ArticleJournal of cheminformatics2021
Benchmarks for interpretation of QSAR models.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed.
- Beyond the Score: Fixed-Budget Benchmarking of Virtual Screening Integration Strategies for Decision-Centric Drug Discovery.International journal of molecular sciences · 2026Article
- Systematic validation of graph neural network explanations against adverse outcome pathway reactive centers for skin sensitization.Journal of cheminformatics · 2026Article
- Projection-based molecular feature maps for CNN-driven nephrotoxicity prediction.Archives of toxicology · 2026Article
- Cheminformatics and Machine Learning-Driven QSAR Analysis of SPHK2 Inhibitors for Anticancer Drug Design.Current medicinal chemistry · 2026Article
- Computational Evaluation of Potential c-Abl Kinase Inhibitors for Parkinson's Disease: QSAR, Docking, Bioisosteric Replacement, ADMET, and MD Simulations.Applied biochemistry and biotechnology · 2025Article
- Review
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- A Review of In Silico and In Vitro Approaches in the Fight Against Carbapenem-Resistant Enterobacterales.Journal of clinical laboratory analysis · 2025Review
- One size does not fit all: revising traditional paradigms for assessing accuracy of QSAR models used for virtual screening.Journal of cheminformatics · 2025Article
- Interpretable Deep-Learning pJournal of chemical information and modeling · 2025Article
- A systematic review of data and models for predicting food flavor and texture.Current research in food science · 2025Review
- Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features.Journal of chemical information and modeling · 2024Article
- Prediction and Interpretability of Melting Points of Ionic Liquids Using Graph Neural Networks.ACS omega · 2024Article
- De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Article
- Structure-based discovery of novel P-glycoprotein inhibitors targeting the nucleotide binding domains.Scientific reports · 2023Article
- Calculation of exact Shapley values for explaining support vector machine models using the radial basis function kernel.Scientific reports · 2023Article
- Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding.Journal of cheminformatics · 2023Article
- Explaining compound activity predictions with a substructure-aware loss for graph neural networks.Journal of cheminformatics · 2023Article
- Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Interpretation of QSAR models is useful to understand the complex nature of biological or physicochemical processes, guide structural optimization or perform knowledge-based validation of QSAR models. Highly predictive models are usually complex and their interpretation is non-trivial. This is particularly true for modern neural networks. Various approaches to interpretation of these models exist. However, it is difficult to evaluate and compare performance and applicability of these ever-emerging methods. Herein, we developed several benchmark data sets with end-points determined by pre-defined patterns. These data sets are purposed for evaluation of the ability of interpretation approaches to retrieve these patterns. They represent tasks with different complexity levels: from simple atom-based additive properties to pharmacophore hypothesis. We proposed several quantitative metrics of interpretation performance. Applicability of benchmarks and metrics was demonstrated on a set of conventional models and end-to-end graph convolutional neural networks, interpreted by the previously suggested universal ML-agnostic approach for structural interpretation. We anticipate these benchmarks to be useful in evaluation of new interpretation approaches and investigation of decision making of complex "black box" models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.