Generative models show promise in drug discovery by enabling the design of molecules with desired properties, yet often face challenges related to target engagement, synthetic accessibility, and generalization. To address these, the authors developed a workflow combining a variational autoencoder with active learning cycles, generating diverse, drug-like molecules with synthetic feasibility and high predicted affinity for CDK2 and KRAS.
- Isaac Filella-Merce
- Alexis Molina
- Victor Guallar
