Why DeepPumas™?
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Incorporate a wide range of information in your models
A model is only as powerful as the information it’s built upon. While incremental refinements to processing of specific data can be helpful, true breakthroughs come from integrating entirely different classes of information—mechanistic, hierarchical, and data-driven—into a unified modeling framework.
Learn DeepPumas™ Through Collaboration
Solve Real-World Problems While Gaining Hands-On Experience
PumasAI partners with organizations on targeted projects to accelerate the delivery of impactful solutions. By combining your domain expertise with our modeling and industry knowledge, we offer a bespoke DeepPumas learning experience, one that’s grounded in solving real-world challenges.
Build Bridges Between Departments
Facilitate interdepartmental collaboration by bridging the gap between scientific modeling and machine learning. DeepPumas helps unify the expertise of machine learning researchers, mechanistic modelers, and domain experts to drive aligned, data-informed decisions.
Build Drug Development Dashboards
Develop complex models for challenging disease areas, such as oncology and rare diseases, to link early biomarkers with clinical outcomes. Enable real-world usability by allowing non-data scientists to interpret results through intuitive model dashboards.
Identify Complex Relationships Quickly, Even with Small Datasets
Don’t let limited data in early development stages hinder decision-making. DeepPumas leverages known biology and mechanisms of action to detect patient response patterns and optimize dosing regimens in real time, accelerating clinical trials.
Stay Ahead of Your Competitors
DeepPumas is the first-of-its-kind platform that combines Scientific Machine Learning with non-linear mixed-effects (NLME) modeling. It dramatically expands the scope of usable information, empowering better knowledge for better business outcomes.
Where Intelligence Meets Innovation
Healthcare Business Intelligence Redefined. Don’t choose between Scientific Modeling and Machine Learning — combine them!
What is DeepPumas™?
DeepPumas is both software and a service. Customers use the DeepPumas software to build and run their own models, and work with PumasAI's advisory and consulting team to design, implement, and interpret DeepPumas-powered analyses, delivering data-driven recommendations tailored to each client's drug development strategy.
DeepPumas: First-to-Market Scientific Machine Learning Framework
DeepPumas is the first solution of its kind—designed to integrate scientific understanding with machine learning to support smarter, more confident decision-making.DeepPumas™ in Practice: Advisory and Consulting Services
PumasAI provides end-to-end business consulting and advisory services built on the DeepPumas platform. Our engagements span the full drug development lifecycle, from early biomarker discovery through late-stage clinical modeling, and are designed to deliver strategic, data-informed recommendations to our clients' development teams.
Our DeepPumas consulting practice has supported leading organizations across a range of complex challenges:
Oncology modeling: Supporting the development and qualification of tumor growth dynamics and overall survival modeling platforms for major oncology programs, enabling clients to make more confident go/no-go decisions in Phase 1 and Phase 2 trials.
Neurodegenerative disease: Applying deep representation learning and NLME integration to Tau PET imaging data to predict disease progression and clinical outcomes in Alzheimer's disease programs.
Rare disease and specialty biologics: Providing model-informed drug development advisory services for novel biologic programs, including population PK modeling and machine learning-based response prediction.
Ophthalmology: Applying machine learning to complex longitudinal data to characterize disease progression, patient heterogeneity, and biomarker relationships, informing future trial design and portfolio decisions.
Immunology and oncology analytics: Building machine learning models to identify complex biomarker relationships and optimize dosing strategies across diverse patient populations.
In each engagement, PumasAI's expert team partners closely with client scientists, quantitative pharmacologists, and decision-makers to translate DeepPumas outputs into clear business intelligence, informing trial design, patient stratification, regulatory strategy, and portfolio prioritization.
Who should use DeepPumas™?
Quantitative Systems Biologists
Quantitative Pharmacologists
Translational Modelers
Pharmacometricians
Biostatisticians
Health Outcomes Researchers
Clinical Research
Precision Medicine
Real-World Data
Manufacturing
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Seamlessly Combine Modeling Approaches
When we say seamless, we mean it!
Use variables, covariates, and random effects as inputs to neural networks that predict missing terms in a dynamical system.
Train everything together using maximum likelihood estimation while marginalizing over random effects, ensuring a unified, interpretable, and powerful modeling pipeline.
Transform your understanding of biological interactions, system constraints, and conserved quantities into the structural backbone of your models. Encode this knowledge as mathematical functions, transformations, or systems of differential equations to ensure your models reflect real-world dynamics.
Leverage the contextual structure of your data. Repeated measurements from the same patient are inherently more correlated than those across different patients, so don’t treat them as independent. Use nonlinear mixed-effects modeling to account for hierarchical variability, disentangling universally shared biological properties from individual differences and measurement noise.
Apply machine learning to uncover predictive patterns hidden in your data. Embed machine learning seamlessly within mechanistic and hierarchical models to capture unknown relationships, process complex covariates, such as images, identify missing terms in your dynamical systems, and discover individualizable transformations linking latent variables to observed outcomes.
