Article ; Online: cMolGPT
Molecules, Vol 28, Iss 4430, p
A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation
2023 Volume 4430
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 ... ...
| 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. |
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| Keywords | molecular design ; machine learning ; generative pre-trained transformer ; GPT ; Organic chemistry ; QD241-441 |
| Subject code | 006 |
| Language | English |
| Publishing date | 2023-05-01T00:00:00Z |
| Publisher | MDPI AG |
| Document type | Article ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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