title: Cheminformatic models based on machine learning for pyruvate kinase inhibitors of Leishmania mexicana.
abstract: BACKGROUND
  Leishmaniasis is a neglected tropical disease which affects approx. 12 million individuals worldwide and caused by parasite Leishmania. The current drugs used in the treatment of Leishmaniasis are highly toxic and has seen widespread emergence of drug resistant strains which necessitates the need for the development of new therapeutic options. The high throughput screen data available has made it possible to generate computational predictive models which have the ability to assess the active scaffolds in a chemical library followed by its ADME/toxicity properties in the biological trials.
  
  
  RESULTS
  In the present study, we have used publicly available, high-throughput screen datasets of chemical moieties which have been adjudged to target the pyruvate kinase enzyme of L. mexicana (LmPK). The machine learning approach was used to create computational models capable of predicting the biological activity of novel antileishmanial compounds. Further, we evaluated the molecules using the substructure based approach to identify the common substructures contributing to their activity.
  
  
  CONCLUSIONS
  We generated computational models based on machine learning methods and evaluated the performance of these models based on various statistical figures of merit. Random forest based approach was determined to be the most sensitive, better accuracy as well as ROC. We further added a substructure based approach to analyze the molecules to identify potentially enriched substructures in the active dataset. We believe that the models developed in the present study would lead to reduction in cost and length of clinical studies and hence newer drugs would appear faster in the market providing better healthcare options to the patients.
authors.affiliation: GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology, Mall Road, Delhi, 110007, India. vinods@igib.in.
authors.forename: Salma
authors.initials: S
authors.lastname: Jamal
authors.forename: Vinod
authors.initials: V
authors.lastname: Scaria
mesh_terms: D000465:Algorithms; D000981:Antiprotozoal Agents / Q000737:chemistry* / Q000191:economics / Q000627:therapeutic use*; D001185:Artificial Intelligence / Q000191:economics; D003198:Computer Simulation / Q000191:economics; D055808:Drug Discovery / Q000191:economics; D006801:Humans; D007894:Leishmania mexicana / Q000201:enzymology*; D007896:Leishmaniasis / Q000188:drug therapy* / Q000201:enzymology / Q000453:epidemiology; D011237:Predictive Value of Tests; D011770:Pyruvate Kinase / Q000037:antagonists & inhibitors* / Q000737:chemistry* / Q000191:economics; D054852:Small Molecule Libraries / Q000737:chemistry / Q000191:economics / Q000627:therapeutic use
chemical_list: D000981:Antiprotozoal Agents; D054852:Small Molecule Libraries; D011770:Pyruvate Kinase