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Mini Review ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Robot. AI | doi: 10.3389/frobt.2019.00108

Advances and Perspectives in Applying Deep Learning for Drug Design and Discovery

  • 1Institute of Chemistry of São Carlos, University of São Paulo, Brazil
  • 2Federal University of Minas Gerais, Brazil
  • 3School of Arts, Sciences and Humanities, University of São Paulo, Brazil

Discovering (or planning) a new drug candidate involves many parameters, which makes this process slow, costly and leading to failures at the end in some cases. In the last decades, we have witnessed a revolution in the computational area (hardware, software, large-scale computing, etc.), as well as an explosion in data generation (big data), which raises the need for more sophisticated algorithms to analyze this myriad of data. In this scenario, we can highlight the potentialities of artificial intelligence (AI) or computational intelligence (CI) as a powerful tool to analyze medicinal chemistry data. According to IEEE, computational intelligence involves the theory, the design, the application and the development of biologically and linguistically motivated computational paradigms. In addition, CI encompasses three main methodologies: neural networks (NN), fuzzy systems and evolutionary computation. In particular, artificial neural networks have been successfully applied in medicinal chemistry studies. A branch of the NN area that has attracted a lot of attention refers to deep learning (DL) due to its generalization power and ability to extract features from data. Therefore, in this mini-review we will briefly outline the present scope, advances and challenges related to the use of DL in drug design and discovery, describing successful studies involving quantitative structure-activity relationships (QSAR) and virtual screening (VS) of databases containing thousands of compounds.

Keywords: artificial intelligence, Computational Intelligence, deep learning, Medicinal Chemistry, Drug design-

Received: 06 May 2019; Accepted: 11 Oct 2019.

Copyright: © 2019 Lipinski, Maltarollo, Oliveira, da Silva and Honorio. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Kathia Honorio, School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, 03828-000, São Paulo, Brazil, kmhonorio@usp.br