ArticleJournal of molecular graphics & modelling2012
Teach-Discover-Treat (TDT): collaborative computational drug discovery for neglected diseases.
Article in Journal of molecular graphics & modelling, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Evaluating generalization in protein-ligand cofolding methods.Nature structural & molecular biology · 2026Article
- A fully automated benchmarking suite to compare macromolecular complexes.Nature methods · 2026Review
- The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks.Journal of chemical information and modeling · 2025Review
- New prediction categories in CASP15.Proteins · 2023Article
- CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.Nature reviews. Chemistry · 2022Article
- Towards reproducible computational drug discovery.Journal of cheminformatics · 2020Review
- Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking.Structure (London, England : 1993) · 2019Article
- TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data.Journal of cheminformatics · 2019Article
- Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria.F1000Research · 2017Article
- CSAR Benchmark Exercise 2013: Evaluation of Results from a Combined Computational Protein Design, Docking, and Scoring/Ranking Challenge.Journal of chemical information and modeling · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Teach-Discover-Treat (TDT) is an initiative to promote the development and sharing of computational tools solicited through a competition with the aim to impact education and collaborative drug discovery for neglected diseases. Collaboration, multidisciplinary integration, and innovation are essential for successful drug discovery. This requires a workforce that is trained in state-of-the-art workflows and equipped with the ability to collaborate on platforms that are accessible and free. The TDT competition solicits high quality computational workflows for neglected disease targets, using freely available, open access tools.
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.