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通过转移学习机制,提高新型HIV-1蛋白酶抑制剂对抗药性预测效率

Huseyin Tunc¹, Sumeyye Yilmaz², Busra Nur Darendeli Kiraz³

¹Department of Biostatistics and Medical Informatics, School of Medicine, Bahcesehir University, Istanbul 34734, Turkey.

Journal of chemical information and modeling

|October 11, 2024

概括

这项研究引入了一种新的药物隔离折叠变化 (DIF) 模型,以预测人类免疫缺陷病毒的耐药性. DIF模型有效地使用分子表示来改善对新型蛋白酶抑制剂的预测.

科学领域:

病毒学病毒学
计算生物学计算生物学
药物发现药物发现药物发现

背景情况:

人类免疫缺陷病毒 (HIV) 由于快速突变和耐药性,构成了全球健康威胁.
机器学习 (ML) 和深度学习 (DL) 在预测药物耐药性方面表现有前途,但在推广抑制剂表征方面存在困难.
预测HIV耐药性需要从病毒分离物和药物特征中学习的模型.

研究的目的:

开发一种新的药物隔离折叠变化 (DIF) 模型框架,用于预测HIV耐药性得分.
通过结合抑制剂表示来增强ML/DL模型的分子学习能力.
通过使用现实的验证策略和各种数据集来评估DIF模型的有效性.

主要方法:

提出了一个新的药物隔离折叠变化 (DIF) 模型框架,集成蛋白序列和抑制剂表示.
通过预训练图形神经网络 (GNN) 来对4855种HIV-1蛋白酶抑制剂 (PI) 进行活动预测,员工转移学习.
分析了各种ML/DL模型,抑制剂和蛋白质表示,使用内部和外部的基因型-表型数据集.

主要成果:

通过利用学习的抑制剂表示,DIF模型表现出更好的预测能力.
实现了高性能指标:0.802准确度,0.874 AUROC和0.727 r对于未见的外部PI.
与孤立折叠变化 (IF) 模型相比,观察到具有统计学意义的改善,证实了分子表示的好处.

结论:

DIF模型框架有效预测HIV-1蛋白酶抑制剂的耐药性得分.
纳入分子表征显著提高了ML / DL模型对HIV耐药性的预测能力.
DIF模型提供了一种强大的方法来评估针对耐药性HIV菌株的新型PI.

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