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• W2895884529 abstract "The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales. The key parameters range from solubility (angstroms) to protein–ligand binding (nanometers) to in vivo toxicity (meters). Through feature learning—instead of feature engineering—deep neural networks promise to outperform both traditional physics-based and knowledge-based machine learning models for predicting molecular properties pertinent to drug discovery. To this end, we present the PotentialNet family of graph convolutions. These models are specifically designed for and achieve state-of-the-art performance for protein–ligand binding affinity. We further validate these deep neural networks by setting new standards of performance in several ligand-based tasks. In parallel, we introduce a new metric, the Regression Enrichment Factor EFχ(R), to measure the early enrichment of computational models for chemical data. Finally, we introduce a cross-validation strategy based on structural homology clustering that can more accurately measure model generalizability, which crucially distinguishes the aims of machine learning for drug discovery from standard machine learning tasks." @default.
• W2895884529 created "2018-10-26" @default.
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• W2895884529 date "2018-11-02" @default.
• W2895884529 modified "2023-10-13" @default.
• W2895884529 title "PotentialNet for Molecular Property Prediction" @default.
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• W2895884529 doi "https://doi.org/10.1021/acscentsci.8b00507" @default.
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• W2895884529 hasPublicationYear "2018" @default.
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