Computational drug discovery

Abstract

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field.

Figure 1

Multiple computational drug discovery approaches that have been applied in various stages of the drug discovery and development pipeline, including target identification and validation, lead discovery and optimization, and preclinical tests.

Figure 2

Important methodologies and platforms in the computational drug discovery field introduced and discussed in this article, with a focus on target identification and lead discovery fields.