机器学习驱动的计算药物重新定位用于识别新的针对癌症的氨酸抑制剂
Neelima Neelima1, Hussein Elrewey2, Sneha Smarakan1
1Institute of Cancer Therapeutics, School of Pharmacy, Optometry and Medical Sciences, University of Bradford, Bradford BD71DP, UK.
Bioorganic & medicinal chemistry
|February 14, 2026
概括
机器学习识别了现有的药物作为潜在的抗癌药物,向图布林. 这种药物重新定位的方法产生了对癌细胞具有显著的抗增殖活性的奥梅普拉和波多菲洛克斯.
科学领域:
- 在瘤学瘤学.
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 图布林是一种关键的抗癌标,但现有的抑制剂面临毒性和耐药性问题.
- 胆固醇结合部位抑制剂的临床转化是有限的.
- 需要新的策略来发现更安全的氨酸向抗癌剂.
研究的目的:
- 为了加快新型,更安全的氨酸向抗癌剂的发现.
- 开发和应用一个基于机器学习的药物重用策略.
- 通过准布林的菌素结合部位来识别具有潜在抗癌活性的现有药物.
主要方法:
- 机器学习 (ML) AutoQSAR 模型在 279 种氨酸抑制剂上受过训练.
- 4500种美国FDA批准的药物使用ML模型和基于结构的虚拟选 (Glide HTVS,SP,XP) 进行了选.
- 分子动力学模拟 (MM-GBSA,200 ns MD) 和生化分析验证了潜在的候选药物.
主要成果:
- 从最初的药物选中预测了1800种潜在的氨酸抑制剂.
- 确定了四种稳定的结合剂 (omeprazole,podofilox,sulfadoxine,trimethoprim) 具有有利的结合能量.
- 奥梅普拉和波多菲洛克斯对黑色素瘤和结直肠癌细胞显示出低微分子抗增殖活性.
结论:
- 一个以机器学习为指导的药物重定向框架成功地识别了针对结素结合部位的新型抗癌候选物.
- 这种综合计算和实验方法为发现更安全的抗癌剂提供了一个有希望的策略.
- 奥梅普拉和波多菲洛克斯代表了进一步发展的潜在线索,作为针对图布林的癌症治疗药物.
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