Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure-activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers.

中文翻译：

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化学信息学中的机器学习方法。

机器学习算法通常在计算机科学或相邻学科中开发，并通过扩散过程进入化学建模。尽管特定的机器学习方法在化学信息学和定量构效关系 (QSAR) 中很流行，但技术文献中还存在许多其他方法。这个讨论是基于方法的，重点是化学信息学研究人员经常使用的一些算法。它没有声称是详尽无遗的。我们专注于监督学习的方法，根据训练集的已知值预测测试集实例（通常是分子）的未知属性值。特别相关的方法包括人工神经网络、随机森林、支持向量机、k-最近邻和朴素贝叶斯分类器。