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Structure-based maximal affinity model predicts small-molecule druggability

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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Figure 1: Ligand molecular weight correlates with protein-binding pocket surface area.
Figure 2: Calculated druggability for a set of 27 target binding sites.
Figure 3: MAPpod score comparisons.
Figure 4: Predictions and screening results for two novel targets using a diverse set of 11,000 compounds.

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Acknowledgements

We thank colleagues across Pfizer Global R&D for discussions, and Jill Milne and Ralph Lambalot for supporting the HTS experiment. A.C.C. additionally thanks Ken Dill for advice. This work was entirely funded by Pfizer.

Author information

Authors and Affiliations

Authors

Contributions

A.C.C. developed the method, designed experiments and analyzed data and wrote the manuscript; R.G.C. developed computational geometry algorithms and analyzed data; K.T.S. helped design the HTS experiment, performed screening for HSD, and analyzed results of the screening comparison; P.S. helped design the HTS experiment, developed the H-PGDS assay and performed screening; Q.C. and D.R.C. helped analyze data for Figures 1 and 3a; A.C.S. helped implement computational methods; E.S.H. discussed results and helped design the HTS experiment. All authors reviewed the manuscript.

Corresponding author

Correspondence to Alan C Cheng.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

Details of targets used in study.

Supplementary Table 2

References and notes for best affinity values.

Supplementary Methods

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Cheng, A., Coleman, R., Smyth, K. et al. Structure-based maximal affinity model predicts small-molecule druggability. Nat Biotechnol 25, 71–75 (2007). https://doi.org/10.1038/nbt1273

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