Analysis abstract


Nature Biotechnology 25, 71 - 75 (2007)
Published online: 8 January 2007 | doi:10.1038/nbt1273

Structure-based maximal affinity model predicts small-molecule druggability

Alan C Cheng1,2,3, Ryan G Coleman1, Kathleen T Smyth2, Qing Cao1, Patricia Soulard2, Daniel R Caffrey1, Anna C Salzberg1 & Enoch S Huang1


Lead generation is a major hurdle in small-molecule drug discovery, with an estimated 60% of projects failing from lack of lead matter or difficulty in optimizing leads for drug-like properties. It would be valuable to identify these less-druggable targets before incurring substantial expenditure and effort. Here we show that a model-based approach using basic biophysical principles yields good prediction of druggability based solely on the crystal structure of the target binding site. We quantitatively estimate the maximal affinity achievable by a drug-like molecule, and we show that these calculated values correlate with drug discovery outcomes. We experimentally test two predictions using high-throughput screening of a diverse compound collection. The collective results highlight the utility of our approach as well as strategies for tackling difficult targets.

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  1. Department of Molecular Informatics, Research Technology Center, Pfizer Global Research & Development, Cambridge, Massachusetts 02139, USA.
  2. Department of Biological Sciences, Research Technology Center, Pfizer Global Research & Development, Cambridge, Massachusetts 02139, USA.
  3. Present address: Amgen Inc., One Kendall Square Bldg. 1000, Cambridge, Massachusetts 02139, USA.

Correspondence to: Alan C Cheng1,2,3 e-mail: alan.cheng@amgen.com.



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