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Van-Heerden2023 - Chemical Features Identification for Stage-Specific Antimalarial Compounds

ABSTRACT: Prediction of the antimalarial potential of small molecules using data from various chemical libraries that were screened against the asexual and sexual (gametocyte) stages of the parasite. Several compounds’ molecular fingerprints were used to train machine learning models to recognize stage-specific active and inactive compounds. Model Type: Predictive machine learning model. Model Relevance: Probability of inhibition of the malaria parasite growth. Model Encoded by: Gemma Turon (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos80ch

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2403270002 | BioModels | 2024-03-28

REPOSITORIES: BioModels

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Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds.

van Heerden Ashleigh A Turon Gemma G Duran-Frigola Miquel M Pillay Nelishia N Birkholtz Lyn-Marié LM

ACS omega 20231107 46

Efficacy data from diverse chemical libraries, screened against the various stages of the malaria parasite <i>Plasmodium falciparum</i>, including asexual blood stage (ABS) parasites and transmissible gametocytes, serve as a valuable reservoir of information on the chemical space of compounds that are either active (or not) against the parasite. We postulated that this data can be mined to define chemical features associated with the sole ABS activity and/or those that provide additional life cy ...[more]

PMID: 38027377

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