Open AccessCollaborative Approach between Explainable Artificial Intelligence and Simplified Chemical Interactions to Explore Active Ligands for Cyclin-Dependent Kinase 2
Author(s) -
Tomomi Shimazaki,
Masanori Tachikawa
Publication year - 2022
Publication title -
acs omega
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 40
ISSN - 2470-1343
DOI - 10.1021/acsomega.1c06976
Subject(s) - virtual screening , artificial intelligence , decoy , computer science , docking (animal) , active site , computational biology , kinase , cyclin dependent kinase , chemistry , machine learning , drug discovery , combinatorial chemistry , biochemistry , biology , receptor , enzyme , medicine , nursing , cell cycle , cell
To improve virtual screening for drug discovery, we present a collaborative approach between explainable artificial intelligence (AI) and simplified chemical interaction scores to efficiently search for active ligands bound to the target receptor. In particular, we focus on cyclin-dependent kinase 2 (CDK2), which is well known as a cancer target protein. Docking simulation alone is insufficient to distinguish active ligands from decoy molecules. To identify active ligands, in this paper, machine learning is employed together with scoring functions that simplify the screened Coulomb and Lennard-Jones interactions between the ligands and residues of the target receptor. We demonstrate that these simplified interaction scores can significantly improve the classification ability of machine learning models. We also demonstrate that explainable AI together with the simplified scoring method can highlight the important residues of CDK2 for recognizing active ligands.