Molecule docking

Docking small molecules to a protein’s binding site is often one of the first steps for virtual screening. This application is realated to CANDLE and provides a valubale example.

Molecule docking: Docking small molecules to a protein’s binding site is often one of the first steps for virtual screening 1. Although many open-source and commercial packages exist for docking, AI approaches can be equally powerful (and computationally more efficient) for docking studies 2. Utilizing advances in control from reinforcement learning (RL), Argonne researchers trained an agent to drive the docking of a rigid ligand into a flexible protein pocket. The RL agent treats the ligand as a rigid body to which it can move through affine transformations along the protein. This procedure bypasses sampling on a grid as the agent is trained to optimize the pose against OpenEye Fred docking function 3, and/or other openly available docking tools such as UCSF DOCK, Autodock/Vina. The challenge of this approach is that there is a need to train the agent based on the protein target, which can still take considerable time on single-GPU systems. This area comes from the major Argonne CANDLE 4 project and other applications (DeepDriveMD) will come from this project in the new submissions category.

Refernces

  1. P. D. Lyne, “Structure-based virtual screening: an overview,” Drug Discov. Today, vol. 7, no. 20, pp. 1047–1055, Oct. 2002 [Online]. Available: http://dx.doi.org/10.1016/s1359-6446(02)02483-2 ↩︎

  2. J. Li, A. Fu, and L. Zhang, “An overview of scoring functions used for protein–ligand interactions in molecular docking,” Interdiscip. Sci., pp. 1–9, 2019 [Online]. Available: https://idp.springer.com/authorize/casa?redirect_uri=https://link.springer.com/article/10.1007/s12539-019-00327-w&casa_token=Usuqtf4tu-4AAAAA:VD0uKAo49lSwaEEpmufft87cpUtbmE9MSdlR_Wpv880jHArsLIfLy8PQPAaN6ODJIArQ9GMz15wJ6lSX ↩︎

  3. M. McGann, “FRED pose prediction and virtual screening accuracy,” J. Chem. Inf. Model., vol. 51, no. 3, pp. 578–596, Mar. 2011 [Online]. Available: http://dx.doi.org/10.1021/ci100436p ↩︎

  4. “CANDLE Exascale Deep Learning and Simulation Enabled Precision Medicine for Cancer.” [Online]. Available: https://candle.cels.anl.gov/. [Accessed: 01-May-2020] ↩︎

Last modified January 26, 2024: Fixed typos (a1d6fc4)