A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor.

ABSTRACT:

Background

Virtual screening is vital for contemporary drug discovery but striking performance fluctuations are commonly encountered, thus hampering error-free use. Results and Methodology: A conceptual framework is suggested for combining screening algorithms characterized by orthogonality (docking-scoring calculations, 3D shape similarity, 2D fingerprint similarity) into a simple, efficient and expansible python-based consensus ranking scheme. An original experimental dataset is created for comparing individual screening methods versus the novel approach. Its utilization leads to identification and phosphoproteomic evaluation of a cell-active DYRK1α inhibitor.

Conclusion

Consensus ranking considerably stabilizes screening performance at reasonable computational cost, where

SUBMITTER: Mavrogeni ME 

PROVIDER: S-EPMC6479281 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature