Combining label-free cell phenotypic profiling with computational approaches for novel drug discovery

Abstract

Introduction: Drug discovery is a long and costly process. Innovations and paradigm shifts are essential for continuous improvement in the productivity of pharmaceutical R&D.

Areas covered: The author reviews the progress of label-free cell phenotypic and computational approaches in early drug discovery since 2004 and proposes a novel paradigm, which combines both approaches.

Expert opinion: Label-free cell phenotypic profiling techniques offer an unprecedented and integrated approach to comprehend drug–target interactions in their native environments. However, these approaches have disadvantages associated with the lack of molecular details. Computational approaches, including ligand-, structure- and phenotype-based virtual screens, have become versatile tools in the early drug discovery process. However, these approaches mostly predict the binding of drug molecules to targets of interest and are limited to targets that are either well annotated for ligands or that are structurally resolved. Thus, combining label-free cell phenotypic profiling with computational approaches can provide a potential paradigm to accelerate novel drug discovery by taking advantages of the best of both approaches.

Keywords:

• chemical similarity
• drug discovery
• label-free cell phenotypic approach
• ligand-based virtual screen
• polypharmacology
• structure-based virtual screen
• text mining

Declaration of interest

Y Fang is a research director and fellow in the Science and Technology Division of Corning Incorporated. He is also a stockholder of Corning Incorporated. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Notes

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