NDLI: Predictive modeling of anti-malarial molecules inhibiting apicoplast formation

Content Provider	Springer Nature : BioMed Central
Author	Jamal, Salma Periwal, Vinita Scaria, Vinod
Abstract	Background Malaria is a major healthcare problem worldwide resulting in an estimated 0.65 million deaths every year. It is caused by the members of the parasite genus Plasmodium. The current therapeutic options for malaria are limited to a few classes of molecules, and are fast shrinking due to the emergence of widespread resistance to drugs in the pathogen. The recent availability of high-throughput phenotypic screen datasets for antimalarial activity offers a possibility to create computational models for bioactivity based on chemical descriptors of molecules with potential to accelerate drug discovery for malaria. Results In the present study, we have used high-throughput screen datasets for the discovery of apicoplast inhibitors of the malarial pathogen as assayed from the delayed death response. We employed machine learning approach and developed computational predictive models to predict the biological activity of new antimalarial compounds. The molecules were further evaluated for common substructures using a Maximum Common Substructure (MCS) based approach. Conclusions We created computational models using state-of-the-art machine learning algorithms. The models were evaluated based on multiple statistical criteria. We found Random Forest based approach provides for better accuracy as assessed from ROC curve analysis. We further evaluated the active molecules using a substructure based approach to identify common substructures enriched in the active set. We argue that the computational models generated could be effectively used to screen large molecular datasets to prioritize them for phenotypic screens, drastically reducing cost while improving the hit rate.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-14-55.pdf
Ending Page	8
Page Count	8
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/1471-2105-14-55
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	14
Language	English
Publisher	BioMed Central
Publisher Date	2013-02-15
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Malaria Random Forest Area Under Curve Misclassification Cost Receiver Operating Characteristic Curve Curve Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

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