Rodriguez Perez, Raquel, Miljkovic, Filip and Bajorath, Jürgen (2022) Machine Learning in Chemoinformatics and Medicinal Chemistry. Annual review of biomedical data science. ISSN 2574-3414

Abstract

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.

Item Type:	Article
Date Deposited:	06 May 2022 00:45
Last Modified:	06 May 2022 00:45
URI:	https://oak.novartis.com/id/eprint/45768