关键词： ADMET, Absorption, distribution, metabolism, elimination and toxicity ADR, Adverse Drug Reaction AI, Artificial Intelligence ANN, Artificial Neural Networks APFP, Atom Pairs 2d FingerPrint AUC, Area under the Curve BBB, Blood–Brain barrier CDK, Chemical Development Kit CNN, Convolutional Neural Networks CNS, Central Nervous System CPI, Compound-protein interaction CV, Cross Validation Cheminformatics DL, Deep Learning DNA, Deoxyribonucleic acid Deep Learning Drug Discovery ECFP, Extended Connectivity Fingerprints FDA, Food and Drug Administration FNN, Fully Connected Neural Networks FP, Fringerprints FS, Feature Selection GCN, Graph Convolutional Networks GEO, Gene Expression Omnibus GNN, Graph Neural Networks GO, Gene Ontology KEGG, Kyoto Encyclopedia of Genes and Genomes MACCS, Molecular ACCess System MCC, Matthews correlation coefficient MD, Molecular Descriptors MKL, Multiple Kernel Learning ML, Machine Learning Machine Learning Molecular Descriptors NB, Naive Bayes OOB, Out of Bag PCA, Principal Component Analyisis QSAR QSAR, Quantitative structure–activity relationship RF, Random Forest RNA, Ribonucleic Acid SMILES, simplified molecular-input line-entry system SVM, Support Vector Machines TCGA, The Cancer Genome Atlas WHO, World Health Organization t-SNE, t-Distributed Stochastic Neighbor Embedding

来  源：   DOI：10.1016/j.csbj.2021.08.011   PDF(Sci-hub)   PDF(Pubmed)

Abstract：
Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.
摘要：
药物发现旨在寻找具有特定化学性质的用于治疗疾病的新化合物。在过去的几年里，在这个搜索中使用的方法提出了一个重要的组成部分，在计算机科学与机器学习技术的飞涨，由于其民主化。随着精准医学计划设定的目标和产生的新挑战，有必要建立健壮的，实现既定目标的标准和可重复的计算方法。目前,基于机器学习的预测模型在临床前研究之前的步骤中已经变得非常重要。这一阶段设法大大减少了发现新药的成本和研究时间。这篇综述文章的重点是如何在近年来的研究中使用这些新方法。分析该领域的最新技术将使我们了解在短期内化学信息学的发展方向，它所呈现的局限性和所取得的积极成果。这篇综述将主要关注用于对分子数据进行建模的方法，以及近年来解决的生物学问题和用于药物发现的机器学习算法。
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