Applied Machine Learning Toward Drug Discovery Enhancement: Leishmaniases as a Case Study.

ABSTRACT: Drug discovery (DD) research is a complex field with a high attrition rate. Machine learning (ML) approaches combined to chemoinformatics are of valuable input to this field. We, herein, focused on implementing multiple ML algorithms that shall learn from different molecular fingerprints (FPs) of 65 057 molecules that have been identified as active or inactive against Leishmania major promastigotes. We sought to build a classifier able to predict whether a given molecule has the potential of being anti-leishmanial or not. Using the RDkit library, we calculated 5 molecular FPs of the molecules. Then, we implemented 4 ML algorithms that we trained and tested for their ability to classify the molecules into active/inactive classes based on their chemical structure, encoded by the molec

SUBMITTER: Harigua-Souiai E 

PROVIDER: S-EPMC9036323 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature