Dataset Information

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 molecular generation called MTMol-GPT. The multi-target molecular generator employs a dual discriminator model using the Inverse Reinforcement Learning (IRL) method for a concurrently multi-target molecular generation. Extensive results show that MTMol-GPT generates various valid, novel, and effective multi-target molecules for various complex diseases, demonstrating robustness and generalization capability. In addition, molecular docking and pharmacophore mapping experiments demonstrate the drug-likeness properties and effectiveness of generated molecules potentially improve neuropsychiatric interventions. Furthermore, our model's generalizability is exemplified by a case study focusing on the multi-targeted drug design for breast cancer. As a broadly applicable solution for multiple targets, MTMol-GPT provides new insight into future directions to enhance potential complex disease therapeutics by generating high-quality multi-target molecules in drug discovery.

SUBMITTER: Ai C

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

REPOSITORIES: biostudies-literature

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Publications

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

Ai Chengwei C Yang Hongpeng H Liu Xiaoyi X Dong Ruihan R Ding Yijie Y Guo Fei F

PLoS computational biology 20240626 6

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 t ...[more]

PMID: 38924082

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Dataset Information

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

Publications

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

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