Aprendizado de máquina e big data na descoberta de novos medicamentos: um mapeamento sistemático
DOI:
https://doi.org/10.34117/bjdv9n8-091Keywords:
aprendizado de máquina, big data, medicamentosAbstract
A descoberta e o desenvolvimento de medicamentos é um processo demorado e custoso. Muitos esforços de pesquisa recentes geraram enormes quantidades de dados que mudaram a forma de se proceder a descoberta de novos medicamentos para uma era de big data. Neste contexto, abordagens de aprendizado de máquina têm sido aplicadas permitindo a redução de ensaios laboratoriais e consequente redução no tempo e custo do desenvolvimento de medicamentos. Neste trabalho, por meio de um mapeamento sistemático da literatura recente, foi avaliado o uso do aprendizado de máquina aliado ao contexto do big data na descoberta e desenvolvimento de novos medicamentos. Considerando os últimos cinco anos e utilizando uma consulta incluindo os termos machine learning, big data e drug discovery, 106 artigos científicos foram encontrados na base de dados do PubMed e, dentre eles, 16 artigos foram considerados elegíveis para leitura completa e extração de informações. Embora tenha sido identificado o uso de alguns algoritmos tradicionais de aprendizado de máquina, algoritmos de aprendizado profundo aparecem como os mais indicados ao auxílio do desenvolvimento de medicamentos no contexto do big data. Além disso, foram identificadas dez características intrínsecas ao big data e suas principais bases de dados. Por fim, as atividades de priorização de medicamentos utilizando QSAR, triagem virtual, otimização ADMET, reaproveitamento de medicamentos, desenho de novo de medicamentos, e interações medicamento-alvo e proteína-proteína foram identificadas como as mais ativas no escopo considerado.
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