Work

Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions

Year: 2026

Type: article

Abstract: Phenotypic drug discovery (PDD) identifies new drugs by observing the effects of compounds on living systems without prior knowledge of their targets. Advances in biological data and machine learning have made PDD more systematic and data-driven. This review outlines a computational framework, including phenotype representations, key tools, and public datasets. It also discusses major challenges and strategies to improve PDD's efficiency and translational potential, offering a practical guide for researchers in the field.

Source: npj Systems Biology and Applications

DOI: 10.1038/s41540-026-00676-5

Authors: Shivam Kumar, Aman Pal, Samrat Chatterjee

Institution: Translational Health Science and Technology Institute

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,490

Funders: Council of Scientific and Industrial Research, India, Translational Health Science and Technology Institute