Structure-based maximal affinity model predicts small-molecule druggability.
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.
Cheng
Alan C
AC
Department of Molecular Informatics, Research Technology Center, Pfizer Global Research & Development, Cambridge, Massachusetts 02139, USA. alan.cheng@amgen.com
Coleman
Ryan G
RG
Smyth
Kathleen T
KT
Cao
Qing
Q
Soulard
Patricia
P
Caffrey
Daniel R
DR
Salzberg
Anna C
AC
Huang
Enoch S
ES
D000465:Algorithms; D001665:Binding Sites; D003198:Computer Simulation; D015195:Drug Design; D008956:Models, Chemical; D008958:Models, Molecular; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry*
D004364:Pharmaceutical Preparations; D011506:Proteins