由人工智能驱动的生成框架整合了ML-QSAR和碎片学习,用于设计异形选择性PI3K抑制剂
Harshit Sajal1, Aswin Mohan1, Rajesh Raju1
1Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be) University, Mangalore, Karnataka, 575018, India.
Molecular diversity
|February 25, 2026
概括
开发具有高同位素选择性的新型酸3-激酶 (PI3K) 抑制剂对于治疗癌症和免疫疾病至关重要. 一个整合机器学习和生成化学的AI框架成功设计了有前途的PI3K抑制剂.
科学领域:
- 药物发现和开发 药物发现和开发
- 计算化学计算化学
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
背景情况:
- 失调的酸3-激酶 (PI3K) 信号传递与癌症的进展,治疗耐药性和免疫系统疾病有关.
- 由于它们的结构相似,在PI3K催化子单元 (α,β,δ,γ) 之间实现异形选择性是具有挑战性的.
研究的目的:
- 开发一个人工智能 (AI) 驱动的框架来设计异形选择性PI3K抑制剂.
- 为了克服结构相似性的局限性,在实现精确的异构体选择性方面.
主要方法:
- 一个整合性AI框架,结合机器学习-定量结构-活动关系 (ML-QSAR) 建模,片段级选择性分析和强化学习生成化学.
- 训练每个PI3K异型的独立XGBoost模型,使用精选的ChEMBL数据集.
- 使用夏普利添加式扩展 (SHAP) 进行模型解释性和碎片分析,以指导分子设计.
主要成果:
- ML-QSAR模型表现出强大的预测性能 (R2 = 0.760.82),具有可解释的特征归属.
- 人工智能框架通过分子对接产生了超过1万种独特的化合物,具有有利的预测结合能 (-7.9到-9.7千卡/mol).
- 产生的化合物表现出良好的药物相似性和合成可访问性,表明作为化合物的潜力.
结论:
- 人工智能框架有效地整合了预测建模和生成化学,以快速发现选择性优化的PI3K抑制剂.
- 这种方法可以将其推广到其他多异型标,为合理的药物设计建立可扩展的AI方法.
- 这项研究推动了人工智能驱动的药物发现,用于精密瘤学和免疫调节疗法.
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