Deep Learning-driven research for drug discovery: Tackling Malaria

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...

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Bibliographic Details
Published in:PLoS computational biology 2020-02, Vol.16 (2), p.e1007025-e1007025
Main Authors: Neves, Bruno J, Braga, Rodolpho C, Alves, Vinicius M, Lima, Marília N N, Cassiano, Gustavo C, Muratov, Eugene N, Costa, Fabio T M, Andrade, Carolina Horta
Format: Article
Language:English
Subjects:
Antimalarial agents
Antimalarials
Antimalarials - chemistry
Antimalarials - therapeutic use
Antimicrobial agents
Antiprotozoal agents
Artemisinin
Asexuality
Biology
Biology and Life Sciences
Cells (Biology)
Chemical compounds
Communicable diseases
Computer and Information Sciences
Computer applications
Cytotoxicity
Datasets
Deep Learning
Diseases
Drug development
Drug discovery
Drug Discovery - methods
Drug resistance
Drug therapy
Drugs
Evolution
Fibroblasts
Global health
Health
Humans
Immunology
Infectious diseases
Laboratories
Machine learning
Malaria
Malaria - drug therapy
Mammalian cells
Medicine and Health Sciences
Neural networks
Parasite resistance
Pharmacy
Plasmodium falciparum
Pyrimethamine
Quantitative Structure-Activity Relationship
Reproducibility of Results
Software
Statistical methods
Structure-Activity Relationship
Structure-activity relationships
Supervision
Toxicity
Tropical diseases
Vector-borne diseases
World health
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Summary: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
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1007025