ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation

TL;DR

ChemSpaceAL introduces an efficient active learning approach for protein-specific molecular generation, enabling targeted drug discovery with minimal data evaluation and demonstrating success in generating molecules for proteins with and without known inhibitors.

Contribution

The paper presents a novel, computationally efficient active learning methodology for targeted molecular generation, applicable to proteins with or without known inhibitors.

Findings

Successfully fine-tuned a GPT-based generator for c-Abl kinase inhibitors.

Generated molecules similar to known inhibitors without prior knowledge.

Effective for proteins lacking existing small-molecule inhibitors.

Abstract

The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Within this domain, the vastness of chemical space motivates the development of more efficient methods for identifying regions with molecules that exhibit desired characteristics. In this work, we present a computationally efficient active learning methodology that requires evaluation of only a subset of the generated data in the constructed sample space to successfully align a generative model with respect to a specified objective. We demonstrate the applicability of this methodology to targeted molecular generation by fine-tuning a GPT-based molecular generator toward a protein with FDA-approved small-molecule inhibitors, c-Abl kinase. Remarkably, the model learns to generate molecules similar to the inhibitors without prior knowledge of their existence, and even reproduces two of them exactly. We also show that the methodology is effective for a protein without any commercially available small-molecule inhibitors, the HNH domain of the CRISPR-associated protein 9 (Cas9) enzyme. We believe that the inherent generality of this method ensures that it will remain applicable as the exciting field of in silico molecular generation evolves. To facilitate implementation and reproducibility, we have made all of our software available through the open-source ChemSpaceAL Python package.