PharmRL: pharmacophore elucidation with deep geometric reinforcement learning.

ABSTRACT:

Background

Molecular interactions between proteins and their ligands are important for drug design. A pharmacophore consists of favorable molecular interactions in a protein binding site and can be utilized for virtual screening. Pharmacophores are easiest to identify from co-crystal structures of a bound protein-ligand complex. However, designing a pharmacophore in the absence of a ligand is a much harder task.

Results

In this work, we develop a deep learning method that can identify pharmacophores in the absence of a ligand. Specifically, we train a CNN model to identify potential favorable interactions in the binding site, and develop a deep geometric Q-learning algorithm that attempts to select an optimal subset of these interaction points to form a pharmacophore. With t

SUBMITTER: Aggarwal R 

PROVIDER: S-EPMC11687028 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature