机器学习算法在药物筛选中的应用

Ke Jin, Cunqing Rong, Jincai Chang
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引用次数: 0

摘要

目前,在医学领域,药物筛选通常是通过体内药物实验进行的。然而,对大量药物进行体内实验,需要逐一筛选,非常耗时费力。本文试图提出利用机器学习算法对大量待筛选化合物及其分子结构进行初步筛选,以减少体内实验的工作量。其中,国际上公认乳腺癌进展与雌激素受体α亚型有重要关联。具有优异疗效的抗乳腺癌候选药物需要含有能更好地拮抗ERα活性的化合物。本文将研究对象从化合物缩小到化合物的分子结构,然后利用随机森林回归算法建立分子结构- erα活性预测模型。从众多化合物的分子结构描述符中筛选出对生物活性有显著影响的分子结构。采用4种不同的核函数进行对比实验,最终建立基于径向基核函数的支持向量回归算法,实现了化合物对ERα生物活性的定量预测,并能发现潜在的有利于乳腺癌治疗的化合物。这是一种新颖的基于计算机的药物初步筛选方法,可以帮助医学研究人员有效地缩小实验范围,实现更准确的药物优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of machine learning algorithms in drug screening
At present, in the medical field, drug screening is usually performed using in vivo drug experiments. However, it is very time-consuming and laborious to conduct in vivo experiments on a large number of drugs to be screened one by one. This paper attempts to propose using machine learning algorithms to perform preliminary screening of a large number of compounds to be screened and their molecular structures to reduce the workload of in vivo experiments. Among them, it is internationally recognized that there is an important association between breast cancer progression and the alpha subtype of the estrogen receptor. Anti-breast cancer drug candidates with excellent efficacy need to contain compounds that can better antagonize ERα activity. In this paper, the research object is narrowed down from compounds to the molecular structure of the compounds, and then the random forest regression algorithm is used to develop the molecular structure-ERα activity prediction model. Molecular structures with significant effects on biological activity were screened from molecular structure descriptors in numerous compounds. Four different kernel functions were used to conduct comparative experiments, and finally a support vector regression algorithm based on radial basis kernel function was established, which realized the quantitative prediction of compounds on biological activity of ERα, and could find potential compounds beneficial to breast cancer treatment. This is a novel, computer-based method for preliminary drug screening, which can help medical researchers effectively narrow the scope of experiments and achieve more accurate optimization of drugs.
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