Pharmaceuticals
End-to-end Platform to Fast-Track Drug Development
JuliaHub is a 21 CFR Part 11 compliant platform that helps pharmaceutical companies manage the full lifecycle of drug development, pharmacometrics, quantitative systems pharmacology, and analytics
Pharmaceuticals
End-to-end Platform to Fast-Track Drug Development
JuliaHub is a 21 CFR Part 11 compliant platform that helps pharmaceutical companies manage the full lifecycle of drug development, pharmacometrics, quantitative systems pharmacology, and analytics
Pharmaceuticals
End-to-end Platform to Fast-Track Drug Development
JuliaHub is a 21 CFR Part 11 compliant platform that helps pharmaceutical companies manage the full lifecycle of drug development, pharmacometrics, quantitative systems pharmacology, and analytics
JuliaHub for Innovation in Pharmaceutical Development
JuliaHub for Innovation in Pharmaceutical Development
JuliaHub for Innovation in Pharmaceutical Development
Interoperability
Interoperability
Interoperability
Seamless integration with existing pharmaceutical tools and workflows using languages such as R, Python, Julia, and others.
Seamless integration with existing pharmaceutical tools and workflows using languages such as R, Python, Julia, and others.
Seamless integration with existing pharmaceutical tools and workflows using languages such as R, Python, Julia, and others.
Workflow and provisioning for NONMEM, Monolix, Phoenix and other common tools
Workflow and provisioning for NONMEM, Monolix, Phoenix and other common tools
Workflow and provisioning for NONMEM, Monolix, Phoenix and other common tools
Jobs API for programmatically executing your workflows in a reproducible fashion
Jobs API for programmatically executing your workflows in a reproducible fashion
Jobs API for programmatically executing your workflows in a reproducible fashion
Data API for secure, versioned, and auditable data
Data API for secure, versioned, and auditable data
Data API for secure, versioned, and auditable data
Julia’s open source ecosystem offers a large number of packages and libraries to connect with tools and data sources.
Julia’s open source ecosystem offers a large number of packages and libraries to connect with tools and data sources.
Julia’s open source ecosystem offers a large number of packages and libraries to connect with tools and data sources.
Collaboration
Collaboration
Collaboration
Straightforward version control to enhance collaboration between researchers and scientists best-in-class version control without the usual headaches
Straightforward version control to enhance collaboration between researchers and scientists best-in-class version control without the usual headaches
Straightforward version control to enhance collaboration between researchers and scientists best-in-class version control without the usual headaches
Create a single source of truth for your code and data
Create a single source of truth for your code and data
Create a single source of truth for your code and data
Use version control to manage code and data changes and track history
Use version control to manage code and data changes and track history
Use version control to manage code and data changes and track history
Ensure data security and privacy with granular access control
Ensure data security and privacy with granular access control
Ensure data security and privacy with granular access control
Reproducibility
Reproducibility
Reproducibility
Consistent and repeatable results in computational models, reducing variability in drug development and testing
Consistent and repeatable results in computational models, reducing variability in drug development and testing
Consistent and repeatable results in computational models, reducing variability in drug development and testing
Automatic saving of batch jobs ensures future reuse with seamless re-execution of jobs with the same environment, settings, and data
Automatic saving of batch jobs ensures future reuse with seamless re-execution of jobs with the same environment, settings, and data
Automatic saving of batch jobs ensures future reuse with seamless re-execution of jobs with the same environment, settings, and data
Time Capsule empowers users the ability to retain job information for years to meet regulatory compliance
Time Capsule empowers users the ability to retain job information for years to meet regulatory compliance
Time Capsule empowers users the ability to retain job information for years to meet regulatory compliance
Compliance
Compliance
Compliance
Compliance with regulatory standards for pharmaceuticals and healthcare such as FDA 21 CFR Part 11 and GAMP 5
Compliance with regulatory standards for pharmaceuticals and healthcare such as FDA 21 CFR Part 11 and GAMP 5
Compliance with regulatory standards for pharmaceuticals and healthcare such as FDA 21 CFR Part 11 and GAMP 5
JuliaHub is SOC II compliant, providing validation and verification processes essential for regulatory approval.
JuliaHub is SOC II compliant, providing validation and verification processes essential for regulatory approval.
JuliaHub is SOC II compliant, providing validation and verification processes essential for regulatory approval.
Data integrity, accuracy, and robust security to protect sensitive information
Data integrity, accuracy, and robust security to protect sensitive information
Data integrity, accuracy, and robust security to protect sensitive information
Visit trust.juliahub.com, our dedicated portal to understand our commitment to security, compliance, and transparency.
Visit trust.juliahub.com, our dedicated portal to understand our commitment to security, compliance, and transparency.
Visit trust.juliahub.com, our dedicated portal to understand our commitment to security, compliance, and transparency.
Traceability
Traceability
Traceability
Detailed audit logs to record user actions and changes to data
Detailed audit logs to record user actions and changes to data
Detailed audit logs to record user actions and changes to data
Monitor compliance with regulatory requirements and industry standards
Monitor compliance with regulatory requirements and industry standards
Monitor compliance with regulatory requirements and industry standards
Use Cases
How JuliaHub Empowers Pharmaceutical Development
Use Cases
How JuliaHub Empowers Pharmaceutical Development
Use Cases
How JuliaHub Empowers Pharmaceutical Development
Drug Discovery & Preclinical Development
Accelerate pharmacology simulations up to 175x faster using Julia with GPU acceleration for model-informed drug development (MIDD)
Perform quantitative systems pharmacology (QSP) modeling to predict drug effects before clinical trials
Reduce global optimization time from 15.5 hours to 1 hour per patient in active drug-target models
Drug Discovery & Preclinical Development
Accelerate pharmacology simulations up to 175x faster using Julia with GPU acceleration for model-informed drug development (MIDD)
Perform quantitative systems pharmacology (QSP) modeling to predict drug effects before clinical trials
Reduce global optimization time from 15.5 hours to 1 hour per patient in active drug-target models
Drug Discovery & Preclinical Development
Accelerate pharmacology simulations up to 175x faster using Julia with GPU acceleration for model-informed drug development (MIDD)
Perform quantitative systems pharmacology (QSP) modeling to predict drug effects before clinical trials
Reduce global optimization time from 15.5 hours to 1 hour per patient in active drug-target models
Pharmacometric Modeling & Analysis
Conduct comprehensive PK/PD modeling with pharmacokinetic and pharmacodynamic simulations in a single platform
Perform physiologically-based pharmacokinetic (PBPK) modeling for complex absorption, distribution, metabolism, and excretion predictions
Execute non-compartmental analysis (NCA) and bioequivalence studies with integrated toolsets
Analyze in vitro-in vivo correlations (IVIVC) to bridge laboratory and clinical results
Pharmacometric Modeling & Analysis
Conduct comprehensive PK/PD modeling with pharmacokinetic and pharmacodynamic simulations in a single platform
Perform physiologically-based pharmacokinetic (PBPK) modeling for complex absorption, distribution, metabolism, and excretion predictions
Execute non-compartmental analysis (NCA) and bioequivalence studies with integrated toolsets
Analyze in vitro-in vivo correlations (IVIVC) to bridge laboratory and clinical results
Pharmacometric Modeling & Analysis
Conduct comprehensive PK/PD modeling with pharmacokinetic and pharmacodynamic simulations in a single platform
Perform physiologically-based pharmacokinetic (PBPK) modeling for complex absorption, distribution, metabolism, and excretion predictions
Execute non-compartmental analysis (NCA) and bioequivalence studies with integrated toolsets
Analyze in vitro-in vivo correlations (IVIVC) to bridge laboratory and clinical results
Clinical Trial Optimization
Accelerate clinical trial simulations with integrated machine learning and traditional pharmacometric approaches
Design virtual patient populations using nonlinear mixed effects (NLME) modeling for trial planning
Scale analyses from single machines to cloud clusters with automatic GPU and CPU parallelization
Integrate with existing tools including NONMEM, Monolix, Phoenix, R, and Python workflows
Clinical Trial Optimization
Accelerate clinical trial simulations with integrated machine learning and traditional pharmacometric approaches
Design virtual patient populations using nonlinear mixed effects (NLME) modeling for trial planning
Scale analyses from single machines to cloud clusters with automatic GPU and CPU parallelization
Integrate with existing tools including NONMEM, Monolix, Phoenix, R, and Python workflows
Clinical Trial Optimization
Accelerate clinical trial simulations with integrated machine learning and traditional pharmacometric approaches
Design virtual patient populations using nonlinear mixed effects (NLME) modeling for trial planning
Scale analyses from single machines to cloud clusters with automatic GPU and CPU parallelization
Integrate with existing tools including NONMEM, Monolix, Phoenix, R, and Python workflows
Regulatory Compliance & Submission
Meet FDA 21 CFR Part 11 compliance with audit trails, data lineage, and digital record-keeping
Generate regulatory reports with explainable machine learning models for submission purposes
Maintain data integrity with Time Capsule technology retaining job information for years
Regulatory Compliance & Submission
Meet FDA 21 CFR Part 11 compliance with audit trails, data lineage, and digital record-keeping
Generate regulatory reports with explainable machine learning models for submission purposes
Maintain data integrity with Time Capsule technology retaining job information for years
Regulatory Compliance & Submission
Meet FDA 21 CFR Part 11 compliance with audit trails, data lineage, and digital record-keeping
Generate regulatory reports with explainable machine learning models for submission purposes
Maintain data integrity with Time Capsule technology retaining job information for years
Powered by Julia: the Standard for technical and scientific computing
Vibrant Open-Source Ecosystem
Julia’s expansive and continuously growing ecosystem fosters collaboration and accelerates code deployment. Its readability and maintainability ensure long-term sustainability of projects.
High Performance
Julia’s just-in-time (JIT) compilation system delivers lightning-fast execution, with performance gains of 100-1000x over legacy languages making it an ideal choice for HPC applications in computing solutions.
Ease of Use
Julia’s intuitive syntax is easy to learn for developers familiar with Python or MATLAB, enabling rapid skill acquisition and minimizing training time.
Advanced Parallelism
Built-in support for parallel computing allows for efficient execution of concurrent tasks, maximizing resource utilization and reducing project timelines.
Math-Friendly Syntax
Julia’s syntax is tailored for complex mathematical operations, streamlining the development process for scientific and engineering applications.
Automatic Differentiation
Simplify the creation of algorithms and models that require gradients and derivatives, reducing coding complexity and enhancing model accuracy.
Native GPU Support
Harness the full potential of GPUs to accelerate computational tasks and simulations, driving innovation in data-intensive projects.
Vibrant Open-Source Ecosystem
Julia’s expansive and continuously growing ecosystem fosters collaboration and accelerates code deployment. Its readability and maintainability ensure long-term sustainability of projects.
High Performance
Julia’s just-in-time (JIT) compilation system delivers lightning-fast execution, with performance gains of 100-1000x over legacy languages making it an ideal choice for HPC applications in computing solutions.
Ease of Use
Julia’s intuitive syntax is easy to learn for developers familiar with Python or MATLAB, enabling rapid skill acquisition and minimizing training time.
Advanced Parallelism
Built-in support for parallel computing allows for efficient execution of concurrent tasks, maximizing resource utilization and reducing project timelines.
Math-Friendly Syntax
Julia’s syntax is tailored for complex mathematical operations, streamlining the development process for scientific and engineering applications.
Automatic Differentiation
Simplify the creation of algorithms and models that require gradients and derivatives, reducing coding complexity and enhancing model accuracy.
Native GPU Support
Harness the full potential of GPUs to accelerate computational tasks and simulations, driving innovation in data-intensive projects.
Vibrant Open-Source Ecosystem
Julia’s expansive and continuously growing ecosystem fosters collaboration and accelerates code deployment. Its readability and maintainability ensure long-term sustainability of projects.
High Performance
Julia’s just-in-time (JIT) compilation system delivers lightning-fast execution, with performance gains of 100-1000x over legacy languages making it an ideal choice for HPC applications in computing solutions.
Ease of Use
Julia’s intuitive syntax is easy to learn for developers familiar with Python or MATLAB, enabling rapid skill acquisition and minimizing training time.
Advanced Parallelism
Built-in support for parallel computing allows for efficient execution of concurrent tasks, maximizing resource utilization and reducing project timelines.
Math-Friendly Syntax
Julia’s syntax is tailored for complex mathematical operations, streamlining the development process for scientific and engineering applications.
Automatic Differentiation
Simplify the creation of algorithms and models that require gradients and derivatives, reducing coding complexity and enhancing model accuracy.
Native GPU Support
Harness the full potential of GPUs to accelerate computational tasks and simulations, driving innovation in data-intensive projects.
Vibrant Open-Source Ecosystem
Julia’s expansive and continuously growing ecosystem fosters collaboration and accelerates code deployment. Its readability and maintainability ensure long-term sustainability of projects.
High Performance
Julia’s just-in-time (JIT) compilation system delivers lightning-fast execution, with performance gains of 100-1000x over legacy languages making it an ideal choice for HPC applications in computing solutions.
Ease of Use
Julia’s intuitive syntax is easy to learn for developers familiar with Python or MATLAB, enabling rapid skill acquisition and minimizing training time.
Advanced Parallelism
Built-in support for parallel computing allows for efficient execution of concurrent tasks, maximizing resource utilization and reducing project timelines.
Math-Friendly Syntax
Julia’s syntax is tailored for complex mathematical operations, streamlining the development process for scientific and engineering applications.
Automatic Differentiation
Simplify the creation of algorithms and models that require gradients and derivatives, reducing coding complexity and enhancing model accuracy.
Native GPU Support
Harness the full potential of GPUs to accelerate computational tasks and simulations, driving innovation in data-intensive projects.
Powered by Julia: the Standard for technical and scientific computing
Vibrant Open-Source Ecosystem
Julia’s expansive and continuously growing ecosystem fosters collaboration and accelerates code deployment. Its readability and maintainability ensure long-term sustainability of projects.
High Performance
Julia’s just-in-time (JIT) compilation system delivers lightning-fast execution, with performance gains of 100-1000x over legacy languages making it an ideal choice for HPC applications in computing solutions.
Ease of Use
Julia’s intuitive syntax is easy to learn for developers familiar with Python or MATLAB, enabling rapid skill acquisition and minimizing training time.
Advanced Parallelism
Built-in support for parallel computing allows for efficient execution of concurrent tasks, maximizing resource utilization and reducing project timelines.
Math-Friendly Syntax
Julia’s syntax is tailored for complex mathematical operations, streamlining the development process for scientific and engineering applications.
Automatic Differentiation
Simplify the creation of algorithms and models that require gradients and derivatives, reducing coding complexity and enhancing model accuracy.
Native GPU Support
Harness the full potential of GPUs to accelerate computational tasks and simulations, driving innovation in data-intensive projects.
Vibrant Open-Source Ecosystem
Julia’s expansive and continuously growing ecosystem fosters collaboration and accelerates code deployment. Its readability and maintainability ensure long-term sustainability of projects.
High Performance
Julia’s just-in-time (JIT) compilation system delivers lightning-fast execution, with performance gains of 100-1000x over legacy languages making it an ideal choice for HPC applications in computing solutions.
Ease of Use
Julia’s intuitive syntax is easy to learn for developers familiar with Python or MATLAB, enabling rapid skill acquisition and minimizing training time.
Advanced Parallelism
Built-in support for parallel computing allows for efficient execution of concurrent tasks, maximizing resource utilization and reducing project timelines.
Math-Friendly Syntax
Julia’s syntax is tailored for complex mathematical operations, streamlining the development process for scientific and engineering applications.
Automatic Differentiation
Simplify the creation of algorithms and models that require gradients and derivatives, reducing coding complexity and enhancing model accuracy.
Native GPU Support
Harness the full potential of GPUs to accelerate computational tasks and simulations, driving innovation in data-intensive projects.
Vibrant Open-Source Ecosystem
Julia’s expansive and continuously growing ecosystem fosters collaboration and accelerates code deployment. Its readability and maintainability ensure long-term sustainability of projects.
High Performance
Julia’s just-in-time (JIT) compilation system delivers lightning-fast execution, with performance gains of 100-1000x over legacy languages making it an ideal choice for HPC applications in computing solutions.
Ease of Use
Julia’s intuitive syntax is easy to learn for developers familiar with Python or MATLAB, enabling rapid skill acquisition and minimizing training time.
Advanced Parallelism
Built-in support for parallel computing allows for efficient execution of concurrent tasks, maximizing resource utilization and reducing project timelines.
Math-Friendly Syntax
Julia’s syntax is tailored for complex mathematical operations, streamlining the development process for scientific and engineering applications.
Automatic Differentiation
Simplify the creation of algorithms and models that require gradients and derivatives, reducing coding complexity and enhancing model accuracy.
Native GPU Support
Harness the full potential of GPUs to accelerate computational tasks and simulations, driving innovation in data-intensive projects.
Vibrant Open-Source Ecosystem
Julia’s expansive and continuously growing ecosystem fosters collaboration and accelerates code deployment. Its readability and maintainability ensure long-term sustainability of projects.
High Performance
Julia’s just-in-time (JIT) compilation system delivers lightning-fast execution, with performance gains of 100-1000x over legacy languages making it an ideal choice for HPC applications in computing solutions.
Ease of Use
Julia’s intuitive syntax is easy to learn for developers familiar with Python or MATLAB, enabling rapid skill acquisition and minimizing training time.
Advanced Parallelism
Built-in support for parallel computing allows for efficient execution of concurrent tasks, maximizing resource utilization and reducing project timelines.
Math-Friendly Syntax
Julia’s syntax is tailored for complex mathematical operations, streamlining the development process for scientific and engineering applications.
Automatic Differentiation
Simplify the creation of algorithms and models that require gradients and derivatives, reducing coding complexity and enhancing model accuracy.
Native GPU Support
Harness the full potential of GPUs to accelerate computational tasks and simulations, driving innovation in data-intensive projects.
Powered by Julia: the Standard for technical and scientific computing
Vibrant Open-Source Ecosystem
Julia’s expansive and continuously growing ecosystem fosters collaboration and accelerates code deployment. Its readability and maintainability ensure long-term sustainability of projects.
High Performance
Julia’s just-in-time (JIT) compilation system delivers lightning-fast execution, with performance gains of 100-1000x over legacy languages making it an ideal choice for HPC applications in computing solutions.
Ease of Use
Julia’s intuitive syntax is easy to learn for developers familiar with Python or MATLAB, enabling rapid skill acquisition and minimizing training time.
Advanced Parallelism
Built-in support for parallel computing allows for efficient execution of concurrent tasks, maximizing resource utilization and reducing project timelines.
Math-Friendly Syntax
Julia’s syntax is tailored for complex mathematical operations, streamlining the development process for scientific and engineering applications.
Automatic Differentiation
Simplify the creation of algorithms and models that require gradients and derivatives, reducing coding complexity and enhancing model accuracy.
Native GPU Support
Harness the full potential of GPUs to accelerate computational tasks and simulations, driving innovation in data-intensive projects.
Vibrant Open-Source Ecosystem
Julia’s expansive and continuously growing ecosystem fosters collaboration and accelerates code deployment. Its readability and maintainability ensure long-term sustainability of projects.
High Performance
Julia’s just-in-time (JIT) compilation system delivers lightning-fast execution, with performance gains of 100-1000x over legacy languages making it an ideal choice for HPC applications in computing solutions.
Ease of Use
Julia’s intuitive syntax is easy to learn for developers familiar with Python or MATLAB, enabling rapid skill acquisition and minimizing training time.
Advanced Parallelism
Built-in support for parallel computing allows for efficient execution of concurrent tasks, maximizing resource utilization and reducing project timelines.
Math-Friendly Syntax
Julia’s syntax is tailored for complex mathematical operations, streamlining the development process for scientific and engineering applications.
Automatic Differentiation
Simplify the creation of algorithms and models that require gradients and derivatives, reducing coding complexity and enhancing model accuracy.
Native GPU Support
Harness the full potential of GPUs to accelerate computational tasks and simulations, driving innovation in data-intensive projects.
Vibrant Open-Source Ecosystem
Julia’s expansive and continuously growing ecosystem fosters collaboration and accelerates code deployment. Its readability and maintainability ensure long-term sustainability of projects.
High Performance
Julia’s just-in-time (JIT) compilation system delivers lightning-fast execution, with performance gains of 100-1000x over legacy languages making it an ideal choice for HPC applications in computing solutions.
Ease of Use
Julia’s intuitive syntax is easy to learn for developers familiar with Python or MATLAB, enabling rapid skill acquisition and minimizing training time.
Advanced Parallelism
Built-in support for parallel computing allows for efficient execution of concurrent tasks, maximizing resource utilization and reducing project timelines.
Math-Friendly Syntax
Julia’s syntax is tailored for complex mathematical operations, streamlining the development process for scientific and engineering applications.
Automatic Differentiation
Simplify the creation of algorithms and models that require gradients and derivatives, reducing coding complexity and enhancing model accuracy.
Native GPU Support
Harness the full potential of GPUs to accelerate computational tasks and simulations, driving innovation in data-intensive projects.
Vibrant Open-Source Ecosystem
Julia’s expansive and continuously growing ecosystem fosters collaboration and accelerates code deployment. Its readability and maintainability ensure long-term sustainability of projects.
High Performance
Julia’s just-in-time (JIT) compilation system delivers lightning-fast execution, with performance gains of 100-1000x over legacy languages making it an ideal choice for HPC applications in computing solutions.
Ease of Use
Julia’s intuitive syntax is easy to learn for developers familiar with Python or MATLAB, enabling rapid skill acquisition and minimizing training time.
Advanced Parallelism
Built-in support for parallel computing allows for efficient execution of concurrent tasks, maximizing resource utilization and reducing project timelines.
Math-Friendly Syntax
Julia’s syntax is tailored for complex mathematical operations, streamlining the development process for scientific and engineering applications.
Automatic Differentiation
Simplify the creation of algorithms and models that require gradients and derivatives, reducing coding complexity and enhancing model accuracy.
Native GPU Support
Harness the full potential of GPUs to accelerate computational tasks and simulations, driving innovation in data-intensive projects.
Case Studies
See how top pharmaceutical companies are delivering new drugs faster
Discover how research teams are accelerating innovation and reducing time-to-market
Case Studies
See how top pharmaceutical companies are delivering new drugs faster
Discover how research teams are accelerating innovation and reducing time-to-market
Case Studies
See how top pharmaceutical companies are delivering new drugs faster
Discover how research teams are accelerating innovation and reducing time-to-market
Pumas with JuliaHub
Deliver Solutions Faster
Pumas is designed to take over the repetitive, predictable tasks, allowing scientists more time to focus on the solution.
Pumas with JuliaHub
Deliver Solutions Faster
Pumas is designed to take over the repetitive, predictable tasks, allowing scientists more time to focus on the solution.
Pumas with JuliaHub
Deliver Solutions Faster
Pumas is designed to take over the repetitive, predictable tasks, allowing scientists more time to focus on the solution.
The Power of One
Single solution from pre-trial to launch in a drug development workflow, obviating the need for multiple languages, products, and solutions.
Intuitive Development Experience
Build and deploy models with unprecedented ease. Simplified workflows reduce learning curves while powerful automation handles parallelization and optimization behind the scenes.
Scalable & Parallel Computing
Leverage the power of Julia for high-performance computing, with built-in support for composable multi-threading, distributed computing, and GPU acceleration. Effortlessly scale your computations from a single CPU/GPU instance to a cluster of machines.
Any Computing Environment
Deploy on your laptop, private data centers, or leading cloud platforms (AWS, Azure, Google Cloud). From desktop workstations to supercomputers, TPUs, and GPU clusters - Pumas runs everywhere.
AI-Enhanced Modeling
Integrate machine learning seamlessly into pharmacometric workflows with built-in explainability features for regulatory compliance and validation.
Single Language Solution
From interactive research to production deployment, Julia eliminates the traditional two-language problem. No translation overhead, no workflow breaks.
The Power of One
Single solution from pre-trial to launch in a drug development workflow, obviating the need for multiple languages, products, and solutions.
Intuitive Development Experience
Build and deploy models with unprecedented ease. Simplified workflows reduce learning curves while powerful automation handles parallelization and optimization behind the scenes.
Scalable & Parallel Computing
Leverage the power of Julia for high-performance computing, with built-in support for composable multi-threading, distributed computing, and GPU acceleration. Effortlessly scale your computations from a single CPU/GPU instance to a cluster of machines.
Any Computing Environment
Deploy on your laptop, private data centers, or leading cloud platforms (AWS, Azure, Google Cloud). From desktop workstations to supercomputers, TPUs, and GPU clusters - Pumas runs everywhere.
AI-Enhanced Modeling
Integrate machine learning seamlessly into pharmacometric workflows with built-in explainability features for regulatory compliance and validation.
Single Language Solution
From interactive research to production deployment, Julia eliminates the traditional two-language problem. No translation overhead, no workflow breaks.
The Power of One
Single solution from pre-trial to launch in a drug development workflow, obviating the need for multiple languages, products, and solutions.
Intuitive Development Experience
Build and deploy models with unprecedented ease. Simplified workflows reduce learning curves while powerful automation handles parallelization and optimization behind the scenes.
Scalable & Parallel Computing
Leverage the power of Julia for high-performance computing, with built-in support for composable multi-threading, distributed computing, and GPU acceleration. Effortlessly scale your computations from a single CPU/GPU instance to a cluster of machines.
Any Computing Environment
Deploy on your laptop, private data centers, or leading cloud platforms (AWS, Azure, Google Cloud). From desktop workstations to supercomputers, TPUs, and GPU clusters - Pumas runs everywhere.
AI-Enhanced Modeling
Integrate machine learning seamlessly into pharmacometric workflows with built-in explainability features for regulatory compliance and validation.
Single Language Solution
From interactive research to production deployment, Julia eliminates the traditional two-language problem. No translation overhead, no workflow breaks.
Contact Sales
Learn about our products, pricing, implementation, and how JuliaHub can help your business
We’ll use your information to respond to your inquiry and, if applicable, classify your interest for relevant follow-up regarding our products. If you'd like to receive our newsletter and product updates, please check the box above. You can unsubscribe at any time. Learn more in our Privacy Policy.
Get a Demo
Discover how Dyad, JuliaHub, and Pumas can improve your modeling and simulation workflows.
Enterprise Support
Leverage our developers, engineers and data scientists to help you build new solutions.
Custom Solutions
Have a complex setup that needs a custom solution? We are here to help.
Contact Sales
Learn about our products, pricing, implementation, and how JuliaHub can help your business
We’ll use your information to respond to your inquiry and, if applicable, classify your interest for relevant follow-up regarding our products. If you'd like to receive our newsletter and product updates, please check the box above. You can unsubscribe at any time. Learn more in our Privacy Policy.
Contact Sales
Learn about our products, pricing, implementation, and how JuliaHub can help your business
We’ll use your information to respond to your inquiry and, if applicable, classify your interest for relevant follow-up regarding our products. If you'd like to receive our newsletter and product updates, please check the box above. You can unsubscribe at any time. Learn more in our Privacy Policy.
Get a Demo
Discover how Dyad, JuliaHub, and Pumas can improve your modeling and simulation workflows.
Enterprise Support
Leverage our developers, engineers and data scientists to help you build new solutions.
Custom Solutions
Have a complex setup that needs a custom solution? We are here to help.