ArticleJournal of the Royal Society, Interface2015
Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases.
Article in Journal of the Royal Society, Interface, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 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
48 citing papers in PubMed, 157 citations in OpenAlex.
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- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- The ADePT framework for assessing autonomous laboratory robotics.Communications chemistry · 2026Review
- Automated Biomedical Research Laboratories: Development, Current State, and a Roadmap for Adaptation into Shared Research Resources.Journal of biomolecular techniques : JBT · 2026Review
- Computer-aided drug discovery: historical foundations, practical AI tools, and emerging ethical considerations.Frontiers in pharmacology · 2026Review
- Innovative horizons: harnessing drug repositioning for targeted therapeutics in colorectal cancer.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- Optimized Machine Learning for Autonomous Enzymatic Reaction Intensification in a Self-Driving Lab.Biotechnology and bioengineering · 2025Article
- Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Autonomous 'self-driving' laboratories: a review of technology and policy implications.Royal Society open science · 2025Review
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- Collective predictive coding as model of science: formalizing scientific activities towards generative science.Royal Society open science · 2025Article
- Artificial Intelligence and Predictive Modeling in the Management and Treatment of Episodic Migraine.Current pain and headache reports · 2025Review
- Automating the practice of science: Opportunities, challenges, and implications.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Responsible AI in biotechnology: balancing discovery, innovation and biosecurity risks.Frontiers in bioengineering and biotechnology · 2025Review
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting.Journal of the American Society for Mass Spectrometry · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 6 institutions in 4 countries.
Funding
Abstract
There is an urgent need to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs. Here, we report the Robot Scientist 'Eve' designed to make drug discovery more economical. A Robot Scientist is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. Eve integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing. Using econometric modelling we demonstrate that the use of AI to select compounds economically outperforms standard drug screening. For further efficiency Eve uses a standardized form of assay to compute Boolean functions of compound properties. These assays can be quickly and cheaply engineered using synthetic biology, enabling more targets to be assayed for a given budget. Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax.
Indexed as
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.