Deep Learning-driven Research for Drug Discovery: Tackling Malaria

Overview

Journal PLoS Comput Biol

Specialty Biology

Date 2020 Feb 19

PMID 32069285

Citations 19

Authors

Bruno J Neves

Rodolpho C Braga

Vinicius M Alves

Marilia N N Lima

Gustavo C Cassiano

Eugene N Muratov

Fabio T M Costa

Carolina Horta Andrade

Affiliations

Soon will be listed here.

Abstract

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 (EC50 <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.

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