ArticleBioinformatics (Oxford, England)2025
Robustly interrogating machine learning-based scoring functions: what are they learning?
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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Who cites it
14 citing papers in PubMed.
- More protein-ligand data are needed for AlphaFold-like models to enable drug discovery.Current opinion in structural biology · 2026Review
- Integrative Computational Chemistry Approaches in Modern Drug Discovery: Advances in Docking, Pharmacophore Modeling, Molecular Dynamics, and Virtual Screening.Pharmaceutics · 2026Review
- Reindeer: a protein-ligand feature generator software for machine learning algorithms.BMC bioinformatics · 2026Article
- Prediction of Protein-Ligand Binding Affinities Using Atomic Surface Site Interaction Points.Journal of chemical information and modeling · 2026Article
- Article
- Uncertainty quantification enables reliable deep learning for protein-ligand binding affinity prediction.Scientific reports · 2025Article
- Incorporating targeted protein structure in deep learning methods for molecule generation in computational drug design.Chemical science · 2025Review
- All That Glitters Is Not Gold: Importance of Rigorous Evaluation of Proteochemometric Models.Journal of chemical information and modeling · 2025Article
- Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions.Nature communications · 2025Article
- MolSnapper: Conditioning Diffusion for Structure-Based Drug Design.Journal of chemical information and modeling · 2025Article
- T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity.Communications biology · 2025Article
- Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data.Communications chemistry · 2025Article
- The algebraic extended atom-type graph-based model for precise ligand-receptor binding affinity prediction.Journal of cheminformatics · 2025Article
- Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures.Journal of cheminformatics · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationMachine learning-based scoring functions (MLBSFs) have been found to exhibit inconsistent performance on different benchmarks and be prone to learning dataset bias. For the field to develop MLBSFs that learn a generalizable understanding of physics, a more rigorous understanding of how they perform is required.
resultsIn this work, we compared the performance of a diverse set of popular MLBSFs (RFScore, SIGN, OnionNet-2, Pafnucy, and PointVS) to our proposed baseline models that can only learn dataset biases on a range of benchmarks. We found that these baseline models were competitive in accuracy to these MLBSFs in almost all proposed benchmarks, indicating these models only learn dataset biases. Our tests and provided platform, ToolBoxSF, will enable researchers to robustly interrogate MLBSF performance and determine the effect of dataset biases on their predictions. AVAILABILITY AND IMPLEMENTATION: https://github.com/guydurant/toolboxsf.
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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.