Bibliographic Details
| Title: |
First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa. |
| Authors: |
Turon, Gemma, Hlozek, Jason, Woodland, John G., Kumar, Ankur, Chibale, Kelly, Duran-Frigola, Miquel |
| Source: |
Nature Communications; 9/15/2023, Vol. 14 Issue 1, p1-11, 11p |
| Subject Terms: |
DRUG discovery, RESOURCE-limited settings, ARTIFICIAL intelligence, ARTIFICIAL membranes, MACHINE learning, CARDIOTOXICITY |
| Geographic Terms: |
SOUTH Africa, AFRICA |
| Abstract: |
Streamlined data-driven drug discovery remains challenging, especially in resource-limited settings. We present ZairaChem, an artificial intelligence (AI)- and machine learning (ML)-based tool for quantitative structure-activity/property relationship (QSAR/QSPR) modelling. ZairaChem is fully automated, requires low computational resources and works across a broad spectrum of datasets. We describe an end-to-end implementation at the H3D Centre, the leading integrated drug discovery unit in Africa, at which no prior AI/ML capabilities were available. By leveraging in-house data collected over a decade, we have developed a virtual screening cascade for malaria and tuberculosis drug discovery comprising 15 models for key decision-making assays ranging from whole-cell phenotypic screening and cytotoxicity to aqueous solubility, permeability, microsomal metabolic stability, cytochrome inhibition, and cardiotoxicity. We show how computational profiling of compounds, prior to synthesis and testing, can inform progression of frontrunner compounds at H3D. This project is a first-of-its-kind deployment at scale of AI/ML tools in a research centre operating in a low-resource setting. Streamlined data-driven drug discovery remains challenging, especially in resource-limited settings. Here, the authors present ZairaChem, an AI/ML tool that streamlines QSAR/QSPR modelling, implemented for the first time at the H3D Centre in South Africa. [ABSTRACT FROM AUTHOR] |
|
Copyright of Nature Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |
