  Traditional drug discovery programs are being transformed by the advent of
machine learning methods. Among these, Generative AI methods (GM) have gained
attention due to their ability to design new molecules and enhance specific
properties of existing ones. However, current GM methods have limitations, such
as low affinity towards the target, unknown ADME/PK properties, or the lack of
synthetic tractability. To improve the applicability domain of GM methods, we
have developed a workflow based on a variational autoencoder coupled with
active learning steps. The designed GM workflow iteratively learns from
molecular metrics, including drug likeliness, synthesizability, similarity, and
docking scores. In addition, we also included a hierarchical set of criteria
based on advanced molecular modeling simulations during a final selection step.
We tested our GM workflow on two model systems, CDK2 and KRAS. In both cases,
our model generated chemically viable molecules with a high predicted affinity
toward the targets. Particularly, the proportion of high-affinity molecules
inferred by our GM workflow was significantly greater than that in the training
data. Notably, we also uncovered novel scaffolds significantly dissimilar to
those known for each target. These results highlight the potential of our GM
workflow to explore novel chemical space for specific targets, thereby opening
up new possibilities for drug discovery endeavors.
