Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem.

ABSTRACT: Despite considerable advances obtained by applying machine learning approaches in protein-ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a solution to this problem, the optimum choice of receptor conformations is still an open question considering the issues related to the computational cost and false positive pose predictions. Here, a combination of ensemble learning and ensemble docking is suggested to rank different conformations of the target protein in light of their importance for the final accuracy of the model. Available X-ray structures of cyclin-dependent kinase 2 (CDK2) in complex with different ligands are used as an initial receptor ensemble, and its redundancy is remov

SUBMITTER: Mohammadi S 

PROVIDER: S-EPMC8748946 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature