ArticleFrontiers in molecular biosciences2020
An Analysis of Proteochemometric and Conformal Prediction Machine Learning Protein-Ligand Binding Affinity Models.
Article in Frontiers in molecular biosciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Explainable AI-driven prediction of APE1 inhibitors: enhancing cancer therapy with machine learning models and feature importance analysis.Molecular diversity · 2025Article
- ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery.Journal of chemical information and modeling · 2023Article
- Ensemble of structure and ligand-based classification models for hERG liability profiling.Frontiers in pharmacology · 2023Article
- In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses.Infection and drug resistance · 2023Review
- Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors.Molecular diversity · 2022Article
- Proteochemometric Method for pIC50 Prediction of Flaviviridae.BioMed research international · 2022Article
- Chagas Disease: Perspectives on the Past and Present and Challenges in Drug Discovery.Molecules (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Protein-ligand binding affinity is a key pharmacodynamic endpoint in drug discovery. Sole reliance on experimental design, make, and test cycles is costly and time consuming, providing an opportunity for computational methods to assist. Herein, we present results comparing random forest and feed-forward neural network proteochemometric models for their ability to predict pIC50 measurements for held out generic Bemis-Murcko scaffolds. In addition, we assess the ability of conformal prediction to provide calibrated prediction intervals in both a retrospective and semi-prospective test using the recently released Grand Challenge 4 data set as an external test set. In total, random forest and deep neural network proteochemometric models show quality retrospective performance but suffer in the semi-prospective setting. However, the conformal predictor prediction intervals prove to be well-calibrated both retrospectively and semi-prospectively showing that they can be used to guide hit discovery and lead optimization campaigns.
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Registered trials
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