MTMol-GPT: De novo multi-target molecular generation with transformer-based generative adversarial imitation learning.

ABSTRACT: De novo drug design is crucial in advancing drug discovery, which aims to generate new drugs with specific pharmacological properties. Recently, deep generative models have achieved inspiring progress in generating drug-like compounds. However, the models prioritize a single target drug generation for pharmacological intervention, neglecting the complicated inherent mechanisms of diseases, and influenced by multiple factors. Consequently, developing novel multi-target drugs that simultaneously target specific targets can enhance anti-tumor efficacy and address issues related to resistance mechanisms. To address this issue and inspired by Generative Pre-trained Transformers (GPT) models, we propose an upgraded GPT model with generative adversarial imitation learning for multi-target molecul

SUBMITTER: Ai C 

PROVIDER: S-EPMC11233020 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature