Across the open-source R packages we author and maintain on CRAN: dbparser, covid19dbcand
Our Impact
Explainable Discoveries & Defendable Results
JOSS-published, rOpenSci peer-reviewed, cited in 10+ journals
- Drug data integration pipelines that turn fragmented pharmacological databases into clean, analysis-ready objects
- Production-grade R Shiny dashboards and scientific applications deployed at scale
- Explainable AI systems for drug repurposing and knowledge discovery
- Professional services bridging open-source tools and regulated industry deployment
- Original architects of dbparser, periscope2, and the DrugVerse ecosystem
- Open-source foundation with professional accountability. Peer-reviewed, CRAN-published, proven in production
- Cross-language vision spanning R and Python, unified under one architectural paradigm
- End-to-end capability from raw data parsing to AI-driven discovery to production deployment
What We Deliver
Purpose-built solutions for life sciences and pharmaceutical teams
Our Services
Comprehensive life sciences consulting, scientific data integration, and R Shiny dashboard development support.
Scientific Consulting
Expert guidance for your research projects and scientific endeavors. We provide strategic insights and methodological expertise.
Training Programs
Comprehensive training in scientific methodologies and technologies. Develop skills that drive innovation.
Research Assistance
End-to-end support for academic and industrial research initiatives. From planning to publication.
Data Analysis
Advanced analytical solutions for complex scientific datasets. Turn data into actionable insights.
Innovation Strategy
Strategic planning for research and development initiatives. Build a roadmap for scientific success.
Project Management
Professional management of scientific projects. Ensure timely delivery and quality outcomes.
Publication Support
Assistance with manuscript preparation and journal submission. Get published in top-tier journals.
Package Development
Developing custom packages, update pre-existing ones, and use our packages for client benefit.
Scientific Consulting
Expert guidance for your research projects and scientific endeavors. We provide strategic insights and methodological expertise.
Training Programs
Comprehensive training in scientific methodologies and technologies. Develop skills that drive innovation.
Research Assistance
End-to-end support for academic and industrial research initiatives. From planning to publication.
Data Analysis
Advanced analytical solutions for complex scientific datasets. Turn data into actionable insights.
Innovation Strategy
Strategic planning for research and development initiatives. Build a roadmap for scientific success.
Project Management
Professional management of scientific projects. Ensure timely delivery and quality outcomes.
Publication Support
Assistance with manuscript preparation and journal submission. Get published in top-tier journals.
Package Development
Developing custom packages, update pre-existing ones, and use our packages for client benefit.
Our Research
Explore our latest research on explainable AI, knowledge graphs, and biomedical data analysis workflows.

Explainable AI for Knowledge Graph-Based Drug Repurposing: Methods, Challenges, and Interpretability Frameworks
Taxonomy of XAI techniques and interpretability frameworks for biomedical KG drug repurposing.

The Role of Explainable AI in Knowledge Graph-Based Drug Repurposing
Bridging Trust and Discovery through XAI and Knowledge Graphs for accelerated drug repurposing.
Our News
Highlights from our latest publications, conference presentations, and biomedical software releases.
Two IEEE ICEBE 2025 papers released on explainable AI for drug repurposing
Companion papers on explainable AI for KG-based drug repurposing now on IEEE Xplore.

New ASD Detection Approach by Mennahtullah Mabrouk at JAC-ECC 2025
Hybrid learning approach for ASD detection using fMRI presented at JAC-ECC 2025.
Connecting the Dots in Drug Safety: Introducing dbparser 2.2.0
Data in computational pharmacology is often siloed. Today, we are releasing dbparser 2.2.0 to solve this problem once and for all.


