ArticleACS omega2023
Latent Biases in Machine Learning Models for Predicting Binding Affinities Using Popular Data Sets.
Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- In Silico Isomerization Produces Apt Negative Data for VHTS Validation.Journal of chemical information and modeling · 2026Article
- Accurate and Generalizable Protein-Ligand Binding Affinity Prediction With Geometric Deep Learning.IEEE open journal of engineering in medicine and biology · 2026Article
- Challenging Reaction Prediction Models to Generalize to Novel Chemistry.ACS central science · 2025Article
- Sifting through the noise: A survey of diffusion probabilistic models and their applications to biomolecules.Journal of molecular biology · 2025Review
- G-Computational and structural biotechnology journal · 2024Article
- DrugGym: A testbed for the economics of autonomous drug discovery.bioRxiv : the preprint server for biology · 2024Article
- A new paradigm for applying deep learning to protein-ligand interaction prediction.Briefings in bioinformatics · 2024Article
- PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications.Scientific data · 2024Article
- Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning.RSC advances · 2024Article
- Poor Generalization by Current Deep Learning Models for Predicting Binding Affinities of Kinase Inhibitors.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug design involves the process of identifying and designing molecules that bind well to a given receptor. A vital computational component of this process is the protein-ligand interaction scoring functions that evaluate the binding ability of various molecules or ligands with a given protein receptor binding pocket reasonably accurately. With the publicly available protein-ligand binding affinity data sets in both sequential and structural forms, machine learning methods have gained traction as a top choice for developing such scoring functions. While the performance shown by these models is optimistic, there are several hidden biases present in these data sets themselves that affect the utility of such models for practical purposes such as virtual screening. In this work, we use published methods to systematically investigate several such factors or biases present in these data sets. In our analysis, we highlight the importance of considering sequence, protein-ligand interaction, and pocket structure similarity while constructing data splits and provide an explanation for good protein-only and ligand-only performances in some data sets. Through this study, we provide to the community several pointers for the design of binding affinity predictors and data sets for reliable applicability.
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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.