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RESEARCH PAPER · Journal of Chemical Information and Modeling · 2017

Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future

Павел Геннадьевич Полищук

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Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and FutureOriginalQuantitative structure–activity relationships for primary aerobic biodegradation of organic chemicals in pristine surface waters: starting points for predicting biodegradation under acclimatization30Predictive and mechanistic multivariate linear regression models for reaction development404QSAR: What Else?22e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods64Thin-layer chromatography in quantitative structure-activity relationship studies9Photoredox‐Catalyzed Decarboxylative Oxidation of Arylacetic Acids50The Current Limits in Virtual Screening and Property Prediction24Multiple quantitative structure-activity relationships (QSARs) analysis for orally active trypanocidal N-myristoyltransferase inhibitors33Optimal Piecewise Linear Regression Algorithm for QSAR Modelling24Comparing quantitative prediction methods for the discovery of small-molecule chiral catalysts146Deep learning for predicting toxicity of chemicals: a mini review84Finding the structural requirements of diverse HIV-1 protease inhibitors using multiple QSAR modelling for lead identification30Interpretation of QSAR Models: Mining Structural Patterns Taking into Account Molecular Context11Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes44In-Silico Extraction of Design Ideas Using MMPA-by-QSAR and its Application on ADME Endpoints12QSAR investigation of acute toxicity of organic compounds during oral administration to mice9Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets222Development and design of novel cardiovascular therapeutics based on Rho kinase inhibition—In silico approach13Predicting Blood–Brain Partitioning of Small Molecules Using a Novel Minimalistic Descriptor-Based Approach via the 3D-RISM-KH Molecular Solvation Theory13Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?83In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening321Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis47From machine learning to deep learning: Advances in scoring functions for protein–ligand docking244Contributions of Computer-Based Chemical Modeling Technologies on the Risk Assessment and the Environmental Fate Study of (Nano)pesticides7Time-Domain Analysis of Molecular Dynamics Trajectories Using Deep Neural Networks: Application to Activity Ranking of Tankyrase Inhibitors30Effect of pectin on the interactions among phenolic compounds determined by antioxidant capacity84Artificial Neural Network (ANN) Modeling of Odor Threshold Property of Diverse Chemical Constituents of Black Tea and Coffee0Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values512Neural-based approaches to overcome feature selection and applicability domain in drug-related property prediction23Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonome Entdeckung in den chemischen Wissenschaften, Teil II: Ausblick4Autonomous Discovery in the Chemical Sciences Part I: Progress317Autonomous Discovery in the Chemical Sciences Part II: Outlook245Toward Interpretable Machine Learning Models for Materials Discovery42A Random Forest Model to Predict the Activity of a Large Set of Soluble Epoxide Hydrolase Inhibitors Solely Based on a Set of Simple Fragmental Descriptors12Identification of prodigious and under-privileged structural features for RG7834 analogs as Hepatitis B virus expression inhibitor4A Comprehensive QSAR Study on Antileishmanial and Antitrypanosomal Cinnamate Ester Analogues14Exploration of histone deacetylase 8 inhibitors through classification QSAR study: Part II15Essential elements regulating HDAC8 inhibition: a classification based structural analysis and enzyme-inhibitor interaction study of hydroxamate based HDAC8 inhibitors12Prospects for Discovering the Secondary Metabolites of Cordyceps Sensu Lato by the Integrated Strategy14Exploring indole derivatives as myeloid cell leukaemia-1 (Mcl-1) inhibitors with multi-QSAR approach: a novel hope in anti-cancer drug discovery8QSAR without borders878The Cross-Interpretation of QSAR Toxicological Models2Quantum chemical predictions of water–octanol partition coefficients applied to the SAMPL6 logP blind challenge16QSAR studies on benzothiophene derivatives as Plasmodium falciparum N‐myristoyltransferase inhibitors: Molecular insights into affinity and selectivity12Discriminations of active from inactive HDAC8 inhibitors Part II: Bayesian classification study to find molecular fingerprints7A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility163VISAR: an interactive tool for dissecting chemical features learned by deep neural network QSAR models12In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways169Discriminant models on mitochondrial toxicity improved by consensus modeling and resolving imbalance in training31Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions708Learning Molecular Representations for Medicinal Chemistry222Machine Learning in Chemistry77Chemical-informatics approach to COVID-19 drug discovery: Monte Carlo based QSAR, virtual screening and molecular docking study of some in-house molecules as papain-like protease (PLpro) inhibitors108Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna8Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors65Quantitative structure‐activity relationships (QSAR) of aroma compounds in different aged Huangjiu25The Influence of Structural Patterns on Acute Aquatic Toxicity of Organic Compounds20Development of classification models for predicting inhibition of mitochondrial fusion and fission using machine learning methods19Deep Learning Approaches in Predicting ADMET Properties94Dipole Moment Variation Clears Up Electronic Excitations in the π‑Stacked Complexes of Fluorescent Protein Chromophores8Challenges and Opportunities for Integrating In Silico Models and Adverse Outcomes Pathways to Set and Relate New Biomarkers8Evaluation of the performance of various machine learning methods on the discrimination of the active compounds6Additional file 1 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models0Additional file 2 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models0First structure–activity relationship analysis of SARS-CoV-2 virus main protease (Mpro) inhibitors: an endeavor on COVID-19 drug discovery53Application of Quantitative Structure–Property Relationship Predictive Models to Water Treatment: A Critical Review36Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish21Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models656Practical constraints with machine learning in drug discovery7Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment96Virtual Screening and Molecular Design of Potential SARS-COV-2 Inhibitors2Benchmarks for interpretation of QSAR models7Exploring naphthyl derivatives as SARS-CoV papain-like protease (PLpro) inhibitors and its implications in COVID-19 drug discovery13Development of QSAR ensemble models for predicting the PAMPA Effective Permeability of new, non-peptidic leads with potential antiviral activity against the coronavirus SARS-CoV-2.2Anti‑inflammatory and anti‑rheumatic activities in vitro of alkaloids separated from Aconitum soongoricum Stapf38A DFT approach on tioguanine: Exploring tio-tiol tautomers, frontier molecular orbitals, IR and UV spectra, and quadrupole coupling constants24Benchmarks for interpretation of QSAR models87QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction51QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis13Relationships between Changes in Guest Ion Properties and in the Host Framework Topology in Ionic Coordination Polymers2Multiple Conformer Descriptors for QSAR Modeling18Structure-based design and classifications of small molecules regulating the circadian rhythm period38Artificial Neural Network (ANN) Modeling of Odor Threshold Property of Diverse Chemical Constituents of Black Tea and Coffee0Artificial intelligence in drug discovery: applications and techniques220How can SHAP values help to shape metabolic stability of chemical compounds?112Chemistry-centric explanation of machine learning models15From Data to Models2Ligand-based drug designing16Deep Neural Networks for QSAR37TargetTox: A Feature Selection Pipeline for Identifying Predictive Targets Associated with Drug Toxicity8Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion5BeeToxAI: An artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees48An Overview of In Silico Drug Discovery with Emphasis on Cancer0Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives11Explainable Machine Learning for Property Predictions in Compound Optimization86An Overview of In Silico Drug Discovery with Emphasis on Cancer1Computational Medicinal Chemistry to Target GPCRs3Integrative Analysis of Functional Genomic Screening and Clinical Data Identifies a Protective Role for Spironolactone in Severe COVID-195QSAR Methods11A detailed kinetic study on the tautomerization reactions of barbituric acid: A combined DFT-QTAIM analysis2Interpretation of Structure–Activity Relationships in Real-World Drug Design Data Sets Using Explainable Artificial Intelligence66Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability93Exploring the Prominent and Concealed Inhibitory Features for Cytoplasmic Isoforms of Hsp90 Using QSAR Analysis7Do machines dream of atoms? A quantitative molecular benchmark for explainable AI heatmaps13Application of Bioinformatics Techniques to Screen and Characterize the Plant-Based Anti-Cancer Compounds3Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery259In Silico Structure-Based Approach for Group Efficiency Estimation in Fragment-Based Drug Design Using Evaluation of Fragment Contributions14Targeting Lipid Rafts as a Rapid Screening Strategy for Potential Antiadipogenic Polyphenols along with the Structure–Activity Relationship and Mechanism Elucidation12Optimized Anticorrosion of Polypyrrole Coating by Inverted-Electrode Strategy: Experimental and Molecular Dynamics Investigations24ChemInformatics Model Explorer (CIME): exploratory analysis of chemical model explanations35Heuristics and Uncertainty Quantification in Rational and Inverse Compound and Catalyst Design3Beyond Woodward–Fieser Rules: Design Principles of Property-Oriented Chromophores Based on Explainable Deep Learning Optical Spectroscopy20Development of Predictive Classification Models for Whole Cell Antimycobacterial Activity of Benzothiazinones13Explaining structure-activity relationships using locally faithful surrogate models2Explaining structure-activity relationships using locally faithful surrogate models5Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks0Direct Prediction of Physicochemical Properties and Toxicities of Chemicals from Analytical Descriptors by GC–MS16Mechanistic Analysis of Chemically Diverse Bromodomain-4 Inhibitors Using Balanced QSAR Analysis and Supported by X-ray Resolved Crystal Structures4Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods14Perceiving the Concealed and Unreported Pharmacophoric Features of the 5-Hydroxytryptamine Receptor Using Balanced QSAR Analysis7A Review of Computational Methods in Predicting hERG Channel Blockers10Knowledge-guided deep learning models of drug toxicity improve interpretation41Explaining molecular properties with natural language25KNOWLEDGE GRAPH AIDS COMPREHENSIVE EXPLANATION OF DRUG TOXICITY0Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement28Symbolic regression for the interpretation of quantitative structure-property relationships9In silico study of natural antioxidants10Setting limits for N-nitrosamines in drugs: A defined approach based on read-across and structure-activity relationship for N-nitrosopiperazine impurities8Identification of Novel 5-Lipoxygenase-Activating Protein (FLAP) Inhibitors by an Integrated Method of Pharmacophore Virtual Screening, Docking, QSAR and ADMET Analyses14The (Re)-Evolution of Quantitative Structure–Activity Relationship (QSAR) Studies Propelled by the Surge of Machine Learning Methods131Interpretation of multi-task clearance models from molecular images supported by experimental design4DFT investigation of a Zn-doped carbon nanocone for the drug delivery of methylated aspirins13Human health risk assessment for contaminated sites: A retrospective review89Big data and deep learning: extracting and revising chemical knowledge from data1Computer-aided drug design-based system pharmacology applications for the treatment of diabetes mellitus1Identification of Structural Alerts by Machine Learning and Their Applications in Toxicology1Machine Learning-Based QSAR Models and Structural Alerts for Prediction of Mitochondrial Dysfunction1PredPS: Attention-based graph neural network for predicting stability of compounds in human plasma10Quantitative Structure Activity Relationship Methods for the Prediction of Ahr Binding Affinity of Organic Pollutants0Quantum similarity description of a unique classical and quantum QSPR algorithm in molecular spaces: the connection with Boolean hypercubes, algorithmic intelligence, and Gödel's incompleteness theorems1XSMILES: interactive visualization for molecules, SMILES and XAI attribution scores37Investigating the Lack of Translation from Cruzain Inhibition to Trypanosoma cruzi Activity with Machine Learning and Chemical Space Analyses8Establishment of extensive artificial intelligence models for kinase inhibitor prediction: Identification of novel PDGFRB inhibitors12Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking191Occurrence and risk assessment of organophosphate esters in global aquatic products79Predicting the Life of the Solder Joints in Electronic Assemblies using Physics-informed Data-driven Methodology12Prediction of IDO1 Inhibitors by a Fingerprint‐Based Stacking Ensemble Model Named IDO1Stack5Do machines dream of atoms? Crippen's logP as a quantitative molecular benchmark for explainable AI heatmaps11Over-Pressured Layer Chromatography and QSARs in predicting pharmacokinetics and toxicity of new herbicide candidates0

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  42. QSAR without borders

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  43. The Cross-Interpretation of QSAR Toxicological Models

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  48. VISAR: an interactive tool for dissecting chemical features learned by deep neural network QSAR models

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  49. In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

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