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- W4200509818 abstract "Over the past five decades, tremendous effort has been devoted to computational methods for predicting properties of ligands-i.e., molecules that bind macromolecular targets. Such methods, which are critical to rational drug design, fall into two categories: physics-based methods, which directly model ligand interactions with the target given the target's three-dimensional (3D) structure, and ligand-based methods, which predict ligand properties given experimental measurements for similar ligands. Here, we present a rigorous statistical framework to combine these two sources of information. We develop a method to predict a ligand's pose-the 3D structure of the ligand bound to its target-that leverages a widely available source of information: a list of other ligands that are known to bind the same target but for which no 3D structure is available. This combination of physics-based and ligand-based modeling improves pose prediction accuracy across all major families of drug targets. Using the same framework, we develop a method for virtual screening of drug candidates, which outperforms standard physics-based and ligand-based virtual screening methods. Our results suggest broad opportunities to improve prediction of various ligand properties by combining diverse sources of information through customized machine-learning approaches." @default.
- W4200509818 created "2021-12-31" @default.
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- W4200509818 date "2021-12-17" @default.
- W4200509818 modified "2023-10-14" @default.
- W4200509818 title "Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes" @default.
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- W4200509818 doi "https://doi.org/10.1073/pnas.2112621118" @default.
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- W4200509818 hasPublicationYear "2021" @default.
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