Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms.

ABSTRACT: This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs

SUBMITTER: Bhowmik D 

PROVIDER: S-EPMC11026973 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature