机器学习引导新型伪天然产品的产生:加速药物发现的应用
Wenyu Lu1, Xiaoqian Peng1, Yan Huang1
1School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, PR China.
Journal of chemical information and modeling
|October 18, 2025
概括
人工智能 (AI) 通过产生各种伪天然产品来增强天然产品药物发现. 新的机器学习模型提高了化合物药物相似性,并识别了潜在的抗炎剂,加速了发现.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 自然产品 (NPs) 对药物发现至关重要.
- 人工智能 (AI) 提高了基于NP的药物发现效率.
- 现有的人工智能模型产生有限的伪NP,药物相似性差.
研究的目的:
- 开发人工智能模型,用于各种伪NP生成.
- 提高产生的化合物的药物相似性和合成可访问性.
- 从伪NP中识别出新的抗炎药物.
主要方法:
- 具有有效性,独特性和新性的多样化伪NP生成的GPT1模型.
- 增强的登山 (AHC) 战略,以提高药物相似性和合成可访问性.
- 综合NPDL-GEN模型 (GPT1 + AHC) 用于化合物生成.
- 转移学习用于生成抗炎性伪NP.
主要成果:
- GPT1产生了多种类型的伪NP,保留了训练集的分子特征.
- NPDL-GEN模型产生了G1-G5化合物,具有更好的药物相似性.
- 转移学习产生了具有强烈抗炎活性的伪NP H1-H3.
- 生成的化合物显示出出色的有效性,独特性和新性.
结论:
- 开发的机器学习模型加速了基于NP的药物发现.
- 综合NPDL-GEN模型增强了化合物药物相似性和合成可访问性.
- 人工智能驱动的伪NP生成可以导致新型治疗剂.
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