Adapt-cMolGPT: A Conditional Generative Pre-Trained Transformer with Adapter-Based Fine-Tuning for Target-Specific Molecular Generation

Small-molecule drug design aims to generate compounds that target specific proteins, playing a crucial role in the early stages of drug discovery. Recently, research has emerged that utilizes the GPT model, which has achieved significant success in various fields to generate molecular compounds. How...

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Bibliographic Details
Published in:International journal of molecular sciences 2024-06, Vol.25 (12), p.6641
Main Authors: Yoo, Soyoung, Kim, Junghyun
Format: Article
Language:English
Subjects:
Algorithms
Computational linguistics
Datasets
Deep learning
Drug Design
Drug discovery
Drug Discovery - methods
Electric transformers
Evaluation
fine-tuning
generative pre-trained transformer
Humans
Language processing
Natural language interfaces
Performance evaluation
Proteins
SELFIES
Small Molecule Libraries - chemistry
Small Molecule Libraries - pharmacology
small-molecule drug design
Validity
Online Access:Get full text
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Summary:Small-molecule drug design aims to generate compounds that target specific proteins, playing a crucial role in the early stages of drug discovery. Recently, research has emerged that utilizes the GPT model, which has achieved significant success in various fields to generate molecular compounds. However, due to the persistent challenge of small datasets in the pharmaceutical field, there has been some degradation in the performance of generating target-specific compounds. To address this issue, we propose an enhanced target-specific drug generation model, Adapt-cMolGPT, which modifies molecular representation and optimizes the fine-tuning process. In particular, we introduce a new fine-tuning method that incorporates an adapter module into a pre-trained base model and alternates weight updates by sections. We evaluated the proposed model through multiple experiments and demonstrated performance improvements compared to previous models. In the experimental results, Adapt-cMolGPT generated a greater number of novel and valid compounds compared to other models, with these generated compounds exhibiting properties similar to those of real molecular data. These results indicate that our proposed method is highly effective in designing drugs targeting specific proteins.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms25126641