ArticleProceedings of the National Academy of Sciences of the United States of America2016
Predicting protein-ligand affinity with a random matrix framework.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Biopesticidal Potential of theInternational journal of molecular sciences · 2026Article
- Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach.Journal of chemical information and modeling · 2024Article
- Improving the generalizability of protein-ligand binding predictions with AI-Bind.Nature communications · 2023Article
- Machine learning for chemical discovery.Nature communications · 2020Article
- When two are better than one: Modeling the mechanisms of antibody mixtures.PLoS computational biology · 2020Article
- DeltaDelta neural networks for lead optimization of small molecule potency.Chemical science · 2019Article
- AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening.Journal of chemical information and modeling · 2019Article
- Using attribution to decode binding mechanism in neural network models for chemistry.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Ligand biological activity predicted by cleaning positive and negative chemical correlations.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- On identifying collective displacements in apo-proteins that reveal eventual binding pathways.PLoS computational biology · 2019Article
- Visualizing convolutional neural network protein-ligand scoring.Journal of molecular graphics & modelling · 2018Article
- Machine learning unifies the modeling of materials and molecules.Science advances · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapid determination of whether a candidate compound will bind to a particular target receptor remains a stumbling block in drug discovery. We use an approach inspired by random matrix theory to decompose the known ligand set of a target in terms of orthogonal "signals" of salient chemical features, and distinguish these from the much larger set of ligand chemical features that are not relevant for binding to that particular target receptor. After removing the noise caused by finite sampling, we show that the similarity of an unknown ligand to the remaining, cleaned chemical features is a robust predictor of ligand-target affinity, performing as well or better than any algorithm in the published literature. We interpret our algorithm as deriving a model for the binding energy between a target receptor and the set of known ligands, where the underlying binding energy model is related to the classic Ising model in statistical physics.
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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.