Benchmarking Cross-Docking Strategies in Kinase Drug Discovery

In recent years, machine learning has transformed many aspects of the drug discovery process, including small molecule design, for which the prediction of bioactivity is an integral part. Leveraging structural information about the interactions between a small molecule and its protein target has gre...

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Bibliographic Details
Main Author:	David A. Schaller (20288030) (author)
Other Authors:	Clara D. Christ (2632918) (author), John D. Chodera (1323594) (author), Andrea Volkamer (1444000) (author)
Published:	2024
Subjects:	Biophysics Biochemistry Genetics Pharmacology Biotechnology Immunology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified transformed many aspects reproduce binding poses recovering binding poses maximum common substructure leveraging structural information inhibitor complex geometries finding practical approaches drug discovery process utilizing shape overlap kinase drug discovery based docking alone pose selection strategies docking methods biased generating useful kinase docking utilizing docking strategies docking pose three methods studied docking docking scenario success rate standard physics square deviation small molecule recent years realistic cross protein target protein kinases protein families openeye toolkits machine learning low root kinoml framework integral part included systems great potential general findings fundamentally limited efficient way different classes competitive ligands cocrystallized ligand benchmarking cross automated fashion although focused allowing automated 423 atp
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Benchmarking Cross-Docking Strategies in Kinase Drug Discovery

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