Biotechs are based on a promising platform, modality, or scientific pathway. However, you still need to queray and integrate the right ancillary data resources regarding genetics, target history, semantic parse of literature, genomics, and druggability to make informed decisions on which targets and molecules to prioritize. These early decisions are crucial to your success and missteps can prove calamatious. Good decisions require 1) an in-depth understanding of the platform and therapeutic area, 2) interpretation of the relevant genetics and literature data, 3) experience making drug discovery decisions using complex data. While the biotech has the expertise and resources on platform and therapy area, it often lacks the genetics/literature data and its interpretation.

We have a built an analytical platform to help you rapidly prioritize across all targets using our data integration, expertise, and consultation. Please contact us to initiate a discussion.

Other services offered include:

1. Recruiting the impossible to find expert based on their science.
2. Indication evaluation or expansion for your (pre)clinical asset.
3. Evaluation of molecular biomarkers for an indication.
4. Pharmacodynamic biomarkers for your drug.

Each of these is supported by proprietary data builds, analytics, and consulting to provide clean solutions to biotech challenges. We specialize in non-oncology indications and rare diseases.

Business Model

Our business model is flat-fee pricing depending on your specific objectives and our evaluation. This includes our comprehensive data build, your specific data requests, prioritization of targets/repurposing, joint assessment of results (includes appropriate consulting). This leverages our experience across scores of disease, platform and modality builts. You own all the right to pursue the targets for the stated oportunity. We retain right to our data and analytics IP. Risk-sharing is possible with milestone-driven payments. We believe every startup/biotech should have access to the best target/repurposing guidance utilizing decades of industry experience.

Human capital is an important element for healthcare and drug discovery. Surprisingly, there are few resources that connect medical and scientific talent to patients, drug discoverers, and biotechs. We have developed two solutions:

1. FindExpertMD (https://FindExpertMD.com) enables patients, doctors, and scientists to rapidly identify medical experts based on objective scientific publications, clinical trials, Medicare ratings, payments from corporations, and funding data.
2. We have a data-driven solution to biomedical recruitment that will connect you with impossible to find higher-quality candidates. Email us to discuss your job descriptions from postdoc to Chief Scientific Officers.

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. 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.
2. 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.
3. Analyzed the potential for setting up a rare disease unit within GSK and identified the seed portfolio of repurposing and target opportunties.
4. Collaborated with multiple disease areas on due diligences, in-licensing, and suitable target identification using multiple drug modalities.
5. Identified and actioned six rare to common drug targets based on agnostic evaluation of their genetics, biology, literature, and druggability.
6. Led a comprehensive evaluation of targets for gene therapy.
7. Led the training of the Bioinformatics 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.
8. Collaborated on a machine learning project to predict oncology response biomarkers and combinations for pipeline and marketed drugs.
9. 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.
10. 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.
11. Developed the largest collection of protein interaction data through licenses with early providers establishing a large biomedical knowledge graph. Co-built and published a network algorithm to mine it.
12. Led the design and development of a comprehensive portal and toolkit with over a 1000 internal pharmaceutical users with gene, disease and Omic tools.
13. 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.
14. The above project included a strategy for identifying KOLs and potential hires in each area as well as an in-licensing analysis and strategy.

Publications