ReviewCommunications medicine2025
Addressing infectious diseases in Africa by accelerating drug discovery through data science.
Review in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Bridging the AI Divide in Drug Discovery: Practical Lessons from Capacity Strengthening in Africa.Journal of medicinal chemistry · 2026Article
- Artificial intelligence for coordinating vaccine design, antiviral discovery, and real-world monitoring in the era of emerging and endemic viral threats.Frontiers in pharmacology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Despite being rich in natural resources and scientific talent, Africa continues to bear a staggering infectious disease burden. Historically, health innovation on the continent has relied on international funding and has been constrained by limited infrastructure and the emigration of skilled professionals. Data science tools offer a promising alternative, typically requiring fewer costly resources than traditional empirical research, with the potential to empower African scientists to generate tangible and impactful health solutions for the continent. Rapid progress in data science is expected to transform infectious disease research; thus, it is encouraging that numerous African initiatives are already applying data science tools to tackling pressing unmet medical needs, particularly in drug discovery. These efforts include identifying novel therapeutic targets, predicting drug-like molecules and their synthesis, enhancing clinical trial success rates and preparing for future disease threats. This review examines the current landscape of data science in infectious disease drug discovery across Africa.
Identifiers
What OpenQuestion holds
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.