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

Frontiers in Robotics and AI

Kathia Maria Honorio¹  Alberico B. F. da Silva²  Celio F. Lipinski²  Patricia R. Oliveira³  Vinicius G. Maltarollo⁴ 

[1] Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, Brazil;Departamento de Química e Física Molecular, Instituto de Química de São Carlos, Universidade de São Paulo, São Carlos, Brazil;Escola de Artes, Ciências e Humanidades, Universidade de São Paulo, São Paulo, Brazil;Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil;

关键词: artificial intelligence;    deep learning;    medicinal chemistry;    drug design;    drug discovery;   

DOI  :  10.3389/frobt.2019.00108

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown