First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa

Abstract

We present ZairaChem, an artificial intelligence (AI)- and machine learning (ML)-based tool to train small-molecule activity prediction models. ZairaChem is fully automated, requires low computational resources and works across a broad spectrum of datasets, ranging from whole-cell growth inhibition assays to drug metabolism properties. The tool has been implemented end-to-end at the Holistic Drug Discovery and Development (H3D) Centre, the leading integrated drug discovery unit in Africa, at which no prior AI/ML capabilities were available. We have exploited in-house data collected from over a decade of drug discovery research in malaria and tuberculosis and built models to predict the outcomes of 15 key checkpoint assays. We subsequently deployed these models as a virtual screening cascade at an organisational scale to increase the hit rate of current experimental assays. We show how computational profiling of compounds, prior to synthesis and experimental testing, can increase the rate of progression by up to 40%. Moreover, we demonstrate that the approach can be applied to prioritise small molecules within a chemical series and to assess the likelihood of success of novel chemotypes, promoting efficient usage of limited experimental resources. This project is part of a first-of-its-kind collaboration between the H3D Centre, a research centre operating in a low-resource setting, and the Ersilia Open Source Initiative, a young tech non-profit devoted to building data science capacity in the Global South.

Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery

“…This tool represents a significant advancement in enabling streamlined drug discovery processes, informing compound progression, and facilitating lead identification in resource-limited settings. Through real-world prospective studies, ZairaChem has demonstrated its efficacy in identifying lead-like compounds, showcasing its potential to revolutionize drug discovery efforts [44].…”

Artificial intelligence utility for drug development: ChatGPT and beyond

“…In conclusion, GPT‐based AI tools have great potential to facilitate the complex, expensive, and time‐consuming drug development process by identifying the most promising drug targets and potential lead compounds (Tiwari et al, 2023; Turon et al, 2023). Improved GPT‐based tools will be coming, and the pharma R&D sector will need to identify and prioritize the optimal modes for using them.…”

5-chloro-3-(2-(2,4-dinitrophenyl) hydrazono)indolin-2-one: synthesis, characterization, biochemical and computational screening against SARS-CoV-2

“…Ligand-based methods often do not require a knowledge of the structural features of a receptor site (Ferreira et al 2015;Vazquez et al 2020). These include quantitative structure-activity relationships (QSAR), ligand-based pharmacophore querying methods, and most recently, artificial intelligence/machine learning (AI/ML) models (Selvaraj et al 2022;Subramanian et al 2022;Namba-Nzanguim et al 2022;Turon et al 2023). Among these methods, the most cite is molecular docking and scoring techniques have been proven to be efficient ways of identifying active compounds from an electronic library of compounds by a what is commonly called virtual screening (Morris & Lim-Wilby 2008;Chen 2015;Lohning et al 2017).…”