PDF(Pubmed)
Abstract：
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.
摘要：
小分子药物设计旨在产生靶向特定蛋白质的化合物，在药物发现的早期阶段起着至关重要的作用。最近,利用GPT模型的研究已经出现，在产生分子化合物的各个领域取得了显著的成功。然而,由于制药领域小数据集的持续挑战，在产生目标特定化合物的性能方面存在一些降解。为了解决这个问题，我们提出了一种增强的靶标特异性药物生成模型，Adapt-cMolGPT,它修改了分子表示并优化了微调过程。特别是,我们引入了一种新的微调方法，该方法将适配器模块集成到预训练的基础模型中，并按部分交替进行权重更新。我们通过多次实验评估了所提出的模型，并证明了与以前模型相比的性能改进。在实验结果中,与其他模型相比，Adapt-cMolGPT产生了更多的新颖有效化合物，这些生成的化合物表现出与真实分子数据相似的特性。这些结果表明，我们提出的方法在设计靶向特定蛋白质的药物方面非常有效。
