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

Autor(es)

Lipinski, Celio F. ^[1] ; Maltarollo, Vinicius G. ^[2] ; Oliveira, Patricia R. ^[3] ; da Silva, Alberico B. F. ^[1] ; Honorio, Kathia Maria ^{[4, 3]}

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Quim Sao Carlos, Dept Quim & Fis Mol, Sao Carlos, SP - Brazil

^[2] Univ Fed Minas Gerais, Fac Farm, Belo Horizonte, MG - Brazil

^[3] Univ Sao Paulo, Escola Artes Ciencias & Humanidades, Sao Paulo, SP - Brazil

^[4] Univ Fed ABC, Ctr Ciencias Nat & Humanas, Santo Andre, SP - Brazil

• Tipo de documento: Artigo de Revisão
• Fonte: FRONTIERS IN ROBOTICS AND AI; v. 6, NOV 5 2019.
• Citações Web of Science: 0

Resumo

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. (AU)

• Processo FAPESP: 16/24524-7 - Análise estrutural e estudos de modelagem molecular para ligantes de origem natural e sintética relacionados a doenças negligenciadas
• Beneficiário: Kathia Maria Honorio
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 16/18840-3 - Aplicação de técnicas de transferência de aprendizagem e ontologias a modelos QSAR para regressão
• Beneficiário: Patrícia Rufino Oliveira
• Linha de fomento: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE