  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.
