Autonomous Molecular Discovery

ModelMole is the natural evolution in AI drug discovery.

By pairing physics-based rigor with swarm intelligence to find candidates that pure machine learning models never see.

We take the highest precautions in protecting the IP of our clients, as well as our own, therefore, what we shown here is only information vetted for disclosure.

We are inventors as much as we are a platform, and we keep pushing our orchestration further into chemical space no one has mapped and extending it at the same time into a Deep Candidate Intelligence.

Want a preview of where we're heading, including molecule IP tracking, Repurposing agents and how we utilise existing clinical trials?

Get in touch, we're happy to walk you through it.

We are on a journey of innovation in drug discovery

Working with ModelMole:

One suite, from first candidate to the clinic.

ModelMole is compensated per project, with other models available where they suit how you work.

Clients can use the full suite end to end, from the first de novo discovery through optimisation and IND, and into later stages or just parts of it to improve their own tools. Either way, ModelMole carries its own data forward at every step to keep the project moving.

Real End-to-end discovery!

02 Repurposing agent

A new agent built to find new value in drugs that already exist and are approved for use in humans, giving a faster path from a compound to a new indication, and a new medicine. It weighs each candidate-indication pairs against reimbursement and market-access reality — payer precedent, coverage pathways, and unmet need — so a repurposing lead isn't just scientifically plausible, but "biologically sound and commercially viable to bring to market”.

03 Deep candidate intelligence

Our agents are learning to look far downstream: from designing and designing molecules to anticipating how a candidate performs in a formulation as well as how best to administer it, either oral, subcutaneous, intravenous or topical.

Our agents are drawing on:

  • Clinical trial landscape, and screening for safety earlier and more rigorously.

  • Legal and IP landscape and checking for protectability while looking for new drugs.

04 Partner matching

Using its own agentic AI, ModelMole can identify the CROs, CDMOs, scientists, and other partners relevant to the success of your next drugmatched to the specific candidate and stage, not a generic directory search.

05 A dedicated IP track

We are developing a high through-put patent screening and patent filing tools that interact with your data, in order to design or repurpose in an area you can protect.

Pre-fill patent applications and integrate access to real lawyers who you can rapidly onboard into your projects.

01 De novo design

We're extending our orchestration engine even further into unexplored chemical space - designing candidates in the dark chemical spaces that have not yet been explored. The ModelMole engine that has changed everything.

The Next Evolution in Drug Discovery

A 3D model of a protein structure with ribbon visualization in blue, purple, and green colors on a black background.

Most AI systems are limited by their training data; they can only extrapolate from what they have seen.

ModelMole is Different.

By integrating high-accuracy physics-based methods with state-of-the-art AI, we can look further from the known chemical space. This allows ModelMole to "look into the dark" where datasets are sparse, identifying highly novel, effective, and patentable solutions that pure machine learning models miss.

We call it Intelligent multi-model orchestration

The Intelligent Orchestration

Our proprietary Swarm AI system doesn't rely on a single model. It utilises a network of multiple AI systems that cross-reference predictions to identify confidence levels.

ModelMole’s automatic active learning loop ensures high accuracy and creates a formidable competitive moat that grows stronger with every problem it solves, rapidly improving the system over time and with every new problem.

The ModelMole Platform

Most drug discovery platforms are either narrow point solutions or complex systems tied to specific vendors or infrastructure.

ModelMole is fully autonomous and infrastructure-agnostic—designed to run anywhere from enterprise clusters to a single workstation. ModelMole has a unique way of following up the user to achieve an optimal result of a drug discovery platform.

Autonomous Swarm Intelligence

Fully customisable

High-accuracy physics-based methods

Intelligent multi-model orchestration

Autonomous end-to-end process

Full project support available

Build your pipeline using ModelMole today!

Beyond Extrapolation: Illuminating the Dark
Physics-Based AI Integration
Traditional AI
ModelMole

By integrating high-accuracy physics-based methods with state-of-the-art AI, ModelMole can look further from the known chemical space.

"Look into the dark"

where datasets are sparse



→ Identifying highly novel, effective, and patentable solutions

Autonomous Swarm Intelligence
  • Multiple AI systems cross-reference predictions to identify confidence levels
  • Automatic gap-filling using computational chemistry
  • Automatic active learning loop
  • Grows stronger with every problem solved

The Competitive Moat

Active Learning

Automatic improvement loop
High accuracy with every problem

Continuous Growth

Stronger with each iteration
Compounding competitive advantage

Proven Track Record

Years of improvement
Continuously evolving system

End-to-End Discovery

⚗️ Drug Design
🗜️ Delivery
🧪 Formulation
📐 Stability & Optimization

Our Work

The ModelMole platform rapidly assesses biological systems and predicts promising candidates that might be missed by traditional approaches, opening doors to innovative biotech and pharmaceutical breakthroughs.

Our platform is continuously used by our clients to develop and optimise new proprietary and collaborative drugs candidates.

Discovery Pipeline

Candidates across discovery, development and approval

Please get in contact for any reference!

Publications and Research

Acknowledgments

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