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Novel algorithms and high-performance cloud computing enable efficient fully quantum mechanical protein-ligand scoring

Mardirossian, Narbe and Wang, Yuhang and Pearlman, David A. and Chan, Garnet Kin-Lic and Shiozaki, Toru (2020) Novel algorithms and high-performance cloud computing enable efficient fully quantum mechanical protein-ligand scoring. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20201028-092038661

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Abstract

Ranking the binding of small molecules to protein receptors through physics-based computation remains challenging. Though inroads have been made using free energy methods, these fail when the underlying classical mechanical force fields are insufficient. In principle, a more accurate approach is provided by quantum mechanical density functional theory (DFT) scoring, but even with approximations, this has yet to become practical on drug discovery-relevant timescales and resources. Here, we describe how to overcome this barrier using algorithms for DFT calculations that scale on widely available cloud architectures, enabling full density functional theory, without approximations, to be applied to protein-ligand complexes with approximately 2500 atoms in tens of minutes. Applying this to a realistic example of 22 ligands binding to MCL1 reveals that density functional scoring outperforms classical free energy perturbation theory for this system. This raises the possibility of broadly applying fully quantum mechanical scoring to real-world drug discovery pipelines.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2004.08725arXivDiscussion Paper
ORCID:
AuthorORCID
Mardirossian, Narbe0000-0002-1387-0158
Wang, Yuhang0000-0001-5336-5183
Pearlman, David A.0000-0002-2574-6066
Chan, Garnet Kin-Lic0000-0001-8009-6038
Additional Information:The authors thank Eric Kessler, Chris Downing, David Kanter, and other members of the Amazon Web Services and Amazon Quantum Solution Lab for technical support and helpful conversations. We would also like to thank Yax Sun, Kai Zhu, Huan Rui, Lei Jia, and David Khachatrian at AMGEN Research for helpful comments. Competing interests: The authors declare the following competing interests: Y.W., D.A.P., and T.S. are employees of Quantum Simulation Technologies, Inc. (QSimulate), in which G.K.-L.C. and T.S. are significant shareholders. G.K.-L.C. is a consultant for QSimulate. N.M. declares no competing interest. Author contributions: N.M., G.K.-L.C., T.S. designed the study, N.M., Y.W., T.S. performed the calculations, all authors participated in the analysis of the results, all authors participated in the writing of the manuscript.
Record Number:CaltechAUTHORS:20201028-092038661
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201028-092038661
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106321
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:29 Oct 2020 18:58
Last Modified:29 Oct 2020 18:58

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