ArticleJournal of cheminformatics2019
TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data.
Article in Journal of cheminformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- Transformers for molecular property prediction: domain adaptation efficiently improves performance.Journal of cheminformatics · 2026Article
- Centralized Review of Alzheimer's Disease and Related Dementias Biomedical Repositories and Computational Methods.Bioengineering (Basel, Switzerland) · 2026Review
- Open-Source Molecular Docking and AI-Augmented Structure-Based Drug Design: Current Workflows, Challenges, and Opportunities.International journal of molecular sciences · 2026Review
- Computational Discovery and Optimization of a Potent, Selective, and Drug-Like Scaffold for p38α Inhibition.Drug design, development and therapy · 2026Article
- eRMSF: A Python Package for Ensemble-Based RMSF Analysis of Biomolecular Systems.Journal of chemical information and modeling · 2025Article
- Python tools for structural tasks in chemistry.Molecular diversity · 2025Review
- Machine learning-based screening and molecular simulations for discovering novel PARP-1 inhibitors targeting DNA repair mechanisms for breast cancer therapy.Molecular diversity · 2025Article
- Machine learning: Python tools for studying biomolecules and drug design.Molecular diversity · 2025Review
- Dynamics understanding of novel solvated drug molecules against emergingResults in chemistry · 2025Article
- Application of Deep Learning to Predict the Persistence, Bioaccumulation, and Toxicity of Pharmaceuticals.Journal of chemical information and modeling · 2025Article
- Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders.Scientific reports · 2025Article
- AI-assisted discovery of potent FGFR1 inhibitors via virtual screening and in silico analysis.PloS one · 2025Article
- Article
- Exploring SureChEMBL from a drug discovery perspective.Scientific data · 2024Article
- An AlphaFold Structure Analysis of COQ2 as Key a Component of the Coenzyme Q Synthesis Complex.Antioxidants (Basel, Switzerland) · 2024Article
- Targeting XGHPRT enzyme to manageSaudi journal of biological sciences · 2024Article
- Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors.Journal of cheminformatics · 2023Article
- An integrated computational approach towards novel drugs discovery against polyketide synthase 13 thioesterase domain of Mycobacterium tuberculosis.Scientific reports · 2023Article
- The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Owing to the increase in freely available software and data for cheminformatics and structural bioinformatics, research for computer-aided drug design (CADD) is more and more built on modular, reproducible, and easy-to-share pipelines. While documentation for such tools is available, there are only a few freely accessible examples that teach the underlying concepts focused on CADD, especially addressing users new to the field. Here, we present TeachOpenCADD, a teaching platform developed by students for students, using open source compound and protein data as well as basic and CADD-related Python packages. We provide interactive Jupyter notebooks for central CADD topics, integrating theoretical background and practical code. TeachOpenCADD is freely available on GitHub: https://github.com/volkamerlab/TeachOpenCADD .
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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.