Deep Learning-driven research for drug discovery: Tackling Malaria
Bruno Junior Neves, Rodolpho C. Braga, Vinícius M. Alves, Marília N. N. Lima, Gustavo Capatti Cassiano, Eugene N. Muratov, Fábio Trindade Maranhão Costa, Carolina Horta Andrade · PLoS Computational Biology · 2020
Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually.Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority.Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds.Then, we applied the best models for a virtual screening of a large database of chemical compounds.The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains.Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC 50 <500 nM) and low cytotoxicity in mammalian cells.Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates. Author summaryMalaria is a serious infectious disease caused by parasites of the genus Plasmodium.The recommended treatment is a combination of antimalarial drugs.However, the rise of parasites resistant to the current antimalarial drugs means that new therapeutics are continually required.To meet this challenge, we developed and applied models using deep learning, a powerful artificial intelligence method, supported by experimental validation