下一代人工智能辅助的抗癌药物设计:大型语言模型与传统的in silico方法相遇
Elina Khanehzar1,2, Fatemeh Shams1,2, Amirsajad Jafari2,3
1Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
In silico pharmacology
|March 2, 2026
概括
这项研究使用大型语言模型 (LLM) 来设计针对AXL-GAS6通路的新型抗癌药物. 人工智能设计的抑制剂显示出有希望的结合亲和力和有利的类似药物的特性,用于癌症治疗.
科学领域:
- 计算化学和药物发现
- 医学中的人工智能
- 瘤学研究的研究.
背景情况:
- 癌症是死亡的主要原因,治疗方法有限.
- 该AXL-GAS6通路驱动瘤的进展和耐药性.
- 大型语言模型 (LLM) 为药物设计提供了潜力.
研究的目的:
- 使用集成的计算管道设计新的小分子AXL抑制剂.
- 为了利用LLMs产生多样化的分子支架.
- 为了加速抗癌药物开发.
主要方法:
- 集成DeepSeek LLM与分子对接,分子动力学 (MD) 和ADMET评估.
- 由天然产品,微生物组和FDA批准的药物启发的新型AXL抑制剂的产生.
- 通过药物相似性,合成可行性和对接得分来过候选药物,然后进行MD模拟和ADMET分析.
主要成果:
- 与bemcentinib相比,AIC1显示出更高的结合亲和力 (-10.079 kcal / mol).
- MD模拟证实了具有广泛结的稳定药物标复合体.
- 对ADMET的分析表明,设计抑制剂,特别是AIC2.2,具有良好的药理动力学和低毒性.
结论:
- 这项研究开创了AXL抑制剂的LLM驱动的设计.
- 开发的计算管道为加速药物发现提供了可扩展的方法.
- 设计的AXL抑制剂代表了抗癌药物开发的有希望的候选人.
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