A review on machine learning approaches and trends in drug discovery.
A review on machine learning approaches and trends in drug discovery.
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DOI：
10.1016/j.csbj.2021.08.011
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发表时间：
2021
影响因子：
6
通讯作者：
Fernandez-Lozano C
中科院分区：
文献类型：
--
作者：
Carracedo-Reboredo P;Liñares-Blanco J;Rodríguez-Fernández N;Cedrón F;Novoa FJ;Carballal A;Maojo V;Pazos A;Fernandez-Lozano C
药物发现中的机器学习极大地造福了制药行业。机器算法的应用必须在实际的临床任务中进行稳健的设计。药物设计中的趋势机器学习算法：NB， SVM， RF和ANN。药物发现的目的是寻找具有特殊化学性质的新化合物来治疗疾病。在过去的几年里，由于机器学习技术的民主化，这种搜索中使用的方法呈现出计算机科学中一个重要的组成部分。随着精准医疗计划设定的目标和产生的新挑战，有必要建立健全、标准和可重复的计算方法来实现设定的目标。目前，基于机器学习的预测模型在临床前研究中已经发挥了重要作用。这一阶段大大降低了发现新药的成本和研究时间。这篇综述文章主要关注这些新方法在近年来的研究中是如何被使用的。分析这一领域的技术状况将使我们了解化学信息学在短期内的发展方向，它所存在的局限性以及它所取得的积极成果。本文将重点介绍近年来用于分子数据建模的方法，以及所解决的生物学问题和用于药物发现的机器学习算法。
Machine Learning in drug discovery has greatly benefited the pharmaceutical industry. Application of machine algorithms must entail a robust design in real clinical tasks. Trending machine learning algorithms in drug design: NB, SVM, RF and ANN. 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.
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影响因子：
8.6
作者：
Burggraaff, Lindsey;Oranje, Paul;van Westen, Gerard J. P.
通讯作者：
van Westen, Gerard J. P.
小分子与结合相关 S-亚硝基化位点之间关联的计算预测和分析
DOI：
10.3390/molecules23040954
发表时间：
2018-04-19
期刊：
Molecules (Basel, Switzerland)
影响因子：
--
作者：
Huang G;Li J;Zhao C
通讯作者：
Zhao C
影响因子：
64.8
作者：
Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
Aparicio, Samuel
影响因子：
4.8
作者：
Burges, CJC
通讯作者：
Burges, CJC
影响因子：
3.6
作者：
Brito-Sanchez, Yoan;Marrero-Ponce, Yovani;Cherkasov, Artem
通讯作者：
Cherkasov, Artem