整合 (深度) 机器学习和化学信息学来预测人类小分子的肠道吸收
Orchid Baruah1, Upashya Parasar1, Anirban Borphukan1
1Department of Information Technology, The Assam Kaziranga University, Jorhat, Assam 785006, India.
预测药物吸收是口服药物输送的关键. 这项研究开发了机器学习模型,以准确预测人类肠道吸收,改善了新药候选药物的口服生物利用率预测.
科学领域:
- 药理学 药理学是指药理学的学科.
- 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:
- 口服是药物管理中最喜欢的途径,使口服生物可用性成为药物开发中的关键因素.
- 人体肠道吸收 (HIA) 是口服生物可用性的关键决定因素,需要准确的预测方法.
- 开发可靠的HIA预测模型可以显著简化药物发现和开发过程.
研究的目的:
- 开发和评估机器学习和深度学习模型,用于预测HIA中的药物透性.
- 建立可靠的计算工具,用于评估药物开发早期口服生物可用性潜力.
- 为培训和验证HIA的预测模型策划一个全面的数据集.
主要方法:
- 为了培训和验证,编制了2648种化合物的数据集.
- 使用工程分子描述器训练了五个机器学习算法 (包括随机森林和LightGBM).
- 两种深度学习模型,图形卷积神经网络 (GCNN) 和图形注意网络 (GAT),是使用自动特征提取来开发的.
主要成果:
- 随机森林在测试组实现了87.71%的准确性,在外部验证组实现了81.43%.
- 在测试组件中,LightGBM的准确度达到86.04%,在外部验证组件中达到77.30%.
- 在GCNN和GAT模型中,测试套件的准确率分别为77.69%和78.58%,外部验证套件的准确率分别为79.29%和79.42%.
结论:
- 机器学习模型,特别是随机森林和LightGBM,在预测HIA药物透性方面表现出高准确性.
- 深度学习模型 (GCNN,GAT) 也为HIA提供了有价值的预测能力.
- 开发的模型和数据集在GitHub上可用,可以帮助选口服药物,以提高药物发现效率.
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