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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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| Published in: | PLoS computational biology 2020-02, Vol.16 (2), p.e1007025-e1007025 |
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| creator | 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 |
| description | 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 |
| doi_str_mv | 10.1371/journal.pcbi.1007025 |
| format | article |
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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. 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| 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 |
| title | Deep Learning-driven research for drug discovery: Tackling Malaria |
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