Data management insights, product updates, and thought leadership on the foundational database designed for scientific discovery.

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High Level Chromatography Workflow 

Chromatography model-based Workflows throughout the drug development phases

Biologics end-to-end process modeling

chromatography-equations-example

Sargent Centre logo

Supersaturation plot - crystallization - the aim is to maintain the supersaturation below 1.5

Supersaturation plot - crystallization

pmh-core vs. CADET-python

ModelFlow™ Downstream Overview

data_pipeline_2

Hero - blog

The Langmuir isotherm - slide sample - with blur

The Langmuir isotherm - slide sample

ModelFlow™ Downstream Capabilities

Sargent Centre Themes

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chromatography-mechanisms

Modelflow Projects

Morphological Crystallization workflow app

crystallization app main screen overview

Modelflow Workflow

Workflow CMC

ARA - Screenshot - Process Flow Diagram Creation

Chromatography app results

V2 Drying Digital Twin With Virtual DOE Visual - light gradient

ModelFlow™ Downstream CEX

E2E Digital Twin - API

Founders Group 1

pmh-core Chromatography

Data Connectors

Best Practices:
- Match workflow to stage.  
Prioritize next experiments early.  
Define data requirements upfront.  

Practices to stay away from:
- Treating modeling as binary.  
- Overengineering early-stage processes.  
- Demanding unrealistic data inputs.

Modeling Workflow Best Practices

ARA what-if analysis creation phase with predefined what-ifs

ARA - Screenshot - What if analysis Creation

Inside a Mechanistic Chromatography Model: Physics, Equations and Data (Part 2)

Chromatography Model-Based Workflows to Accelerate Biologics Process Development (Part 1)) 

Chromatography Model-Based Workflows to Accelerate Biologics Process Development (Part 1)

Stop Guessing, Start Predicting: How E2E Modeling Solves Pharma's Costliest Scale-Up Challenges

Why biopharma teams need modeling workflows, not just models

Why pharma teams need modeling workflows, not just models

PolyModels Hub Raises £7 Million to Power Biopharma’s Digital Backbone

6 Signs You’re Focusing on the Wrong Workflow

PolyModels Hub Joins the Sargent Centre Industrial Consortium as Its First Startup Member

De-Risking Crystallization Scale-Up: A Mechanistic Approach to Morphological Control in Batch Crystallization

ModelFlow™ Downstream: Predictive Modelling for Biologics CMC

Accelerating Pharma Risk Assessment: How an AI-Enabled Tool Addressed a 300-Hour Manual Bottleneck

Faster Python Simulations, Without the Trade-offs: A Chromatography Case Study

Your Model Is Only as Trustworthy as the Data Pipeline Behind It

Modeling End-to-End Downstream Biologics Manufacturing with ModelFlow™

Explore the physics, governing equations, and data needed to build, calibrate, and scale mechanistic chromatography models for bioprocessing.

Inside a Mechanistic Chromatography Model: Physics & Data

Why biopharma teams need modelling workflows, not just models

Is your pharma process modeling overengineered or underpowered? Discover the 6 warning signs your workflow is misaligned with your program's maturity stage.

6 Signs You’re Focusing on the Wrong Workflow | ModelFlow

ModelFlow™ Downstream brings predictive mechanistic modelling to biologics DSP — mapping design spaces and surfacing scale-up risk with less material and time.

Predictive Modelling for Biologics CMC

PolyModels Hub becomes the first startup member of the Sargent Centre Industrial Consortium, accelerating pharmaceutical process development and manufacturing innovation.

PolyModels Hub Joins Sargent Centre to Drive Process Innovation

Discover how ModelFlow Downstream™ shifts biologics manufacturing from siloed unit optimization to connected, end-to-end system modeling with a digital twin.

Modeling End-to-End Downstream Biologics

Learn how ModelFlow's chromatography workflows accelerate biologics process development. Turn mechanistic modeling into scalable, accessible digital twins.

Chromatography Model-Based Workflows for Biologics

Learn how mechanistic modeling de-risks crystallization scale-up. Control API morphology, ensure process stability, and accelerate pharmaceutical R&D workflows.

Mechanistic Modeling for API Crystallization Scale-Up & Shape

A Mechanistic Approach to Morphological Control in Batch Crystallization

Discover how a Top 10 pharma client used an AI-driven, human-in-the-loop system to transform unstructured lab notes into PFDs and TRAs, saving hours in manual data entry.

Pharma AI Case Study: Automating PFD & TRA Workflows

See how a Top 5 Pharma company saved $2.2M, reduced CO2 by 115 tonnes, and accelerated timelines by 3 months using End-to-End mechanistic modeling.

E2E Modeling for Pharma Scale-Up: $2.2M Savings Case Study

Learn how a Top 10 Pharma company saved over $2M and accelerated drying scale-up by 3 months using mechanistic modeling.

Digital Twin for Drying Scale-Up: Pharma Case Study

Digital Twin for Drying Process Scale-Up

PolyModels Hub Raises £7 Million to Power Biopharma’s Digital Backbone!

PolyModels Hub secures £7 million to expand digital support for biopharma process development and production.

PolyModels Hub Raises £7 Million for biopharma digital backbone

PolyModels Hub Raises £7 Million to Power Biopharma’s Digital Backbone 

PolyModels Hub raises GBP 1.1m Seed to build the digital backbone for pharma process development

PolyModels Hub secures GBP 1.1m Seed funding to advance digital tools for pharma process development and support faster, reliable scale-up.