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- Title
cMolGPT: A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation.
- Authors
Wang, Ye; Zhao, Honggang; Sciabola, Simone; Wang, Wenlu
- Abstract
Deep generative models applied to the generation of novel compounds in small-molecule drug design have attracted a lot of attention in recent years. To design compounds that interact with specific target proteins, we propose a Generative Pre-Trained Transformer (GPT)-inspired model for de novo target-specific molecular design. By implementing different keys and values for the multi-head attention conditional on a specified target, the proposed method can generate drug-like compounds both with and without a specific target. The results show that our approach (cMolGPT) is capable of generating SMILES strings that correspond to both drug-like and active compounds. Moreover, the compounds generated from the conditional model closely match the chemical space of real target-specific molecules and cover a significant portion of novel compounds. Thus, the proposed Conditional Generative Pre-Trained Transformer (cMolGPT) is a valuable tool for de novo molecule design and has the potential to accelerate the molecular optimization cycle time.
- Subjects
LANGUAGE models; GENERATIVE pre-trained transformers; DOSAGE forms of drugs; DRUG design
- Publication
Molecules, 2023, Vol 28, Issue 11, p4430
- ISSN
1420-3049
- Publication type
Article
- DOI
10.3390/molecules28114430