Artificial Neural Network Methods Applied to Drug Discovery for Neglected Diseases

Author(s): Luciana Scotti, Hamilton Ishiki, Francisco J.B. Mendonça Júnior, Marcelo S. da Silva, Marcus T. Scotti

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 18 , Issue 8 , 2015

Become EABM
Become Reviewer


Among the chemometric tools used in rational drug design, we find artificial neural network methods (ANNs), a statistical learning algorithm similar to the human brain, to be quite powerful. Some ANN applications use biological and molecular data of the training series that are inserted to ensure the machine learning, and to generate robust and predictive models. In drug discovery, researchers use this methodology, looking to find new chemotherapeutic agents for various diseases. The neglected diseases are a group of tropical parasitic diseases that primarily affect poor countries in Africa, Asia, and South America. Current drugs against these diseases cause side effects, are ineffective during the chronic stages of the disease, and are often not available to the needy population, have relative high toxicity, and face developing resistance. Faced with so many problems, new chemotherapeutic agents to treat these infections are much needed. The present review reports on neural network research, which studies new ligands against Chagas’ disease, sleeping sickness, malaria, tuberculosis, and leishmaniasis; a few of the neglected diseases.

Keywords: Artificial neural network, Chagas’ disease, chemometrics tools, drug discovery, leishmaniasis, malaria, sleeping sickness, tuberculosis.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2015
Page: [819 - 829]
Pages: 11
DOI: 10.2174/1386207318666150803141219
Price: $65

Article Metrics

PDF: 24