用GPT模型生成类天然产物分子，加速药物发现
NPGPT: Natural Product-Like Compound Generation with GPT-based Chemical Language Models
- 基于天然产物数据训练GPT化学语言模型生成新分子
- 生成分子的结构分布与真实天然产物高度相似
- 适合药物研发人员探索未被覆盖的化学空间
天然产物是生物体自然产生的物质，常具生物活性和结构多样性。基于天然产物的药物开发已有多年历史。然而，其复杂结构在结构鉴定和合成上面临挑战，远不如合成化合物的高通量筛选高效。近年来，深度学习方法已用于分子生成。本研究在天然产物数据集上训练化学语言模型，生成类天然产物分子。结果表明，生成分子的分布与天然产物相似。我们还评估了生成分子作为候选药物的有效性。该方法可拓展巨大化学空间，降低天然产物药物发现的时间与成本。
原文摘要 · Abstract (English)
Natural products are substances produced by organisms in nature and often possess biological activity and structural diversity. Drug development based on natural products has been common for many years. However, the intricate structures of these compounds present challenges in terms of structure determination and synthesis, particularly compared to the efficiency of high-throughput screening of synthetic compounds. In recent years, deep learning-based methods have been applied to the generation of molecules. In this study, we trained chemical language models on a natural product dataset and generated natural product-like compounds. The results showed that the distribution of the compounds generated was similar to that of natural products. We also evaluated the effectiveness of the generated compounds as drug candidates. Our method can be used to explore the vast chemical space and reduce the time and cost of drug discovery of natural products.
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