Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design

TL;DR

This paper introduces iMiner, a machine learning framework that combines deep reinforcement learning with real-time molecular docking to generate novel, target-specific inhibitor molecules efficiently, streamlining drug discovery.

Contribution

The novel iMiner algorithm integrates reinforcement learning with docking and filtering, enabling rapid, structure-based design of potential drug molecules with chemical and biological relevance.

Findings

Successfully generated novel inhibitors with desired binding properties.

Demonstrated adaptability to different target proteins.

Enhanced drug discovery efficiency through integrated filtering and validation.

Abstract

The viability of a new drug molecule is a time and resource intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibitor molecules for target proteins by combining deep reinforcement learning with real-time 3D molecular docking using AutoDock Vina, thereby simultaneously creating chemical novelty while constraining molecules for shape and molecular compatibility with target active sites. Moreover, through the use of various types of reward functions, we can generate new molecules that are chemically similar to a target ligand, which can be grown from known protein bound fragments, as well as to create molecules that enforce interactions with target residues in the protein active site. The iMiner algorithm is embedded in a composite workflow that filters out Pan-assay interference compounds, Lipinski rule violations, and poor synthetic accessibility, with options for cross-validation against other docking scoring functions and automation of a molecular dynamics simulation to measure pose stability. Because our approach only relies on the structure of the target protein, iMiner can be easily adapted for future development of other inhibitors or small molecule therapeutics of any target protein.