The principal, Dr. Pankaj Agarwal, has 25+ years strategic and tactical experience utilizing bioinformatics to enable drug discovery and create pipeline value. He has collaborated extensively with numerous pharmaceutical project teams, academic/biotech partners, and top informatics talent. Dr. Agarwal has 50+ publications in top journals, including Nature Rev Drug Discovery, Nature Biotechnology, and Clinical Pharmacology & Therapeutics, and multiple methodological and gene patents. In 2016, he was among a group of select few scientists appointed as senior fellows at GSK. Dr. Agarwal has also served on NSF, NIH, FDA and PhRMA panels. He possesses a B.Tech. in Computer Science & Engineering from IIT, Delhi and a Ph.D. in Computer Science from the Courant Institute of Mathematical Sciences at NYU. He is a founder and senior member of the International Society for Computational Biology (ISCB). Most importantly, Dr. Agarwal is passionate about drug discovery, rare diseases, and helping patients.


Dr. Agarwal's projects and accomplishments include:

  1. Comprehensively discovered and patented numerous gene targets first from ESTs and then the human genome using automated overnight computes. This was enabled by extensive collaborations with top gene-finding experts and joint publications.

  2. Invented and patented the first  gene set enrichment method and extended it to work across multiple genetic loci from the precursor to  GWAS studies. Established a comprehensive collection of gene sets across public domain and private data.

  3. Developed the largest collection of protein interaction data through licenses with early providers. Co-built and published a network algorithm to mine it.

  4. Led the design and development of a comprehensive portal and toolkit with over a 1000 internal pharmaceutical users with gene, disease and Omic tools.

  5. Led numerous target identification projects using the best-in-class tools with clear actionable shortlist of targets. Collaborated with bioinformaticians, disease biologists, and phenotypic  screening experts on these projects.

  6. Led the comprehensive analysis of scientific innovation in disease areas for the Head of R&D. The results of this project were used to scientifically redesign R&D.  Selected results were published in Nature Reviews Drug Discovery.

  7. The above project included a strategy for identifying KOLs and potential hires in each area as well as an in-licensing analysis and strategy.

  8. Analyzed the potential for setting up a rare disease unit within GSK and identified the seed portfolio of repurposing and target opportunties.

  9. Collaborated with multiple disease areas on due diligences, in-licensing, and suitable target identification using multiple drug modalities.

  10. Led an internal biotech for repurposing: Systematic Drug Repositioning (SyDR), which developed multiple computational techniques, assessed all internal pipeline molecules and experimentally validated the most promising hypotheses across 10+ disease areas.

  11. Identified and actioned six rare to common drug targets based on agnostic evaluation of their genetic, biological and druggability.

  12. Led a comprehensive evaluation of targets for gene therapy.

  13. Led the training of the entire team in deep learning and AI. Within a month, the team was using sophisticated Tensorflow methods directly on drug discovery projects. A team member discovered that phase 3 target success could be predicted using an Autoencoder on GTEx data.

  14. Collaborated on a machine learning project to predict oncology response biomarkers and combinations for pipeline and marketed drugs.