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Recently, seven dihalohydroxybenzonitriles (diHHBNs) have been determined as concerning nitrogenous aromatic disinfection byproducts (DBPs) in drinking water. Herein, eight new monohalohydroxybenzonitriles (monoHHBNs), including 3-chloro-2-hydroxybenzonitrile, 5-chloro-2-hydroxybenzonitrile, 3-chloro-4-hydroxybenzonitrile, 3-bromo-2-hydroxybenzonitrile, 5-bromo-2-hydroxybenzonitrile, 3-bromo-4-hydroxybenzonitrile, 5-iodo-2-hydroxybenzonitrile, and 3-iodo-4-hydroxybenzonitrile, were detected and identified in drinking water for the first time. Thereafter, the relative concentration-cytotoxicity contribution of each HHBN was calculated based on the acquired occurrence level and cytotoxicity data in this study, the genome-scale cytotoxicity mechanism was explored, and a quantitative structure-activity relationship (QSAR) model was developed. Results indicated that new monoHHBNs were present in drinking water at concentrations of 0.04-1.83 ng/L and exhibited higher cytotoxicity than some other monohalogenated aromatic DBPs. Notably, monoHHBNs showed concentration - cytotoxicity contribution comparable to diHHBNs, which have been previously identified as potential toxicity drivers in drinking water. Transcriptomic analysis revealed immunotoxicity and genotoxicity as dominant cytotoxicity mechanisms for HHBNs in Chinese hamster ovary (CHO-K1) cells, with potential carcinogenic effects. The QSAR model suggested oxidative stress and cellular uptake efficiency as important factors for their cytotoxicity, highlighting the importance of potential iodinated HHBNs in drinking water, such as 3,5-diiodo-2-hydroxybenzonitrile, for future studies. These findings are meaningful for better understanding the health risk and toxicological significance of HHBNs in drinking water.

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.

Cystic fibrosis (CF) is caused by the functional expression defect of the cystic fibrosis transmembrane conductance regulator (CFTR) protein. Despite the recent success in CFTR modulator development, the available correctors only partially restore the F508del-CFTR channel function, and several rare CF mutations show resistance to available drugs. We previously identified compound 4172 that synergistically rescued the F508del-CFTR folding defect in combination with the existing corrector drugs VX-809 and VX-661. Here, novel CFTR correctors were designed by applying a classical medicinal chemistry approach on the 4172 scaffold. Molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted to propose a plausible binding site and design more potent and effective analogs. We identified three optimized compounds, which, in combination with VX-809 and the investigational corrector 3151 , increased the plasma membrane density and function of F508del-CFTR and other rare CFTR mutants resistant to the currently approved therapies.

Acute oral toxicity is currently not available for most polycyclic aromatic hydrocarbons (PAHs), especially their derivatives, because it is cost-prohibitive to experimentally determine all of them. Here, quantitative structure-activity relationship (QSAR) models using machine learning (ML) for predicting the toxicity of PAH derivatives were developed, based on oral toxicity data points of 788 individual substances of rats. Both the individual ML algorithm gradient boosting regression trees (GBRT) and the stacking ML algorithm (extreme gradient boosting + GBRT + random forest regression) provided the best prediction results with satisfactory determination coefficients for both cross-validation and the test set. It was found that those PAH derivatives with fewer polar hydrogens, more large-sized atoms, more branches, and lower polarizability have higher toxicity. Software based on the optimal ML-QSAR model was successfully developed to expand the application potential of the developed model, obtaining reliable prediction of pLD 50 values and reference doses for 6893 external PAH derivatives. Among these chemicals, 472 were identified as moderately or highly toxic; 10 out of them had clear environment detection or use records. The findings provide valuable insights into the toxicity of PAHs and their derivatives, offering a standard platform for effectively evaluating chemical toxicity using ML-QSAR models.

With the development of large numbers of novel organophosphate esters (OPEs) alternatives, it is imperative to screen and identify those with high priority. In this study, surface water, biofilms, and freshwater snails were collected from the flow-in rivers of Taihu Lake Basin, China. Screened by target, suspect, and nontarget analysis, 11 traditional and 14 novel OPEs were identified, of which 5 OPEs were first discovered in Taihu Lake Basin. The OPE concentrations in surface water ranged from 196 to 2568 ng/L, with the primary homologue tris(2,4-di tert -butylphenyl) phosphate (TD t BPP) being newly identified, which was likely derived from the transformation of tris(2,4-di tert -butylphenyl) phosphite. The majority of the newly identified OPEs displayed substantially higher bioaccumulation and biomagnification potentials in the biofilm-snail food chain than the traditional ones. Quantitative structure-property relationship models revealed both hydrophobicity and polarity influenced the bioaccumulation and biomagnification of the OPEs, while electrostatic attraction also had a contribution to the bioaccumulation in the biofilm. TD t BPP was determined as the utmost priority by toxicological priority index scheme, which integrated concentration, bioaccumulation, biomagnification, acute toxicity, and endocrine disrupting potential of the identified OPEs. These findings provide novel insights into the behaviors of OPEs and scientific bases for better management of high-risk pollutants in aquatic ecosystem.

Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to understand the structure-activity relationships that exist between biomolecules and druggable targets. More recently, these methods have also seen application for phenotypic screening data for neglected diseases such as tuberculosis and malaria. Herein, we apply machine learning to build quantum Quantitative Structure Activity Relationship models from antimalarial data sets. There is a continual need for new antimalarials to address drug resistance, and the readily available in vitro data sets could be utilized with newer machine learning approaches as these develop. Furthermore, quantum machine learning is a relatively new method that uses a quantum computer to perform the calculations. First, we present a classical-quantum hybrid computational approach by building a Latent Bernoulli Autoencoder machine learning model for compressing bit-vector descriptors to a size that can be adapted to quantum computers for classification tasks with limited loss of embedded information. Second, we apply our method for feature map compression to quantum classification algorithms, including a completely novel machine learning algorithm with no analogy in classical computers: the Quantum Fourier Transform Classifier. We apply both these approaches to build quantum machine learning models for small-molecule antimalarials with quantum simulation software and then benchmark these quantum models against classical machine learning approaches. While there are many challenges currently facing the development of reliable quantum computers, our results demonstrate that there is potential for the use of this technology in the field of drug discovery.

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.

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.

Peptide-based drug discovery has surged with the development of peptide hormone-derived analogs for the treatment of diabetes and obesity. Machine learning (ML)-enabled quantitative structure-activity relationship (QSAR) approaches have shown great promise in small molecule drug discovery but have been less successful in peptide drug discovery due to limited data availability. We have developed a peptide drug discovery platform called streaMLine, enabling rigorous design, synthesis, screening, and ML-driven analysis of large peptide libraries. Using streaMLine, this study systematically explored secretin as a peptide backbone to generate potent, selective, and long-acting GLP-1R agonists with improved physicochemical properties. We synthesized and screened a total of 2688 peptides and applied ML-guided QSAR to identify multiple options for designing stable and potent GLP-1R agonists. One candidate, GUB021794, was profiled in vivo (S.C., 10 nmol/kg QD) and showed potent body weight loss in diet-induced obese mice and a half-life compatible with once-weekly dosing.

In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of computational models to predict drug clearance. However, concerns persist regarding the reliability of data collected from public sources, and a majority of current in silico quantitative structure-property relationship models tend to neglect the influence of molecular chirality. In this study, we meticulously examined human liver microsome (HLM) data from public databases and constructed two distinct data sets with varying HLM data quantity and quality. Two baseline models (RF and DNN) and three chirality-focused GNNs (DMPNN, TetraDMPNN, and ChIRo) were proposed, and their performance on HLM data was evaluated and compared with each other. The TetraDMPNN model, which leverages chirality from 2D structure, exhibited the best performance with a test R 2 of 0.639 and a test root-mean-squared error of 0.429. The applicability domain of the model was also defined by using a molecular similarity-based method. Our research indicates that graph neural networks capable of capturing molecular chirality have significant potential for practical application and can deliver superior performance.

Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.

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.

To synthesize the fundamental framework of dihydroagarofuran, a novel strategy was devised for constructing the C-ring through a dearomatization reaction using 6-methoxy-1-tetralone as the initial substrate. Subsequently, the dihydroagarofuran skeleton was assembled via two consecutive Michael addition reactions. The conjugated diene and trans-dihydroagarofuran skeleton were modified. The insecticidal activities of 33 compounds against Mythimna separata were evaluated. Compounds 11 - 5 exhibited an LC 50 value of 0.378 mg/mL. The activity exhibited a remarkable 29-fold increase compared to positive control Celangulin V, which was widely recognized as the most renowned natural dihydroagarofuran polyol ester insecticidal active compound. Docking experiments between synthetic compounds and target proteins revealed the shared binding sites with Celangulin V. Structure-activity relationship studies indicated that methyl groups at positions C4 and C10 significantly improved insecticidal activity, while ether groups with linear chains displayed enhanced activity; in particular, the allyl ether group demonstrated optimal efficacy. Furthermore, a three-dimensional quantitative structure-activity relationship model was established to investigate the correlation between the skeletal structure and activity. These research findings provide valuable insights for discovering and developing dihydroagarofuran-like compounds.

This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.

Oxaliplatin-based chemotherapy has proven to be one of the most effective treatments for advanced or metastatic colorectal cancer. However, increasing clinical resistance to oxaliplatin poses unprecedented challenges for both patients and clinicians. Despite extensive efforts to combat this issue, to date, no new molecules have been discovered that can successfully replace oxaliplatin. With the aim of developing a new generation of Pt(II)-based anticancer agents in response to the challenges of oxaliplatin-induced drug resistance, we performed a systematic screening of new Pt(II)-complexes with a quantitative structure-activity relationship (QSAR) study based on their antiresistance activity against oxaliplatin-resistant colon cancer cells. The results revealed that both the structure and chirality of the chelating ligand had a significant impact on the antiresistance properties of the Pt(II)-complexes. Our study culminated in the identification of chiral R -binaphthyldiamine-ligated Pt(II)-malonatoglycoconjugates that can completely counteract oxaliplatin resistance with excellent in vitro and in vivo potency.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the substantia nigra, resulting in motor dysfunction. Current treatments are primarily centered around enhancing dopamine signaling or providing dopamine replacement therapy and face limitations such as reduced efficacy over time and adverse side effects. To address these challenges, we identified selective dopamine receptor subtype 4 (D 4 R) antagonists not previously reported as potential adjuvants for PD management. In this study, a library screening and artificial neural network quantitative structure-activity relationship (QSAR) modeling with experimentally driven library design resulted in a class of spirocyclic compounds to identify candidate D 4 R antagonists. However, developing selective D 4 R antagonists suitable for clinical translation remains a challenge.

The human Ether-à-go-go-Related Gene (hERG) is a transmembrane protein that regulates cardiac action potential, and its inhibition can induce a potentially deadly cardiac syndrome. In vitro tests help identify hERG blockers at early stages; however, the high cost motivates searching for alternative, cost-effective methods. The primary goal of this study was to enhance the Pred-hERG tool for predicting hERG blockage. To achieve this, we developed new QSAR models that incorporated additional data, updated existing classificatory and multiclassificatory models, and introduced new regression models. Notably, we integrated SHAP (SHapley Additive exPlanations) values to offer a visual interpretation of these models. Utilizing the latest data from ChEMBL v30, encompassing over 14,364 compounds with hERG data, our binary and multiclassification models outperformed both the previous iteration of Pred-hERG and all publicly available models. Notably, the new version of our tool introduces a regression model for predicting hERG activity (pIC50). The optimal model demonstrated an R 2 of 0.61 and an RMSE of 0.48, surpassing the only available regression model in the literature. Pred-hERG 5.0 now offers users a swift, reliable, and user-friendly platform for the early assessment of chemically induced cardiotoxicity through hERG blockage. The tool provides versatile outcomes, including (i) classificatory predictions of hERG blockage with prediction reliability, (ii) multiclassificatory predictions of hERG blockage with reliability, (iii) regression predictions with estimated pIC 50 values, and (iv) probability maps illustrating the contribution of chemical fragments for each prediction. Furthermore, we implemented explainable AI analysis (XAI) to visualize SHAP values, providing insights into the contribution of each feature to binary classification predictions. A consensus prediction calculated based on the predictions of the three developed models is also present to assist the user's decision-making process. Pred-hERG 5.0 has been designed to be user-friendly, making it accessible to users without computational or programming expertise. The tool is freely available at http://predherg.labmol.com.br.

Skin sensitization is increasingly becoming a significant concern in the development of drugs and cosmetics due to consumer safety and occupational health problems. In silico methods have emerged as alternatives to traditional in vivo animal testing due to ethical and economic considerations. In this study, machine learning methods were used to build quantitative structure-activity relationship (QSAR) models on five skin sensitization data sets (GPMT, LLNA, DPRA, KeratinoSens, and h-CLAT), achieving effective predictive accuracies (correct classification rates of 0.688-0.764 on test sets). To address the complex mechanisms of human skin sensitization, the Dempster-Shafer theory was applied to merge multiple QSAR models, resulting in an evidence-based integrated decision model. Various evidence combinations and combination rules were explored, with the self-defined Q3 rule showing superior balance. The combination of evidence such as GPMT and KeratinoSens and h-CLAT achieved a correct classification rate (CCR) of 0.880 and coverage of 0.893 while maintaining the competitiveness of other combinations. Additionally, the Shapley additive explanations (SHAP) method was used to interpret important features and substructures related to skin sensitization. A comparative analysis of an external human test set demonstrated the superior performance of the proposed method. Finally, to enhance accessibility, the workflow was implemented into a user-friendly software named HSkinSensDS.

In order to speculate the three-dimensional structure of the potential binding pocket of the chitin synthase inhibitor, a series of 2,4-diphenyloxazoline derivatives with different lengths of alkyl chains and heteroatoms were designed and synthesized by a homologous strategy. The bioassay results indicate that both the length of the alkyl chains and the type of substituents can affect the acaricidal activity against mite eggs. Compounds containing chloropropyl, alkoxyalkyl, and para-substituted phenoxyalkyl or phenylthioalkyl groups exhibit good activity, while those containing steric hindrance substituents or carbonyl substituents on the benzene ring exhibit reduced activity. Three-dimensional quantitative structure-activity relationship (3D-QSAR) study showed that there may be a narrow hydrophobic region deep in the pocket, and the steric effect plays a more important role than the electrostatic effect. The current work will provide assistance for future molecular design and target binding research.

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result ( R 2 train = 0.93, R 2 ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

A series of novel 2-Ar-1,2,3-triazole derivatives were designed and synthesized based on our previously discovered active compound 6d against Rhizoctonia solani . Most of these compounds exhibited good antifungal activity against R. solani at a concentration of 25 μg/mL. Based on the results of biological activity, we established a three-dimensional quantitative structure-activity relationship (3D-QSAR) model that guided the synthesis of compound 7y . Compound 7y exhibited superior activity against R. solani (EC 50 = 0.47 μg/mL) compared to the positive controls hymexazol (EC 50 = 12.80 μg/mL) and tebuconazole (EC 50 = 0.87 μg/mL). Furthermore, compound 7y demonstrated better protective activity than the aforementioned two commercial fungicides in both detached leaf assays and greenhouse experiments, achieving 56.21% and 65.75% protective efficacy, respectively, at a concentration of 100 μg/mL. The ergosterol content was determined and molecular docking was performed to explore the mechanism of these active molecules. DFT calculation and MEP analysis were performed to illustrate the results of this study. These results suggest that compound 7y could serve as a novel 2-Ar-1,2,3-triazole lead compound for controlling R. solani.

The pursuit of new succinate dehydrogenase (SDH) inhibitors is a leading edge in fungicide research and development. The use of 3D quantitative structure-activity relationship (3D-QSAR) models significantly enhances the development of compounds with potent antifungal properties. In this study, we leveraged the natural product coumarin as a molecular scaffold to synthesize 74 novel 3-coumarin hydrazide derivatives. Notably, compounds 4ap (0.28 μg/mL), 6ae (0.32 μg/mL), and 6ah (0.48 μg/mL) exhibited exceptional in vitro effectiveness against Rhizoctonia solani , outperforming the commonly used fungicide boscalid (0.52 μg/mL). Furthermore, compounds 4ak (0.88 μg/mL), 6ae (0.61 μg/mL), 6ah (0.65 μg/mL), and 6ak (1.11 μg/mL) showed significant activity against Colletotrichum orbiculare , surpassing both the SDHI fungicide boscalid (43.45 μg/mL) and the broad-spectrum fungicide carbendazim (2.15 μg/mL). Molecular docking studies and SDH enzyme assays indicate that compound 4ah may serve as a promising SDHI fungicide. Our ongoing research aims to refine this 3D-QSAR model further, enhance molecular design, and conduct additional bioactivity assays.

The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure-activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.

Dipeptidyl peptidase-IV (DPP-IV) inhibiting peptides have attracted increased attention because of their possible beneficial effects on glycemic homeostasis. However, the structural basis underpinning their activities has not been well understood. This study combined computational and in vitro investigations to explore the structural basis of DPP-IV inhibitory peptides. We first superimposed the Xaa-Pro-type peptide-like structures from several crystal structures of DPP-IV ligand-protein complexes to analyze the recognition interactions of DPP-IV to peptides. Thereafter, a small set of Xaa-Pro-type peptides was designed to explore the effect of key interactions on inhibitory activity. The intramolecular interaction of Xaa-Pro-type peptides at the first and third positions from the N-terminus was pivotal to their inhibitory activities. Residue interactions between DPP-IV and residues of the peptides at the fourth and fifth positions of the N-terminus contributed significantly to the inhibitory effect of Xaa-Pro-type tetrapeptides and pentapeptides. Based on the interaction descriptors, quantitative structure-activity relationship (QSAR) studies with the DPP-IV inhibitory peptides resulted in valid models with high R 2 values (0.90 for tripeptides; 0.91 for tetrapeptides and pentapeptides) and Q 2 values (0.33 for tripeptides; 0.68 for tetrapeptides and pentapeptides). Taken together, the structural information on DPP-IV and peptides in this study facilitated the development of novel DPP-IV inhibitory peptides.

Generating the three-dimensional (3D) structure of small molecules is crucial in both structure- and ligand-based drug design. Structure-based drug design needs bioactive conformations of compounds for lead identification and optimization. Ligand-based drug design techniques, such as 3D shape similarity search, 3D pharmacophore model, 3D-QSAR, etc., all require high-quality small-molecule ligand conformations to obtain reliable results. Although predicting a small molecular bioactive conformer requires information from the receptor, a crystal structure of the molecule is a proper approximation to its bioactive conformer in a specific receptor because the binding pose of a small molecule in its receptor's binding pockets should be energetically close to the crystal structures. This study presents a de novo small molecular structure predictor (dMXP) with graph attention networks based on crystal data derived from the Cambridge Structural Database (CSD) combined with molecular electrostatic information calculated by density-functional theory (DFT). Two featuring strategies (topological and atomic partial change features) were employed to explore the relation between these features and the 3D crystal structure of a small molecule. These features were then assembled to construct the holistic 3D crystal structure of a molecule. Molecular graphs were encoded using a graph attention mechanism to deal with the issues of the inconsistencies of local substructures contributing to the entire molecular structure. The root-mean-square deviation (RMSDs) of approximately 80% dMXP predicted structures and the native binding poses within receptors are less than 2.0 Å.

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel quantitative structure-activity relationship (QSAR) method to predict a parameter more precisely from an incomplete data set via optimizing data handling by making use of predicted explanatory variables. As a case study we focused on the tissue-to-plasma partition coefficient (Kp), which is an important parameter for understanding drug distribution in tissues and building the physiologically based pharmacokinetic model and is a representative of small and sparse data sets. In this study, we predicted the Kp values of 119 compounds in nine tissues (adipose, brain, gut, heart, kidney, liver, lung, muscle, and skin), although some of these were not available. To fill the missing values in Kp for each tissue, first we predicted those Kp values by the nonmissing data set using a random forest (RF) model with in vitro parameters (log P, fu, Drug Class, and fi) like a classical prediction by a QSAR model. Next, to predict the tissue-specific Kp values in a test data set, we constructed a second RF model with not only in vitro parameters but also the Kp values of other tissues (i.e., other than target tissues) predicted by the first RF model as explanatory variables. Furthermore, we tested all possible combinations of explanatory variables and selected the model with the highest predictability from the test data set as the final model. The evaluation of Kp prediction accuracy based on the root-mean-square error and R 2 value revealed that the proposed models outperformed other machine learning methods such as the conventional RF and message-passing neural networks. Significant improvements were observed in the Kp values of adipose tissue, brain, kidney, liver, and skin. These improvements indicated that the Kp information on other tissues can be used to predict the same for a specific tissue. Additionally, we found a novel relationship between each tissue by evaluating all combinations of explanatory variables. In conclusion, we developed a novel RF model to predict Kp values. We hope that this method will be applied to various problems in the field of experimental biology which often contains missing values in the near future.

The elevated activity of leucine-rich repeat kinase 2 (LRRK2) is implicated in the pathogenesis of Parkinson's disease (PD). The quest for effective LRRK2 inhibitors has been impeded by the formidable challenge of crossing the blood-brain barrier (BBB). We leveraged structure-based de novo design and developed robust three-dimensional quantitative structure-activity relationship (3D-QSAR) models to predict BBB permeability, enhancing the likelihood of the inhibitor's brain accessibility. Our strategy involved the synthesis of macrocyclic molecules by linking the two terminal nitrogen atoms of HG-10-102-01 with an alkyl chain ranging from 2 to 4 units, laying the groundwork for innovative LRRK2 inhibitor designs. Through meticulous computational and synthetic optimization of both biochemical efficacy and BBB permeability, 9 out of 14 synthesized candidates demonstrated potent low-nanomolar inhibition and significant BBB penetration. Further assessments of in vitro and in vivo effectiveness, coupled with pharmacological profiling, highlighted 8 as the promising new lead compound for PD therapeutics.

The unsatisfactory effects of conventional bactericides and antimicrobial resistance have increased the challenges in managing plant diseases caused by bacterial pests. Here, we report the successful design and synthesis of benzofuran derivatives using benzofuran as the core skeleton and splicing the disulfide moieties commonly seen in natural substances with antibacterial properties. Most of our developed benzofurans displayed remarkable antibacterial activities to frequently encountered pathogens, including Xanthomonas oryzae pv oryzae ( Xoo ), Xanthomonas oryzae pv oryzicola ( Xoc ), and Xanthomonas axonopodis pv citri ( Xac ). With the assistance of the three-dimensional quantitative constitutive relationship (3D-QSAR) model, the optimal compound V40 was obtained, which has better in vitro antibacterial activity with EC 50 values of 0.28, 0.56, and 10.43 μg/mL against Xoo , Xoc , and Xac , respectively, than those of positive control, TC (66.41, 78.49, and 120.36 μg/mL) and allicin (8.40, 28.22, and 88.04 μg/mL). Combining the results of proteomic analysis and enzyme activity assay allows the antibacterial mechanism of V40 to be preliminarily revealed, suggesting its potential as a versatile bactericide in combating bacterial pests in the future.

Enantioselective antibodies have emerged as efficient tools in the field of chiral chemical detection and separation. However, it is complicated to obtain a highly stereoselective antibody due to the unclear recognition mechanism. In this study, the hapten of metolachlor was synthesized and enantio-separated. The absolute configuration of the four haptens obtained was identified by the computed and experimental electronic circular dichroism comparison. Five polyclonal antibodies against the Rac-metolachlor and its enantiomers were generated by immunization. The cross-activity of all the 5 antibodies with 44 structural analogues, including metolachlor enantiomers, was tested. It demonstrated that antibodies have higher specificity to recognize central chirality than axial chirality. Especially, αRR-MET-Ab exhibited excellent specificity and stereoselectivity. Accordingly, 3D-QSAR models were constructed and revealed that paired stereoisomers exhibited opposite interactions with the antibodies. It is the first time that the antibodies against four stereoisomers were prepared and analyzed, which will be conducive to the rational design of the stereoselective antibodies.

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

Half-life is a significant pharmacokinetic parameter included in the excretion phase of absorption, distribution, metabolism, and excretion. It is one of the key factors for the successful marketing of drug candidates. Therefore, predicting half-life is of great significance in drug design. In this study, we emplo yed eXtreme Gradient Boosting (XGboost), randomForest (RF), gradient boosting machine (GBM), and supporting vector machine (SVM) to build quantitative structure-activity relationship (QSAR) models on 3512 compounds and evaluated model performance by using root-mean-square error (RMSE), R 2 , and mean absolute error (MAE) metrics and interpreted features by SHapley Additive exPlanation (SHAP). Furthermore, we developed consensus models through integrating four individual models and validated their performance using a Y-randomization test and applicability domain analysis. Finally, matched molecular pair analysis was used to extract the transformation rules. Our results revealed that XGboost outperformed other individual models (RMSE = 0.176, R 2 = 0.845, MAE = 0.141). The consensus model integrating all four models continued to enhance prediction performance (RMSE = 0.172, R 2 = 0.856, MAE = 0.138). We evaluated the reliability, robustness, and generalization ability via Y-randomization test and applicability domain analysis. Meanwhile, we utilized SHAP to interpret features and employed matched molecular pair analysis to extract chemical transformation rules that provide suggestions for optimizing drug structure. In conclusion, we believe that the consensus model developed in this study serve as a reliable tool to evaluate half-life in drug discovery, and the chemical transformation rules concluded in this study could provide valuable suggestions in drug discovery.

To increase the structural diversity of insecticides and meet the needs of effective integrated insect management, the structure of chlorantraniliprole was modified based on a previously established three-dimensional quantitative structure-activity relationship (3D-QSAR) model. The pyridinyl moiety in the structure of chlorantraniliprole was replaced with a 4-fluorophenyl group. Further modifications of this 4-fluorophenyl group by introducing a halogen atom at position 2 and an electron-withdrawing group (e.g., iodine, cyano, and trifluoromethyl) at position 5 led to 34 compounds with good insecticidal efficacy against Mythimna separata , Plutella xylostella , and Spodoptera frugiperda . Among them, compound IV f against M. separata showed potency comparable to that of chlorantraniliprole. IV p against P. xylostella displayed a 4.5 times higher potency than chlorantraniliprole. In addition, IV d and chlorantraniliprole exhibited comparable potencies against S. frugiperda . Transcriptome analysis showed that the molecular target of compound IV f is the ryanodine receptor. Molecular docking was further performed to verify the mode of action and insecticidal activity against resistant P. xylostella .

Self-supervised pretrained models are gaining increasingly more popularity in AI-aided drug discovery, leading to more and more pretrained models with the promise that they can extract better feature representations for molecules. Yet, the quality of learned representations has not been fully explored. In this work, inspired by the two phenomena of Activity Cliffs (ACs) and Scaffold Hopping (SH) in traditional Quantitative Structure-Activity Relationship analysis, we propose a method named Re presentation- P roperty R elationship A nalysis (RePRA) to evaluate the quality of the representations extracted by the pretrained model and visualize the relationship between the representations and properties. The concepts of ACs and SH are generalized from the structure-activity context to the representation-property context, and the underlying principles of RePRA are analyzed theoretically. Two scores are designed to measure the generalized ACs and SH detected by RePRA, and therefore, the quality of representations can be evaluated. In experiments, representations of molecules from 10 target tasks generated by 7 pretrained models are analyzed. The results indicate that the state-of-the-art pretrained models can overcome some shortcomings of canonical Extended-Connectivity FingerPrints, while the correlation between the basis of the representation space and specific molecular substructures are not explicit. Thus, some representations could be even worse than the canonical fingerprints. Our method enables researchers to evaluate the quality of molecular representations generated by their proposed self-supervised pretrained models. And our findings can guide the community to develop better pretraining techniques to regularize the occurrence of ACs and SH.

Imputation machine learning (ML) surpasses traditional approaches in modeling toxicity data. The method was tested on an open-source data set comprising approximately 2500 ingredients with limited in vitro and in vivo data obtained from the OECD QSAR Toolbox. By leveraging the relationships between different toxicological end points, imputation extracts more valuable information from each data point compared to well-established single end point methods, such as ML-based Quantitative Structure Activity Relationship (QSAR) approaches, providing a final improvement of up to around 0.2 in the coefficient of determination. A significant aspect of this methodology is its resilience to the inclusion of extraneous chemical or experimental data. While additional data typically introduces a considerable level of noise and can hinder performance of single end point QSAR modeling, imputation models remain unaffected. This implies a reduction in the need for laborious manual preprocessing tasks such as feature selection, thereby making data preparation for ML analysis more efficient. This successful test, conducted on open-source data, validates the efficacy of imputation approaches in toxicity data analysis. This work opens the way for applying similar methods to other types of sparse toxicological data matrices, and so we discuss the development of regulatory authority guidelines to accept imputation models, a key aspect for the wider adoption of these methods.

AlvaBuilder is a software tool for de novo molecular design and can be used to generate novel molecules having desirable characteristics. Such characteristics can be defined using a simple step by step graphical interface, and they can be based on molecular descriptors, on predictions of QSAR/QSPR models, and on matching molecular fragments or used to design compounds similar to a given one. The molecules generated are always syntactically valid since they are composed by combining fragments of molecules taken from a training data set chosen by the user. In this paper, we demonstrate how the software can be used to design new compounds for a defined case study. AlvaBuilder is available at https://www.alvascience.com/alvabuilder/.

Optimizing the activities and properties of lead compounds is an essential step in the drug discovery process. Despite recent advances in machine learning-aided drug discovery, most of the existing methods focus on making predictions for the desired objectives directly while ignoring the explanations for predictions. Although several techniques can provide interpretations for machine learning-based methods such as feature attribution, there are still gaps between these interpretations and the principles commonly adopted by medicinal chemists when designing and optimizing molecules. Here, we propose an interpretation framework, named MolSHAP, for quantitative structure-activity relationship analysis by estimating the contributions of R-groups. Instead of attributing the activities to individual input features, MolSHAP regards the R-group fragments as the basic units of interpretation, which is in accordance with the fragment-based modifications in molecule optimization. MolSHAP is a model-agnostic method that can interpret activity regression models with arbitrary input formats and model architectures. Based on the evaluations of numerous representative activity regression models on a specially designed R-group ranking task, MolSHAP achieved significantly better interpretation power compared with other methods. In addition, we developed a compound optimization algorithm based on MolSHAP and illustrated the reliability of the optimized compounds using an independent case study. These results demonstrated that MolSHAP can provide a useful tool for accurately interpreting the quantitative structure-activity relationships and rationally optimizing the compound activities in drug discovery.

Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.

In molecular optimization, one popular way is R-group decoration on molecular scaffolds, and many efforts have been made to generate R-groups based on deep generative models. However, these methods mostly use information on known binding ligands, without fully utilizing target structure information. In this study, we proposed a new method, DiffDec, to involve 3D pocket constraints by a modified diffusion technique for optimizing molecules through molecular scaffold decoration. For end-to-end generation of R-groups with different sizes, we designed a novel fake atom mechanism. DiffDec was shown to be able to generate structure-aware R-groups with realistic geometric substructures by the analysis of bond angles and dihedral angles and simultaneously generate multiple R-groups for one scaffold on different growth anchors. The growth anchors could be provided by users or automatically determined by our model. DiffDec achieved R-group recovery rates of 69.67% and 45.34% in the single and multiple R-group decoration tasks, respectively, and these values were significantly higher than competing methods (37.33% and 26.85%). According to the molecular docking study, our decorated molecules obtained a better average binding affinity than baseline methods. The docking pose analysis revealed that DiffDec could decorate scaffolds with R-groups that exhibited improved binding affinities and more favorable interactions with the pocket. These results demonstrated the potential and applicability of DiffDec in real-world scaffold decoration for molecular optimization.

Machine learning-based predictive models allow rapid and reliable prediction of material properties and facilitate innovative materials design. Base oils used in the formulation of lubricant products are complex hydrocarbons of varying sizes and structure. This study developed Gaussian process regression-based models to accurately predict the temperature-dependent density and dynamic viscosity of 305 complex hydrocarbons. In our approach, strongly correlated/collinear predictors were trimmed, important predictors were selected by least absolute shrinkage and selection operator (LASSO) regularization and prior domain knowledge, hyperparameters were systematically optimized by Bayesian optimization, and the models were interpreted. The approach provided versatile and quantitative structure-property relationship (QSPR) models with relatively simple predictors for determining the dynamic viscosity and density of complex hydrocarbons at any temperature. In addition, we developed molecular dynamics simulation-based descriptors and evaluated the feasibility and versatility of dynamic descriptors from simulations for predicting the material properties. It was found that the models developed using a comparably smaller pool of dynamic descriptors performed similarly in predicting density and viscosity to models based on many more static descriptors. The best models were shown to predict density and dynamic viscosity with coefficient of determination ( R 2 ) values of 99.6% and 97.7%, respectively, for all data sets, including a test data set of 45 molecules. Finally, partial dependency plots (PDPs), individual conditional expectation (ICE) plots, local interpretable model-agnostic explanation (LIME) values, and trimmed model R 2 values were used to identify the most important static and dynamic predictors of the density and viscosity.

Federated multipartner machine learning has been touted as an appealing and efficient method to increase the effective training data volume and thereby the predictivity of models, particularly when the generation of training data is resource-intensive. In the landmark MELLODDY project, indeed, each of ten pharmaceutical companies realized aggregated improvements on its own classification or regression models through federated learning. To this end, they leveraged a novel implementation extending multitask learning across partners, on a platform audited for privacy and security. The experiments involved an unprecedented cross-pharma data set of 2.6+ billion confidential experimental activity data points, documenting 21+ million physical small molecules and 40+ thousand assays in on-target and secondary pharmacodynamics and pharmacokinetics. Appropriate complementary metrics were developed to evaluate the predictive performance in the federated setting. In addition to predictive performance increases in labeled space, the results point toward an extended applicability domain in federated learning. Increases in collective training data volume, including by means of auxiliary data resulting from single concentration high-throughput and imaging assays, continued to boost predictive performance, albeit with a saturating return. Markedly higher improvements were observed for the pharmacokinetics and safety panel assay-based task subsets.

Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that relate the molecular structure to compound properties. Such quantitative structure-property relationship models are generally trained on large data sets that include diverse chemical series (global models). In the pharmaceutical industry, these ML global models are available across discovery projects as an "out-of-the-box" solution to assist in drug design, synthesis prioritization, and experiment selection. However, drug discovery projects typically focus on confined parts of the chemical space (e.g., chemical series), where global models might not be applicable. Local ML models are sometimes generated to focus on specific projects or series. Herein, ML-based global models, local models, and hybrid global-local strategies were benchmarked. Analyses were done for more than 300 drug discovery projects at Novartis and ten absorption, distribution, metabolism, and excretion (ADME) assays. In this work, hybrid global-local strategies based on transfer learning approaches were proposed to leverage both historical ADME data (global) and project-specific data (local) to adapt model predictions. Fine-tuning a pretrained global ML model (used for weights' initialization, WI) was the top-performing method. Average improvements of mean absolute errors across all assays were 16% and 27% compared with global and local models, respectively. Interestingly, when the effect of training set size was analyzed, WI fine-tuning was found to be successful even in low-data scenarios (e.g., ∼10 molecules per project). Taken together, this work highlights the potential of domain adaptation in the field of molecular property predictions to refine existing pretrained models on a new compound data distribution.

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Flavonoids, ubiquitous natural products, provide sources for drug discovery owing to their structural diversity, broad-spectrum pharmacological activity, and excellent environmental compatibility. To develop antibacterial and antifungal agents with novel mechanisms of action and innovative structures, a series of novel 5-sulfonyl-1,3,4-thiadiazole-substituted flavonoids were designed and synthesized, and their biological activities against seven agriculturally common phytopathogenic microorganisms were evaluated. The results of the antimicrobial bioassay showed that most of the target compounds displayed excellent inhibitory effects against Xanthomonas oryzae , Rhizoctonia solani , and Colletotrichum orbiculare . Compounds 1 , 3 , 7 , 9 , 13 , and 14 exhibited remarkable antibacterial activity against X. oryzae pv. oryzae with EC 50 values below 10 μg/mL, which were superior to bismerthiazol (70.89 μg/mL). Compound 2 (EC 50 = 0.41 μg/mL) displayed the most effective inhibitory potency against R. solani in vivo , comparable protective effects with the positive control carbendizam. Preliminary mechanistic studies indicated that compound 2 induced disordered entanglement of hyphae, shrinkage of hyphal surfaces, extravasation of cellular contents, and vacuole swelling and rupture, which disrupted normal hyphal growth. Subsequently, compounds 35 with good antifungal activity were designed and synthesized based on reliable three-dimensional quantitative structure-activity relationship (3D-QSAR) models. Compound 53 with good antifungal activity were designed and synthesized based on reliable three-dimensional quantitative structure-activity relationship (3D-QSAR) models. Compound 49 showed high efficacy and superior antifungal activity against R. solani , with an EC 50 value of 0.28 μg/mL and a half-maximal effective concentration of 0.46 μg/mL.

Metal-free carbon material-mediated nonradical oxidation processes (C-NOPs) have emerged as a research hotspot due to their excellent performance in selectively eliminating organic pollutants in aqueous environments. However, the selective oxidation mechanisms of C-NOPs remain obscure due to the diversity of organic pollutants and nonradical active species. Herein, quantitative structure-activity relationship (QSAR) models were employed to unveil the origins of C-NOP selectivity toward organic pollutants in different oxidant systems. QSAR analysis based on adsorption and oxidation descriptors revealed that C-NOP selectivity depends on the oxidation potentials of organic pollutants rather than on adsorption interactions. However, the dominance of electronic effects in selective oxidation decreases with increasing structural complexity of organic pollutants. Moreover, the oxidation threshold solely depends on the inherent electronic nature of organic pollutants and not on the reactivity of nonradical active species. Notably, the accuracy of substituent descriptors (Hammett constants) and theoretical descriptors (e.g., highest occupied molecular orbital energy, ionization potential, and single-electron oxidation potential) is significantly influenced by the complexity and molecular state of organic pollutants. Overall, the study findings reveal the origins of organic pollutant-oriented selective oxidation and provide insight into the application of descriptors in QSAR analysis.

The heterogeneous photodegradation behavior of liquid crystal monomers (LCMs) in standard dust (standard reference material, SRM 2583) and environmental dust was investigated. The measured photodegradation ratios for 23 LCMs in SRM and environmental dust in 12 h were 11.1 ± 1.8 to 23.2 ± 1.1% and 8.7 ± 0.5 to 24.0 ± 2.8%, respectively. The degradation behavior of different LCM compounds varied depending on their structural properties. A quantitative structure-activity relationship model for predicting the degradation ratio of LCMs in SRM dust was established, which revealed that the molecular descriptors related to molecular polarizability, electronegativity, and molecular mass were closely associated with LCMs' photodegradation. The photodegradation products of the LCM compound 4'-propoxy-4-biphenylcarbonitrile (PBIPHCN) in dust, including • OH oxidation, C-O bond cleavage, and ring-opening products, were identified by nontarget analysis, and the corresponding degradation pathways were suggested. Some of the identified products, such as 4'-hydroxyethoxy-4-biphenylcarbonitrile, showed predicted toxicity (with an oral rat lethal dose of 50%) comparable to that of PBIPHCN. The half-lives of the studied LCMs in SRM dust were estimated at 32.2-82.5 h by fitting an exponential decay curve to the observed photodegradation data. The photodegradation mechanisms of LCMs in dust were revealed for the first time, enhancing the understanding of LCMs' environmental behavior and risks.

This comprehensive review explores the emerging landscape of Nano-QSAR (quantitative structure-activity relationship) for assessing the risk and potency of nanomaterials in agricultural settings. The paper begins with an introduction to Nano-QSAR, providing background and rationale, and explicitly states the hypotheses guiding the review. The study navigates through various dimensions of nanomaterial applications in agriculture, encompassing their diverse properties, types, and associated challenges. Delving into the principles of QSAR in nanotoxicology, this article elucidates its application in evaluating the safety of nanomaterials, while addressing the unique limitations posed by these materials. The narrative then transitions to the progression of Nano-QSAR in the context of agricultural nanomaterials, exemplified by insightful case studies that highlight both the strengths and the limitations inherent in this methodology. Emerging prospects and hurdles tied to Nano-QSAR in agriculture are rigorously examined, casting light on important pathways forward, existing constraints, and avenues for research enhancement. Culminating in a synthesis of key insights, the review underscores the significance of Nano-QSAR in shaping the future of nanoenabled agriculture. It provides strategic guidance to steer forthcoming research endeavors in this dynamic field.

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

Structural diversity derivatization from natural products is an important and effective method of discovering novel green pesticides. Cinnamic acids are abundant in plants, and their unparalleled structures endow them with various excellent biological activities. A series of novel cinnamic oxime esters were designed and synthesized to develop high antifungal agrochemicals. The antifungal activity, structure-activity relationship, and action mechanism were systematically studied. Compounds 7i , 7u , 7v , and 7x exhibited satisfactory activity against Gaeumannomyces graminis var. tritici , with inhibition rates of ≥90% at 50 μg/mL. Compounds 7z and 7n demonstrated excellent activities against Valsa mali and Botrytis cinerea , with median effective concentration (EC 50 ) values of 0.71 and 1.41 μg/mL, respectively. Compound 7z exhibited 100% protective and curative activities against apple Valsa canker at 200 μg/mL. The control effects of 7n against gray mold on tomato fruits and leaves were all >96%, exhibiting superior or similar effects to those of the commercial fungicide boscalid. Furthermore, the quantitative structure-activity relationship was established to guide the further design of higher-activity compounds. The preliminary results on the action mechanism revealed that 7n treatment could disrupt the function of the nucleus and mitochondria, leading to reactive oxygen species accumulation and cell membrane damage. Its primary biochemical mechanism may be inhibiting fungal ergosterol biosynthesis. The novel structure, simple synthesis, and excellent activity of cinnamic oxime esters render them promising potential fungicides.

Tissue-to-blood partition coefficients ( P tb ) are key parameters for assessing toxicokinetics of xenobiotics in organisms, yet their experimental data were lacking. Experimental methods for measuring P tb values are inefficient, underscoring the urgent need for prediction models. However, most existing models failed to fully exploit P tb data from diverse sources, and their applicability domain (AD) was limited. The current study developed a multimodal model capable of processing and integrating textual (categorical features) and numerical information (molecular descriptors/fingerprints) to simultaneously predict P tb values across various species, tissues, blood matrices, and measurement methods. Artificial neural network algorithms with embedding layers were used for the multimodal modeling. The corresponding unimodal models were developed for comparison. Results showed that the multimodal model outperformed unimodal models. To enhance the reliability of the model, a method considering categorical features, weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes was used to characterize the AD. The model constrained by the AD exhibited better prediction accuracy for the validation set, with the determination coefficient, root mean-square error, and mean absolute error being 0.843, 0.276, and 0.213 log units, respectively. The multimodal model coupled with the AD characterization can serve as an efficient tool for internal exposure assessment of chemicals.

To develop new antioomycete agents against plant pathogens, two series of acrylopimaric acid triazole derivatives from rosin were synthesized. The in vitro antioomycete activity of these derivatives was evaluated and screened against Pseudoperonospora cubensisi , Plasmopara viticola , Phytophthora sojae , Phytophthora infestans , and Phytophthora capsici . Compound 5m showed the highest antioomycete activity against P. capsici , with a half-maximal effective concentration (EC 50 ) value that was lower than that of the positive control metalaxyl (1.391 and 1.815 mg/L, respectively). Compound 5m demonstrated satisfactory protective and curative efficacy against P. capsici in pepper in in vivo antioomycete activity studies. Physiological and biochemical testing showed that the action mechanism of compound 5m on P. capsici involved altering the morphology and ultrastructure of the mycelium, increasing cell membrane permeability, inducing dysfunction of the nucleus and mitochondria, and ultimately causing cell necrosis. In addition, the analysis of three-dimensional quantitative structure-activity relationship (3D-QSAR) revealed the significance of the molecular structure and charge distribution in the interaction between compound 5m and its target. Collectively, these findings indicate that compound 5m has the potential as an antioomycete candidate.