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Bosc2021 - MAIP: a web service for predicting blood‐stage malaria inhibitors

ABSTRACT: Prediction of the antimalarial potential of small molecules. This model is an ensemble of smaller QSAR models trained on proprietary data from various sources, up to a total of >7M compounds. The training sets belong to Evotec, Johns Hopkins, MRCT, MMV - St. Jude, AZ, GSK, and St. Jude Vendor Library. The code and training data are not released, using this model posts predictions to the MAIP online server. The Ersilia Model Hub also offers MAIP-surrogate as a downloadable package for IP-sensitive queries. Model Type: Predictive machine learning model. Model Relevance: Predicts antimalarial activity. Model Encoded by: Amna Ali (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos4zfy

SUBMITTER: Zainab Ashimiyu-Abdusalam  

PROVIDER: MODEL2405210002 | BioModels | 2024-06-17

REPOSITORIES: BioModels

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Publications

Malaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify n  ...[more]

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