Abstract

Antimalarial drugs are becoming less effective due to the emergence of drug resistance. At this time, resistance has been reported for all available antimalarial marketed drugs, including artemisinin, thus creating a perpetual need for alternative drug candidates. The traditional drug discovery approach of high throughput screening (HTS) of large compound libraries for identification of new drug leads is time-consuming and resource-intensive. While virtual screening, which enables finding drug candidates in-silico, is one solution to this problem, the accuracy of these models is limited. Artificial intelligence (AI) however has demonstrated highly accurate performances in chemical property prediction utilizing either structure-based or ligand-based approaches. Leveraging this ability and the existing models, AI could be a suitable alternative to blind-search HTS or feature-based virtual screening. This model would recognize patterns within data and allow the search for hit compounds to be done in an intelligent manner. In this work, we introduce DeepMalaria, a deep-learning-based process capable of predicting the anti-plasmodial properties and parasite to human selectivity of compounds from their SMILES. This graph-based model is trained on nearly 13,000 publicly available antiplasmodial compounds from GlaxoSmithKline (GSK) which are currently being used to find novel antimalarial drug candidates. We used this model for predicting hit compounds from a macrocyclic based compound library. To validate the DeepMalaria generated hits, we utilized the widely used SYBR Green I fluorescence-based phenotypic screening. DeepMalaria was able to predict all compounds that showed nanomolar activity and 87.5% of the compounds with an inhibition rate of 50% or more at 1 µM. Further experiments to reveal the compounds' mechanism of action has shown us that one of the hit compounds, DC-9237, inhibits all intraerythrocytic asexual stages of Plasmodium falciparum, and is a fast-acting compound, making it a strong candidate for further optimization.

Notes

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Graduation Date

2019

Semester

Summer

Advisor

Chakrabarti, Debopam

Degree

Master of Science (M.S.)

College

College of Medicine

Department

Biomedical Sciences

Degree Program

Biotechnology

Format

application/pdf

Identifier

CFE0008090; DP0023229

URL

https://purls.library.ucf.edu/go/DP0023229

Language

English

Release Date

February 2025

Length of Campus-only Access

5 years

Access Status

Masters Thesis (Campus-only Access)

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