ArticleInternational journal of molecular sciences2020
Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Interpretable machine learning-driven QSAR modeling for coagulation factor X inhibitors: from molecular descriptors to predictive potency.Journal of computer-aided molecular design · 2026Article
- Artificial Intelligence and Machine Learning-Based Approaches for Genetic Damage Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Conditional Variational AutoEncoder to Predict Suitable Conditions for Hydrogenation Reactions.Molecules (Basel, Switzerland) · 2025Article
- Molecular Determinants of Per- and Polyfluoroalkyl Substances Binding to Estrogen Receptors.Toxics · 2025Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- General structure-activity relationship models for the inhibitors of Adenosine receptors: A machine learning approach.Molecular diversity · 2025Article
- Dynamic QSAR modeling for predicting in vivo genotoxicity and inflammation induced by nanoparticles and advanced materials: a time-dose-property/response approach.Journal of nanobiotechnology · 2025Article
- A data-driven generative strategy to avoid reward hacking in multi-objective molecular design.Nature communications · 2025Article
- Article
- In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation.Computational and structural biotechnology journal · 2024Article
- Advancements in biotransformation pathway prediction: enhancements, datasets, and novel functionalities in enviPath.Journal of cheminformatics · 2024Article
- An In Silico Study Based on QSAR and Molecular Docking and Molecular Dynamics Simulation for the Discovery of Novel Potent Inhibitor against AChE.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Article
- QSPR analysis of distance-based structural indices for drug compounds in tuberculosis treatment.Heliyon · 2024Article
- Dataset Design for Building Models of Chemical Reactivity.ACS central science · 2023Review
- Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction.The journal of physical chemistry. B · 2023Article
- 6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling.BMC chemistry · 2023Article
- Evaluating the utility of a high throughput thiol-containing fluorescent probe to screen for reactivity: A case study with the Tox21 library.Computational toxicology (Amsterdam, Netherlands) · 2023Article
- Improvement of multi-task learning by data enrichment: application for drug discovery.Journal of computer-aided molecular design · 2023Article
- Article
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6 authors.
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Abstract
Nowadays, the problem of the model's applicability domain (AD) definition is an active research topic in chemoinformatics. Although many various AD definitions for the models predicting properties of molecules (Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) models) were described in the literature, no one for chemical reactions (Quantitative Reaction-Property Relationships (QRPR)) has been reported to date. The point is that a chemical reaction is a much more complex object than an individual molecule, and its yield, thermodynamic and kinetic characteristics depend not only on the structures of reactants and products but also on experimental conditions. The QRPR models' performance largely depends on the way that chemical transformation is encoded. In this study, various AD definition methods extensively used in QSAR/QSPR studies of individual molecules, as well as several novel approaches suggested in this work for reactions, were benchmarked on several reaction datasets. The ability to exclude wrong reaction types, increase coverage, improve the model performance and detect Y-outliers were tested. As a result, several "best" AD definitions for the QRPR models predicting reaction characteristics have been revealed and tested on a previously published external dataset with a clear AD definition problem.
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