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Original Research ARTICLE

Front. Mol. Biosci. | doi: 10.3389/fmolb.2020.601065

RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features Provisionally accepted The final, formatted version of the article will be published soon. Notify me

 Stefan Holderbach1,  Lukas Adam1,  B Jayaram2,  Rebecca C. Wade1, 3* and Goutam Mukherjee1, 3*
  • 1Heidelberg University, Germany
  • 2Indian Institute of Technology Delhi, India
  • 3Heidelberg Institute for Theoretical Studies (HITS), Germany

The virtual screening of large numbers of compounds against target protein binding sites has become an integral component of drug discovery workflows. This screening is often done by computationally docking ligands into a protein binding site of interest, but this has the drawback that a large number of poses must be evaluated to obtain accurate estimates of protein-ligand binding affinity.
We here introduce a fast prefiltering method for ligand prioritization that is based on a set of machine learning models and uses simple pose-invariant physicochemical descriptors of the ligands and the protein binding pocket.
Our method, Rapid Screening with Physicochemical Descriptors + machine learning (RASPD+), is trained on PDBbind data and achieves a regression performance better than for the original RASPD method and comparable to traditional scoring functions on a range of different test sets without the need for generating ligand poses.
Additionally, we use RASPD+ to identify molecular features important for binding affinity and assess the ability of RASPD+ to enrich active molecules from decoys.

Keywords: Structure based drug design, Virtual Screening, Physicochemical molecular descriptors, machine learning, protein-ligand complex, binding free energy

Received: 31 Aug 2020; Accepted: 13 Nov 2020.

Copyright: © 2020 Holderbach, Adam, Jayaram, Wade and Mukherjee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Rebecca C. Wade, Heidelberg University, Heidelberg, 69117, Baden-Württemberg, Germany, rebecca.wade@h-its.org
Dr. Goutam Mukherjee, Heidelberg University, Heidelberg, 69117, Baden-Württemberg, Germany, goutam.mukherjee@h-its.org