ArticleNature communications2023
First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
33 citing papers in PubMed, 68 citations in OpenAlex.
- New thinking for the next generation of antimalarials.EMBO molecular medicine · 2026Review
- PCA-mapping and machine learning identify multiple chemotypes with antiplasmodial, β-haematin and haemozoin formation inhibition activity.RSC medicinal chemistry · 2026Article
- Bridging the AI Divide in Drug Discovery: Practical Lessons from Capacity Strengthening in Africa.Journal of medicinal chemistry · 2026Article
- An End-User Audit of Reproducibility, Data Leakage, and Overfitting of the Top-Ranked ADMET Prediction Models in TDC Leaderboards.Journal of chemical information and modeling · 2026Article
- The use of artificial intelligence in advancing molecular biology in Africa: a narrative review.Molecular genetics and genomics : MGG · 2026Review
- Enhancing ADMET property predictions using cross-aligned multimodal attention mechanisms.Molecular diversity · 2026Article
- De Novo Molecular Design and Bioactivity Prediction of Novel Hexahydroquinolines asBioinformatics and biology insights · 2026Article
- Drugs against broad-spectrum of coronaviruses.Frontiers in immunology · 2026Review
- Scoping review of artificial intelligence via mobile technology and social media for health in Africa.Nature communications · 2025Article
- Artificial intelligence in human immunodeficiency virus mutation prediction and drug design: Advancing personalized treatment and prevention.Pharmaceutical science advances · 2025Review
- Addressing infectious diseases in Africa by accelerating drug discovery through data science.Communications medicine · 2025Review
- Artificial intelligence coupled to pharmacometrics modelling to tailor malaria and tuberculosis treatment in Africa.Nature communications · 2025Article
- Ophthalmic drug discovery and development using artificial intelligence and digital health technologies.NPJ digital medicine · 2025Review
- Harnessing AI-driven reverse docking in drug discovery: a comprehensive review of opportunities, challenges, and emerging trends.Journal of molecular modeling · 2025Review
- Integration of AI and ML in Tuberculosis (TB) Management: From Diagnosis to Drug Discovery.Diseases (Basel, Switzerland) · 2025Review
- MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery.Journal of chemical information and modeling · 2025Article
- Identification of acrylamide-based covalent inhibitors of SARS-CoV-2 (SCoV-2) Nsp15 using high-throughput screening and machine learning.RSC advances · 2025Article
- Review
- Virtual screening: hope, hype, and the fine line in between.Expert opinion on drug discovery · 2025Review
- Artificial intelligence in drug development.Nature medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.