Sanatan Upmanyu

• — Drives development and deployment of the ResearchNexus and Clinical Navigator platforms.
• — Leads strategic AI/ML initiatives for detecting early innovation signals for emerging targets, novel therapeutics, and healthcare solutions.
• — Supports the Asset Lifecycle Manager and Portfolio Management platforms.

• — Advising on AI-driven strategies for early-stage drug discovery and translational research.
• — Shaping the future of health through regulatory-aware innovation in digital and data sciences.

• — Leads development and deployment of the AlphaMeld Platform.
• — Spearheads strategic AI/ML initiatives to enhance AlphaMeld/AlphaCompass capacity.
• — Advises pharma/biotech companies on leveraging data science methodologies.
• — Applied deep molecular biology expertise for MOA analyses and therapeutic target viability.
• — Manages a data science team of 5 members.

• — Owns technology strategy, architecture, and delivery of Artixio's digital product portfolio — from concept to production.
• — Conceptualized and built QuriousRI, a secure role-based regulatory intelligence platform covering pharmaceuticals, biologics, biosimilars, and generics across 12+ agencies (FDA, EMA, PMDA, NMPA, Health Canada, MHRA, Swissmedic, and more), with LLM-powered document analysis.
• — Architected additional technology applications for the company, including an AI assistant that mines global regulatory updates and auto-drafts submission dossiers — opening a new non-pharma revenue channel.
• — Directs enterprise-level data science and AI product development using Agile methodologies, ensuring adherence to global regulatory standards (FDA, EMA, ICH) across the drug development lifecycle.

• — Co-founded and led data sciences, applying statistical, mathematical, and predictive modeling to build algorithms for life-science research questions.
• — Advised pharma and biotech firms on de novo, reformulation, repurposing, and drug-rescue strategies using real-world data.
• — Built statistical predictive models using NLP and machine learning on RWD; led therapeutic target assessment and MOA analysis.
• — Hands-on expertise in Python, R, KNIME, MongoDB, Hadoop, MySQL/MariaDB, Tableau, Gephi, and Cytoscape.

• — Conceptualized, built, and executed innovative solutions throughout the drug development lifecycle.
• — Managed enterprise-level data science products using Agile methodologies.
• — Established short- to long-term strategies for Data Science and AI teams.

• — Directed drug repurposing and novel target hypothesis generation using big data and statistical techniques.
• — Advised pharma and biotech firms on diverse scientific strategic projects, surveys, and future opportunity mapping (de novo, reformulation, repurposing, drug rescue) using real-world data.
• — Managed and mentored a team of data scientists; partnered closely with US-based pharma and biotech clients.
• — Led project management and strategy consulting efforts, providing scientific support to business development.

• — Conducted siRNA targeted gene silencing for target-based drug discovery.
• — Cloning, expression, purification, and activity analysis of human kinases, cytochrome P450 proteins, GPCRs, and bacterial enzymes — supporting enzyme- and cell-based NCE screening assays.
• — Managed experiments using mammalian, baculovirus, and Dictyostelium expression systems.

End-to-end AI stack combining a custom UI layer (Knowledge Graph, ChatAlphaMeld, Bioinformatics, Literature Analysis, AlphaClinMeld) with 12 ML models — including NER, RE, evidence scoring, digital-twin CT predictors and landscape assessment — running on PostgreSQL + Neo4j and self-hosted LLM APIs.

Guided drug-discovery hypothesis-generation platform built on the AlphaMeld Knowledge Graph. Supports four starting points — disease-first, target-first, indication expansion, and combination therapy — through a step-by-step workflow (project → config → exploration → agent scoring → ranking → hypotheses). A project-aware Discovery Copilot accompanies every step. Multi-tenant SaaS with SSO, RBAC, and audit logging.

Two agentic workflow engines fine-tuned on biomedical tasks; outperformed leading general-purpose frontier LLMs; delivered richer step-by-step rationales critical for complex clinical questions.

End-to-end AI stack combining Knowledge Graph, Clinical Trial Prediction, Literature Analysis, Patient Digital Twin, TPP, TOP with 12 ML models running on PostgreSQL + Neo4j and self-hosted LLM APIs.

Clinical-trial–centric SaaS module on AlphaMeld® graph analytics; benchmarks pipeline assets and quantifies comparative risk. Adopted by two BD teams within six months, standardizing evidence-based indication-prioritization across 200 active programs.

Outcome-focused digital-twin framework built on the EUSTAR systemic sclerosis registry — longitudinal records for 23,000+ patients across 200+ centers. A PyTorch deep/generative stack (a mask- and time-aware GRU-D sequence forecaster plus a conditional sequence-VAE twin generator) produces prognostic scores and synthetic placebo trajectories for organ-specific and composite clinical outcomes. Designed as an ML-generated digital control arm — a PROCOVA/ANCOVA covariate that lifts trial power and cuts sample size — aligned with FDA digital-twin and EMA/ICH MIDD guidance.

Conditional variational auto-encoder (CVAE) pipeline that synthesizes patient-level digital twins for Phase II Type 2 Diabetes trials. Generated matched placebo trajectories and predicted drug-response curves — proving feasibility of in-silico arms for protocol optimization and hypothesis testing.