Predicting kinase inhibitors using bioactivity matrix derived informer sets

Abstract excerpt

Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to train supervised learning models for compound prioritization, initial screening must provide the necessary data. Commonly, such an initial library is selected on the ba...

Topics

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• Area Under Curve
• Computational Drug Discovery Methods
• Drug Evaluation, Preclinical
• Enzyme Activation
• Ligands
• Molecular Docking Simulation
• Principal Component Analysis
• Protein Degradation and Inhibitors

Identifiers and source

Literature Corpus work

189385f5-9315-5794-bfc9-e63c89af9fe5

DOI

10.1101/532762