RESUMO
Human Respiratory Syncytial Virus (hRSV) infection results in death and hospitalization of thousands of people worldwide each year. Unfortunately, there are no vaccines or specific treatments for hRSV infections. Screening hundreds or even thousands of promising molecules is a challenge for science. We integrated biological, structural, and physicochemical properties to train and to apply the concept of artificial intelligence (AI) able to predict flavonoids with potential anti-hRSV activity. During the training and simulation steps, the AI produced results with hit rates of more than 83%. The better AIs were able to predict active or inactive flavonoids against hRSV. In the future, in vitro and/or in vivo evaluations of these flavonoids may accelerate trials for new anti-RSV drugs, reduce hospitalizations, deaths, and morbidity caused by this infection worldwide, and be used as input in these networks to determine which parameter is more important for their decision.
Assuntos
Infecções por Vírus Respiratório Sincicial , Vírus Sincicial Respiratório Humano , Humanos , Inteligência Artificial , Infecções por Vírus Respiratório Sincicial/diagnóstico , Infecções por Vírus Respiratório Sincicial/tratamento farmacológicoRESUMO
Human Respiratory Syncytial Virus (hRSV) infection results in death and hospitalization of thousands of people worldwide each year. Unfortunately, there are no vaccines or specific treatments for hRSV infections. Screening hundreds or even thousands of promising molecules is a challenge for science. We integrated biological, structural, and physicochemical properties to train and to apply the concept of artificial intelligence (AI) able to predict flavonoids with potential anti-hRSV activity. During the training and simulation steps, the AI produced results with hit rates of more than 83%. The better AIs were able to predict active or inactive flavonoids against hRSV. In the future, in vitro and/or in vivo evaluations of these flavonoids may accelerate trials for new anti-RSV drugs, reduce hospitalizations, deaths, and morbidity caused by this infection worldwide, and be used as input in these networks to determine which parameter is more important for their decision.
A infecção pelo Vírus Sincicial Respiratório Humano (hRSV) resulta na morte e hospitalização de milhares de pessoas em todo o mundo a cada ano. Infelizmente, não existem vacinas ou tratamentos específicos para tais infecções. A testagem de centenas, ou mesmo milhares, de moléculas promissoras é um desafio para a ciência. Neste trabalho, nós integramos propriedades biológicas, estruturais e físico-químicas para treinar e aplicar o conceito de inteligência artificial (IA) capaz de prever flavonoides com potencial atividade anti-hRSV. Durante as etapas de treinamento e simulação, a IA produziu resultados com taxas de acerto superiores a 83%, sendo capaz de prever flavonoides ativos ou inativos contra o hRSV. No futuro, avaliações in vitro e/ou in vivo desses flavonoides poderão acelerar os testes de novas drogas anti-RSV, reduzir hospitalizações, mortes e morbidade causadas por essa infecção. Além disso, a validação futura destes dados poderá determinar qual parâmetro tem maior peso na decisão da inteligência.