approaches, including SVM, ANN, kNN, and RF. The best predictive model yielded a PCC of 0.71 on the training/testing dataset and 0.81 on the independent validation dataset. The created model's reliability and robustness were assessed using William's plot, scatter plot, decoy set, and chemical clustering analyses. Predictive models were utilized to identify possible drug candidates that could be repurposed. We identified goserelin, gonadorelin, and nafarelin as potential repurposed drugs with high pIC50 values. "Anti-Dengue" may be beneficial in accelerating antiviral drug development against the dengue virus. [ABSTRACT FROM AUTHOR]

Sphingomyelin synthase 2 (SMS2) has emerged as a promising target for atherosclerosis threatment. However, the availability of selective SMS2 inhibitors and their associated pharmacological properties remains limited. This research paper explores various QSAR modeling techniques applied to a range of compounds acting as SMS2 inhibitors. Multiple distinct QSAR modeling methodologies were employed, including conformation-independent, GA-MLR and 3D based QSAR modeling, and their mutual correlations were investigated, Various statistical methods were applied to assess the quality, robustness, and predictive capacity of these developed models, yielding favorable results. Furthermore, molecular fragments derived from SMILES notation descriptors, which account for the observed changes in the evaluated activity, were defined. The methodology presented in this research holds potential for identifying novel agents for atherosclerosis treatment by targeting sphingomyelin synthase 2. [ABSTRACT FROM AUTHOR]

Background: The development and application of machine learning (ML) methods have become so fast that almost nobody can follow their developments in every detail. It is no wonder that numerous errors and inconsistencies in their usage have also spread with a similar speed independently from the tasks: regression and classification. This work summarizes frequent errors committed by certain authors with the aim of helping scientists to avoid them. Methods: The principle of parsimony governs the train of thought. Fair method comparison can be completed with multicriteria decision-making techniques, preferably by the sum of ranking differences (SRD). Its coupling with analysis of variance (ANOVA) decomposes the effects of several factors. Earlier findings are summarized in a review-like manner: the abuse of the correlation coefficient and proper practices for model discrimination are also outlined. Results: Using an illustrative example, the correct practice and the methodology are summarized as guidelines for model discrimination, and for minimizing the prediction errors. The following factors are all prerequisites for successful modeling: proper data preprocessing, statistical tests, suitable performance parameters, appropriate degrees of freedom, fair comparison of models, and outlier detection, just to name a few. A checklist is provided in a tutorial manner on how to present ML modeling properly. The advocated practices are reviewed shortly in the discussion. Conclusions: Many of the errors can easily be filtered out with careful reviewing. Every authors' responsibility is to adhere to the rules of modeling and validation. A representative sampling of recent literature outlines correct practices and emphasizes that no error-free publication exists. [ABSTRACT FROM AUTHOR]

The quinolizidine alkaloids matrine and its N -oxide oxymatrine occur in plants of the genus Sophora. Recently, matrine was sporadically detected in liquorice products. Morphological similarity of the liquorice plant Glycyrrhiza glabra with Sophora species and resulting confusion during harvesting may explain this contamination, but use of matrine as pesticide has also been reported. The detection of matrine in liquorice products raised concern as some studies suggested a genotoxic activity of matrine and oxymatrine. However, these studies are fraught with uncertainties, putting the reliability and robustness into question. Another issue was that Sophora root extracts were usually tested instead of pure matrine and oxymatrine. The aim of this work was therefore to determine whether matrine and oxymatrine have potential for causing gene mutations. In a first step and to support a weight-of-evidence analysis, in silico predictions were performed to improve the database using expert and statistical systems by VEGA, Leadscope (Instem®), and Nexus (Lhasa Limited). Unfortunately, the confidence levels of the predictions were insufficient to either identify or exclude a mutagenic potential. Thus, in order to obtain reliable results, the bacterial reverse mutation assay (Ames test) was carried out in accordance with OECD Test Guideline 471. The test set included the plate incorporation and the preincubation assay. It was performed with five different bacterial strains in the presence or absence of metabolic activation. Neither matrine nor oxymatrine induced a significant increase in the number of revertants under any of the selected experimental conditions. Overall, it can be concluded that matrine and oxymatrine are unlikely to have a gene mutation potential. Any positive findings with Sophora extracts in the Ames test may be related to other components. Notably, the results also indicated a need to extend the application domain of respective (Q)SAR tools to secondary plant metabolites. [ABSTRACT FROM AUTHOR]

Diabetes, a metabolic disease characterized by hyperglycemia, seriously endangers the health and the lives of people. α-Amylase inhibitors have become effective substances to control blood glucose, and attracted extensive attention. In this study, a database of α-amylase inhibitors derived from naturally active small molecules in food was created and a quantitative structure-activity relationship model was developed by combining three machine learning methods (SVM, RF, and LDA) with four descriptors (MOE, ChemoPy, Mordred, and Rdkit). Hydrogen bond and hydrophobic interaction in the inhibition of α-amylase activity was confirmed by molecular docking. Enzyme inhibition experiments showed that the predicted compound had α-amylase inhibitory activity. Nevadensin was identified as a promising candidate of α-amylase inhibitors. The stability of α-amylase binding reaction was verified by molecular dynamics simulation. Optimal process conditions for the extraction of nevadensin from L. pauciflorus maxim were derived from single-factor experiments and response surface modeling. A promising method for digging natural α-amylase inhibitors was developed and the mode between inhibitors and α-amylase was explained in this research. [Display omitted] • A strategy for the development of a novel food-derived hypoglycemic compound. • QSAR utilizes molecular structure for high-throughput screening α-amylase inhibitor. • Bioinformatics facilitates the discovery of new food-derived hypoglycemic compounds. • Molecular dynamics simulations of selected compounds. • The nevadensin extraction process was optimized through the response surface model. [ABSTRACT FROM AUTHOR]

Structural modifications of ethyl-3-((5-bromopyridin-2-yl)imino)butanoate ester enhances its biological activities. In the present work, the synthesis of a Schiff base by reacting 2-amino-5-bromo pyridine and ethyl acetoacetate was reported. Electronic properties were investigated by the computational method of density functional theory (DFT). The pass prediction score supported anti-viral efficacy which satisfied the Lipiniski rule and overall HOMO-LUMO. The most important part of this investigation was docking study which revealed that the ligands L07 has nearly efficacy to standard drugs and maximum docking score was found against Alpha variant (7EKF) at −6.7 kcal/mol, Beta variant (7ekg) at −6.2 kcal/mol, Gamma variant (7EKC) at −6.5 kcal/mol, Beta variant (7ekg) at −6.2 kcal/mol, SARS-CoV-2 Gamma variant (7EKC) at −6.2 kcal/mol, Delta variant (7V8B) at −6.1 kcal/mol and Omicron variant (7T9J) at −6.3 kcal/mol. Finally, molecular dynamics study was performed to determine the stability and it was reported that the molecules L07 has better stability to be an oral drug against the different variant of SARS-CoV-2 viral pathogen. [ABSTRACT FROM AUTHOR]

Background: Resistance to most of the antitubercular drugs has been on rising trends due to the misuse of existing drugs. This has encouraged us to explore a novel scaffold that has the potential for quick antimicrobial action with minimum side effects. Nitrofurans have attracted us due to their extensive biological activities, such as antibacterial and antifungal activities. Objective: The antitubercular activities of 126 nitrofuran derivatives have been investigated by using indicator parameters and topological and structural fragment descriptors. Methods: The different quantitative structure activity relationship (QSAR) models have been created and validated by using two different methodologies: combinatorial protocol in multiple linear regression (CP-MLR) and partial least-squares (PLS) analysis. Results: The 16 descriptors identified in CP-MLR are from six different classes: Constitutional, Functional, Atom Centered Fragments, Topological, Galvez, and 2D autocorrelation. Indicator parameters and Dragon descriptors suggested that the presence of a furan ring substituted by nitro group is essential for antitubercular activity. Further descriptors from constitutional, and functional classes suggest that the number of double bonds, number of sulphur atoms and number of fragments like thiazole, morpholine and thiophene should be minimum, along with the positive influence of Kier‐ Hall electrotopological states (Ss) for improved activity. The ACF class descriptors, GALVEZ class descriptors, and 2D-AUTO descriptor GATS4p have also shown positive influence on the antitubercular activity. The TOPO class descriptor T(O…S) suggests that the minimum gap between sulphur and oxygen is favorable for activity. Conclusions: The models acknowledged in the study have explained the variance between 72 to 76% in the training set and in the prediction of the test set compounds. Also, compounds 122, 123 and 82 were found to possess good binding affinity towards nitroreductase. [ABSTRACT FROM AUTHOR]

Effective plant pathogen control presents an important challenge. Pesticide resistance and associated health and environmental problems demonstrate the need for novel, safe active ingredients for plant protection. Since both coumarins and 1,2,4-triazoles show pesticidal activity, in this study, we evaluated the antimicrobial activity of coumarin-1,2,4-triazole hybrids against plant pathogenic fungi (Fusarium oxysporum, Fusarium culmorum,Macrophomina phaseolina and Sclerotinia sclerotiorum), bacterial plant pathogens (Pseudomonas syringae and Rhodococcus fascians), and beneficial bacteria (Bacillusmycoides and Bradyrhizobium japonicum). Coumarin-1,2,4- triazoles inhibited the growth of S. sclerotiorum and F. oxysporum, while no antibacterial effect on either pathogens or soil-beneficial bacteria was observed. A quantitative structure–activity relationship models for antifungal activities on S. sclerotiorum and F. oxysporum, developed using Dragon descriptors, can explain 79% and 77% of the compounds inhibitory activity, respectively. According to molecular docking, title compounds are potential sterol 14α-demethylase inhibitors, with 7-((5-mercapto-4-(p-tolyl)-4H-1,2,4-triazol-3-yl)methoxy)-4-methyl-2H-chromen2-one as the promising candidate for further research in plant protection. [ABSTRACT FROM AUTHOR]

USP7 is highly expressed in a variety of tumors and is thought to play a major role in cancer development. However, there are no drugs available to target USP7, so there is a need to develop new USP7 inhibitors. In this study, AutoQSAR, multiple linear regression, and Naive Bayesian models were constructed using 543 compounds and used to analyze marine compounds. After selecting 240 small molecules for molecular docking with Maestro, MOE, and GOLD, better small molecules than the positive compound P217564 were screened. The molecular structure of "1, 2-dibromobenzene" was optimized to improve the binding effect of the protein, and 10 optimized compounds in ADMET performed well during the screening process. To study the dynamic combination of protein–ligand effect consistency with static molecular docking, 100ns molecular dynamics simulations of candidate compound 1008-1, reference compound P217564, and negative-positive GNE2917 were conducted. The results of molecular docking and molecular dynamics simulation analysis showed that compound 1008-1 maintained a stable conformation with the target protein. Thus, the comprehensive analysis suggests that compound 1008-1 could provide new possibilities for USP7 covalent inhibitor candidates. [ABSTRACT FROM AUTHOR]

This study aims to predict AXL kinase inhibitors utilizing a Bayesian Optimization-XGBoost machine learning model. A dataset comprising 1074 compounds with IC50 values was collected from the ChEMBL database and molecular descriptors for each compound were calculated. The Bayesian Optimization-XGBoost model demonstrated superior performance in predicting AXL kinase inhibitors, achieving an accuracy of 86.24%, precision of 89.52%, recall of 89.52%, and an F1-score of 89.52%, outperforming other models such as LightGBM, Logistic Regression, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Naïve Bayes. This study underlines the importance of advanced machine learning techniques, particularly Bayesian Optimization-XGBoost, in predicting AXL kinase inhibitors, offering a promising approach for accelerating the early stages of drug discovery. Despite its success, the model's performance depends on the diversity and quality of the training data, and future work should focus on expanding the dataset and validating results with experimental studies. This computational method has the potential to streamline the drug development pipeline and contribute to the discovery of more effective cancer treatments. [ABSTRACT FROM AUTHOR]

Background: The objective of this study is to find novel antineoplastic agents that display greater toxicity to malignant cells than to neoplasms. In addition, the mechanisms of action of representative compounds are sought. This report describes the cytotoxicity of a number of dimers of 3,5-bis(benzylidene)-4-piperidones against human malignant cells (promyelocytic leukemia HL-60 and squamous cell carcinoma HSC-2, HSC-3, and HSC-4). Methods: Tumor specificity was evaluated by the selectivity index (SI), that is the ratio of the mean CC50 for human non-malignant oral cells (gingival fibroblasts, pulp cells, periodontal ligament fibroblasts) to that for malignant cells. Results: The compounds were highly toxic to human malignant cells. On the other hand, these molecules were less toxic to human non-malignant cells. In particular, a potent lead molecule, 3b, was identified. A QSAR study revealed that the placement of electron-releasing and hydrophilic substituents into the aryl rings led to increases in cytotoxic potencies. The modes of action of a lead compound discovered in this study designated 3b were the activation of caspases-3 and -7, as well as causing PARP1 cleavage and G2 arrest, followed by sub-G1 accumulation in the cell cycle. This compound also depolarized the mitochondrial membrane and generated reactive oxygen species in human colon carcinoma HCT116 cells. In conclusion, this study has revealed that, in general, the compounds described in this report are tumor-selective cytotoxins. [ABSTRACT FROM AUTHOR]

Breast cancer is the most frequent form of malignant tumor in women, and represents a major public health problem due to its high mortality rate. Although a multitude of therapeutic options exist for control of this disease, the emergence of resistance to current pharmaceutical treatments underscores the urgency of developing new anti- breast cancer drugs, with a focus on reducing the adverse effects associated with current therapeutic agents. The present study concerns a new series of (23) compounds based on 1,4-quinone and quinoline derivatives to design candidate drugs against breast cancer. For this purpose, integrated computational techniques were applied, including 3D-QSAR, molecular docking and molecular dynamics simulations (MD). CoMFA and CoMSIA were used to build a robust and highly reliable 3D-QSAR models. To validate the model's predictive capabilities, an external validation was carried out. The results of the best model (CoMSIA/SEA) revealed that electrostatic, steric and hydrogen bond acceptor fields had a significant effect on the anti-breast cancer activity of molecules studied. In addition, evaluation of ADMET properties determined whether these newly designed ligands were likely to be selected as drug-candidates. To confirm the binding stability of the selected ligands to aromatase (3S7S) and validate the molecular docking results, molecular dynamics simulations lasting 100 nanoseconds were performed by calculating RMSD, RMSF, RoG, H-bond, SASA and MM-PBSA parameters. As a result, only one designed compound (ligand 5) emerged as the most promising drug candidate for experimental in vitro and in vivo testing, due to its potential inhibition of breast cancer.