Enhancing De Novo Drug Design across Multiple Therapeutic Targets with CVAE Generative Models.

ABSTRACT: Drug discovery is a costly and time-consuming process, necessitating innovative strategies to enhance efficiency across different stages, from initial hit identification to final market approval. Recent advancement in deep learning (DL), particularly in de novo drug design, show promise. Generative models, a subclass of DL algorithms, have significantly accelerated the de novo drug design process by exploring vast areas of chemical space. Here, we introduce a Conditional Variational Autoencoder (CVAE) generative model tailored for de novo molecular design tasks, utilizing both SMILES and SELFIES as molecular representations. Our computational framework successfully generates molecules with specific property profiles validated though metrics such as uniqueness, validity

SUBMITTER: Romanelli V 

PROVIDER: S-EPMC11525747 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature