化学信息学和药物化学中的机器学习。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2022-04-19 DOI:10.1146/annurev-biodatasci-122120-124216
Raquel Rodríguez-Pérez, Filip Miljković, J. Bajorath
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引用次数: 6

摘要

在化学信息学和药物化学中,机器学习已经发展成为一种重要的方法。近年来,越来越多的计算资源和新的深度学习算法将机器学习提升到一个新的水平,解决了制药研究中以前未遇到的挑战。随着新算法的发展和大数据的出现,化合物活性预测、从头设计和反应建模的计算机方法得到了进一步的发展。本文综述了机器学习和深度学习在化学信息学和药物化学中的新应用。讨论了新方法和应用程序的机遇和挑战,重点放在适当的基线比较,稳健的验证方法和新的适用领域。预计《生物医学数据科学年度评论》第5卷的最终在线出版日期为2022年8月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
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Machine Learning in Chemoinformatics and Medicinal Chemistry.
In chemoinformatics and medicinal chemistry, machine learning has evolved into an important approach. In recent years, increasing computational resources and new deep learning algorithms have put machine learning onto a new level, addressing previously unmet challenges in pharmaceutical research. In silico approaches for compound activity predictions, de novo design, and reaction modeling have been further advanced by new algorithmic developments and the emergence of big data in the field. Herein, novel applications of machine learning and deep learning in chemoinformatics and medicinal chemistry are reviewed. Opportunities and challenges for new methods and applications are discussed, placing emphasis on proper baseline comparisons, robust validation methodologies, and new applicability domains. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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