ArticleJournal of computer-aided molecular design2014
Bigger data, collaborative tools and the future of predictive drug discovery.
Article in Journal of computer-aided molecular design, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- MolPredictX: A Pioneer Mobile App Version for Online Biological Activity Predictions by Machine Learning Models.Methods in molecular biology (Clifton, N.J.) · 2025Article
- The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications.Communications chemistry · 2024Article
- Knowledge-based approaches to drug discovery for rare diseases.Drug discovery today · 2022Review
- Metabolomics of clinical samples reveal the treatment mechanism of lanthanum hydroxide on vascular calcification in chronic kidney disease.Proceedings of the Japan Academy. Series B, Physical and biological sciences · 2022Article
- Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery.Molecular pharmaceutics · 2021Article
- Underscoring interstrain variability and the impact of growth conditions on associated antimicrobial susceptibilities in preclinical testing of novel antimicrobial drugs.Critical reviews in microbiology · 2019Review
- Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.Molecular pharmaceutics · 2017Article
- Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).Drug discovery today · 2017Review
- Chembench: A Publicly Accessible, Integrated Cheminformatics Portal.Journal of chemical information and modeling · 2017Article
- The Next Era: Deep Learning in Pharmaceutical Research.Pharmaceutical research · 2016Review
- Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets.Journal of chemical information and modeling · 2015Article
- App-etite for change.Journal of computer-aided molecular design · 2015Article
- A brief review of recent Charcot-Marie-Tooth research and priorities.F1000Research · 2015Review
- Collaboration for rare disease drug discovery research.F1000Research · 2014Article
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
Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.
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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.