AI Infrastructure for Pharmacometric Modeling
AI4Pharma builds AI-powered computational infrastructure that accelerates the entire drug development pipeline — from data preparation to modeling, simulation, and regulatory-ready reporting.
NVIDIA Inception ProgramOur Product Portfolio
Seven products across pharmacometric workflows, PK research, hands-on learning, and pharmacist-supervised prescription review.
PharmPrep — Data Preparation Tool
Automatically transform raw clinical data into pharmacometric (PK/PD) modeling-ready datasets, compatible with non-linear mixed-effects modeling (the gold standard for drug development).
- AI-driven data structure analysis
- Automated format conversion and validation
- Natural language interface for custom handling instructions
- Support for CSV, TXT, TSV, and common clinical data formats
PharmCode — AI Control Stream Writer
An AI agent that automatically generates control streams for nonlinear mixed-effects modeling from your data and modeling objectives.
- Automated control stream generation
- Intelligent model structure selection
- Syntax validation and error checking
- Natural language input for modeling specifications
PharmFast — GPU-Accelerated Execution Engine
Run pharmacometric models at unprecedented speed with GPU-accelerated infrastructure.
- Parallelized pharmacometric model runs
- GPU-assisted numerical optimization
- Faster model convergence
- Large-scale simulation capabilities
PharmAuto — Autonomous Pharmacometric Platform
The autonomous agent that orchestrates PharmPrep, PharmCode, and PharmFast into one seamless end-to-end workflow. What used to take weeks now takes hours.
- Orchestrates the full pipeline — data prep, code generation, execution, and analysis
- Autonomous PK/PD model development and covariate analysis
- Automated diagnostics, visualization, and model comparison
- Regulatory-ready report generation
Kirchhoff PK — Circuit-Law Clearance Modeling
A visual modeling prototype that applies Kirchhoff's Circuit Laws to pharmacokinetic clearance equations, with hepatic, renal, and Michaelis-Menten kinetics represented as electrical circuits.
PharmLearn — Interactive NONMEM Learning
Learn NONMEM population PK/PD modeling interactively in the browser through concepts, control-stream exercises, and guided projects.
AI Pharmacist — Prescription Review on FDA Labeling
Pharmacist-supervised prescription review grounded in DailyMed FDA labeling. It cites the exact source passage and reports checks it could not complete.
Platform Architecture
PharmAuto orchestrates all three tools into a single end-to-end pharmacometric workflow.
Rapid Adoption in the
Pharmacometrics Community
Our PharmPrep prototype is already being used by modeling teams worldwide to automate complex data preparation workflows.
“Such tools will help me overcome the barrier and get to the modeling questions and results interpretation quicker!”
Building the Next Generation of Computational Infrastructure
AI4Pharma is building the next generation of AI-powered infrastructure for drug development. We combine agentic AI, GPU-accelerated computing, and deep pharmacological expertise to automate and accelerate the entire modeling pipeline — enabling faster decisions, shorter timelines, and better outcomes.
Artificial Intelligence
Leveraging state-of-the-art AI models to automate and augment complex pharmacometric reasoning.
High-Performance Computing
GPU-accelerated infrastructure that dramatically shortens model run times and iteration cycles.
Pharmacological Expertise
Deep domain knowledge in PK/PD modeling, clinical pharmacology, and regulatory science.
AI4Pharma — Where Science and Engineering Meet
Our founders combine deep pharmaceutical expertise with AI research and high-performance computing to bridge the gap between bench science and computational innovation.
A clinical scientist and AI engineer with seven years across healthcare and research, trained at UCSF and Stanford. His own work spans PK/PD modeling, pharmacogenomics, graph machine learning, and agentic AI systems for therapeutic discovery and precision medicine. Having worked as each — clinician, scientist, modeler, engineer — he translates between the four groups a drug program depends on and holds them to one definition of the problem.
A medicinal chemist and data scientist with over 10 years in drug discovery, trained at UNC and UCSF for her postdoctoral and master’s degrees. Her work at Vertex Pharmaceuticals and Pfizer spans small-molecule discovery — synthetic chemistry, SAR analysis, and computational oncology — and AI/ML in clinical pharmacology. She leads our pharma partnerships, working with the discovery and clinical pharmacology teams our tools are built for.
An AI and machine learning leader with over 18 years in software engineering, including nine years building healthcare AI and clinical NLP systems. Formerly a Senior Research Scientist at Microsoft/Nuance, he leads the development of our agentic AI architecture, turning advanced AI research into secure, scalable solutions for drug development.
A senior systems and GPU engineer with more than 15 years across high-performance computing, graphics programming, embedded systems, and software architecture. He held engineering and technical leadership roles at Huawei, Qualcomm, and Valeo, covering GPU platform architecture, Vulkan and DirectX systems, and performance optimization. At AI4Pharma he leads our computational infrastructure and high-performance modeling systems, and is the inventor of a U.S. patent in information security.
Ready to Accelerate Your Drug Development?
Get in touch with the AI4Pharma team to learn how our tools can fit into your pharmacometric modeling workflows.