Computational Hit Finding: An Industry Perspective

Abstract

Computational hit finding, particularly virtual screening, has been a mainstay of drug discovery campaigns for decades, providing a cost-efficient complement to wet experiments. Innovation in this space slowed considerably as these approaches converged around mature software programs and stock chemical libraries up to ∼10 million in size. Recently, however, powered by massive increases in computational power, the emergence of ultralarge make-on-demand virtual libraries, the development of large capacity neural networks, the expansion of the domain of applicability of free energy calculations, and advances in protein structure prediction, the virtual screening field is currently seeing major change. We present a guide from industry practitioners summarizing key aspects on the changing computational hit finding landscape including practical recommendations for building a performant end-to-end screening workflow, strategies to mitigate risk by avoiding common pitfalls, determining success criteria, and a brief discussion of emerging technologies likely to impact drug discovery in the near future.