Structure-based maximal affinity model predicts small-molecule druggability |
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Authors: | Cheng Alan C Coleman Ryan G Smyth Kathleen T Cao Qing Soulard Patricia Caffrey Daniel R Salzberg Anna C Huang Enoch S |
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Institution: | Department of Molecular Informatics, Research Technology Center, Pfizer Global Research & Development, Cambridge, Massachusetts 02139, USA. alan.cheng@amgen.com |
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Abstract: | 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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