Model agnostic generation of counterfactual explanations for molecules

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Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

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Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation

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Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES

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Explainability in Graph Neural Networks: A Taxonomic SurveyPreprint

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Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?

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JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular DesignPreprint

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Explaining Deep Graph Networks with Molecular CounterfactualsPreprint

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Interpretable and Explainable Machine Learning for Materials Science and Chemistry

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Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

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SELFIES and the future of molecular string representations

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AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery

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Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review

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Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review

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Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World

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Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations

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Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning

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A Perspective on Explanations of Molecular Prediction Models

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Graph Neural Networks in Modern AI-Aided Drug Discovery

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Recent advances in AI-based toxicity prediction for drug discovery

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Invalid SMILES are beneficial rather than detrimental to chemical language models

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Studying Noncovalent Interactions in Molecular Systems with Machine Learning

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Open-Source Machine Learning in Computational Chemistry

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Differences in learning characteristics between support vector machine and random forest models for compound classification revealed by Shapley value analysis

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Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models

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Recent advances in the self-referencing embedded strings (SELFIES) library

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Explaining compound activity predictions with a substructure-aware loss for graph neural networks

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Counterfactual Learning on Graphs: A Survey

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Explainable artificial intelligence for molecular design in pharmaceutical research

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Active Learning of Ligands That Enhance Perovskite Nanocrystal Luminescence

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SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability

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Modeling bioconcentration factors in fish with explainable deep learning

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Generation of Molecular Counterfactuals for Explainable Machine Learning Based on Core‐Substituent Recombination

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Machine Learning-Based Discovery of Molecular Descriptors that Control Polymer Gas Permeation

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Explainable artificial intelligence for materials discovery: application to catalysts for the HER and ORR

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Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence

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Explaining the Chemical Inventory of Orion KL through Machine Learning

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Do machines dream of atoms? A quantitative molecular benchmark for explainable AI heatmapsPreprint

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Explaining Multiclass Compound Activity Predictions Using Counterfactuals and Shapley Values

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Do machines dream of atoms? Crippen's logP as a quantitative molecular benchmark for explainable AI heatmaps

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Game-theoretic Counterfactual Explanation for Graph Neural Networks

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An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors

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Investigating the Lack of Translation from Cruzain Inhibition to Trypanosoma cruzi Activity with Machine Learning and Chemical Space Analyses

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Active causal learning for decoding chemical complexities with targeted interventions

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Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

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Mining Pareto-optimal counterfactual antecedents with a branch-and-bound model-agnostic algorithm

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General-Purpose Models for the Chemical Sciences: LLMs and Beyond

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FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability

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Decoding polyphenol-protein interactions with deep learning: From molecular mechanisms to food applications.

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ACES-GNN: can graph neural network learn to explain activity cliffs?

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Contrastive explanations for machine learning predictions in chemistry

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Evaluating machine learning models for predicting pesticide toxicity to honey bees

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Intrinsically Interpretable Gaussian Mixture Model

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Molecular machine learning in chemical process design

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Robust Stochastic Graph Generator for Counterfactual Explanations

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Rationalizing Predictions of Isoform-Selective Phosphoinositide 3-Kinase Inhibitors Using MolAnchor Analysis

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Explaining structure-activity relationships using locally faithful surrogate modelsPreprint

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A Unified System for Molecular Property Predictions: Oloren ChemEngine and its ApplicationsPreprint

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Explainable prediction of catalysing enzymes from reactions using multilayer perceptronsPreprint

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Multiscale-Aware Graph Embedding Approach Uncovers LC-61, a Potent Anti- Leishmania infantum Compound

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Recent advances in the Self-Referencing Embedding Strings (SELFIES) library

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Why does that molecule smell?Preprint

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Applications of machine learning in quantitative structure-activity relationships (QSAR)

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Interpretable machine learning rationalizes carbonic anhydrase inhibition via conformal and counterfactual prediction

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Trustworthy Insights: A Novel Multi-Tier Explainable Framework for Ambient Assisted Living

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Explaining structure-activity relationships using locally faithful surrogate modelsPreprint

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Ground truth explanation dataset for chemical property prediction on molecular graphsPreprint

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Counterfactual Explanations for Graph Classification Through the Lenses of Density

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MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks

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Generation of Molecular Counterfactuals for Explainable Machine Learning Based on Core‐Substituent Recombination

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Enhancing Chemical Explainability Through Counterfactual Masking

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Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning

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