The computational tools listed here are provided to the global scientific community free of charge, in support of open science, and to foster communication and collaboration. The Scientific Web Apps Team of the Translational Informatics Division develops software tools including web apps to support extramurally funded research projects, and strives to share methods and tools widely. If you make use of these tools and publish your research, please cite the resources appropriately.
Badapple (Bioassay-Data Associative Promiscuity Pattern Learning Engine) is a method for detecting likely promiscuous compounds via their associated scaffolds, using public bioassay data from PubChem. Badapple 2.0 is a major update with a complete code-rewrite, updated and expanded assay data sets, enhanced functionality, scalability, and metadata, supporting improved explainability and richer bioactivity analyses.
Powered by: RDKit
TICTAC (Target Illumination Clinical Trial Analytics with Cheminformatics) evaluates disease-target associations by linking protein-coding genes to diseases and incorporates a confidence assessment method based on aggregated evidence.
Powered by:TICTAC_DB, FastAPI, React
CARLSBAD (Confederated Annotated Research Libraries of Small molecule Biological Activity Data) is a bioactivity knowledgebase & hypotheses via chemical patterns.
Powered by: ChemAxon, Cytoscape
CKT (Causal Knowledge Trace) generates causal graphs from semantic relationships extracted from the biomedical literature via the SemRep natural language processing system.
Powered by: SemRep, R, R-Shiny
Convert mol formats
Powered by: ChemAxon
Depict molecules
Powered by: RDKit
Distance-geometry conformer generation
Powered by: RDKit, JSmol
Dose-response app for potency exploration: Visualize dose-response curves with 4-parameter logistic regression plots.
Powered by: R, R-dr4pl, R-Shiny
Drug knowledge integration database.
Powered by: Django, PostgreSQL, ChemAxon
Expression profile analytics, with SABV (sex-as-biological-variable), with RNA-seq data from GTEx
Powered by: R, R-Shiny
JavaScript molecular editor, by P. Ertl & B. Bienfait
The KG2ML module was developed to transform the CFDE Data Distillery Knowledge Graph into vector embeddings and an ML-ready dataset, designed to predict genes/proteins associated with a given disease/condition but lacking explicit links within the KG.
Powered by: Python, Neo4j
Molecule clouds (algorithm by P. Ertl & B. Rohde, Novartis)
Powered by: ChemAxon
Lipinski Rule of 5 analysis
Powered by: RDKit
SMARTS filtering with built in Glaxo, Blake, and Oprea SMARTS sets
Powered by:React, rdktools
TIN-X: Target importance and novelty explorer
Powered by: Python, Solr
TIGA: Target Illumination GWAS Analytics. From the NIH IDG Project, aggregated evidence from the NHGRI-EBI GWAS Catalog for gene-trait associations
Powered by: R, R-Shiny
Druglikeness using DRUGS/ACD fragment analysis
Powered by: ChemAxon
Drug repositioning database, developed by Adam Brown and
Chirag Patel at Harvard, updated by Jeremy Yang at UNM with new versions of DrugCentral, AACT, and UMLS.
Powered by: R, R-Shiny
Sincere thanks to the software developers, projects, and partners whose cooperation and commitment to open science and academic research allow these services to exist:
Allowed Use
ALLOWED USE: These applications are for limited, reasonable use by the scientific community and especially academic and non-profit researchers worldwide. Overuse will interfere with access and performance for others, so please be considerate. In general, all users should limit use to 10k molecules per day. All usage is logged, and overuse may result in client IP blocking.
Input file sizes: Note that these web apps, given typical network throughput, are impractical for large input files. In most cases the app restricts input file sizes. In general files in excess of a few thousand lines (e.g. SMILES) should not be uploaded.
Contact & Support
To report a problem with these web apps, contact Bivek Sharma Panthi.
To submit a software issue, please use the corresponding GitHub issues forum.
To inquire regarding a research collaboration, or for special requests such as high-volume jobs and collaboration inquiries contact Jeremy Yang.
Scientific Web Apps Team
- • Jack Ringer, MS – Computer Science, Data Scientist
- • Bivek Sharma Panthi, BS-student, Computer Science, Research Assistant
- • Bat Ochir Artur, BS – Computer Science, Visiting Scholar
- • Vincent T Metzger, PhD – Post-doctoral Researcher
- • Jeremy J Yang, PhD – Research Associate Professor
Past Contributors
- • Daniel Cannon, MS – Computer Science, Founder and President, Elevato-Iterative
- • Jayme Holmes, MS – IT Project Manager
- • Priyansh Kedia, MS – Visiting Scholar
