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Review Article

精准医学与药物发现中的人工智能、大数据和机器学习方法

卷 22, 期 6, 2021

发表于: 04 January, 2021

页: [631 - 655] 页: 25

弟呕挨: 10.2174/1389450122999210104205732

价格: $65

摘要

人工智能使药物开发过程发生了革命性的变化，它可以在短时间内从数百万候选药物中快速识别出潜在的生物活性化合物。目前的审查概述基于某些应用程序基于机器学习的工具,如黄金、深度PVP,自由支持向量机等和所涉及的算法,如支持向量机(SVM),随机森林(RF)、决策树和人工神经网络(ANN)在药物设计和开发的不同阶段等。这些技术可用于SNP的发现、药物的重新利用、基于配体的药物设计(LBDD)、基于配体的虚拟筛选(LBVS)和基于结构的虚拟筛选(SBVS)、先导物的鉴定、定量构效关系(QSAR)建模和ADMET分析。结果表明，支持向量机在预测人体肠道吸收(HIA)方面具有较好的性能。已报道的成功案例表明，SVM和RF模型通过抑制FEN1细胞毒和切割活性，将JFD00950作为一种靶向结肠癌细胞系DLD-1的新化合物。此外，还使用QSAR模型预测类黄酮对AR活性的抑制作用，作为糖尿病(DM)的有效治疗手段。因此，在大数据时代，ML方法已经发展成为一种强大而有效的方法来处理从现代药物发现产生的大量数据，建模小分子药物、基因生物标记物和识别各种疾病的新药物靶点。

关键词: 机器学习、人工智能、大数据、虚拟筛选、精准医学、药物发现

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