the platform.
ModelMole’s AI agents remove the complexity, specialized expertise, and workflow fragmentation that slow discovery.
Simply describe your target and ModelMole designs the workflow, runs the simulations, validates the results, and delivers decision-ready compounds and support selection of the pipeline.
No computational chemistry expertise.
No complex software coding, integration or orchestration.
Just faster, smarter drug discovery.
The ModelMole Difference
ModelMole combines the flexibility of AI models, the rigor of physics-based simulation, and the simplicity of full automation - without forcing scientists to choose between control, scale, or usability.
At its core, the technology employs machine learning algorithms that are able to autonomously direct computational simulations and learn from the outcomes to improve themselves.
These algorithms learn complex relationships between molecular features and desired outcomes, enabling in silico creation of new biotechnologies.
Computational chemistry methods, including molecular dynamics, quantum mechanical and docking calculations are integrated to build up high quality datasets for your target, refine predictions, assess compound viability and interactions with biological targets.
Autonomous Drug Discovery
ModelMole’s AI systems do two main tasks:
Design discovery pipelines that utilise and interconnect physics-backed computational chemistry methods (DFT, docking, molecular dynamics etc), OSS AI tools (Boltz2, DiffDock, Protenix, ProteinMPNN etc) and the ModelMole Core Intelligence.
ModelMole Core Intelligence learns from multiple sources defined in the pipeline optimising towards as many objectives your solution requires.
The ModelMole platform operates automatically via workflows – ‘pipelines’ – that connect various computational tools for iteratively producing better and better virtual libraries of candidate molecules.
ModelMole: AI-Driven Drug Discovery in Five Steps
1. Define Your Biological Target
For example, add a protein target for example through ModelMole’s interface. No expertise in computational chemistry required.
2. Autonomous Workflow
Describe what you need to happen - ModelMole’s AI agent automatically designs the optimal discovery workflow - selecting protocols, setting simulation parameters, and assembling a fully integrated computational pipeline.
3. Scalable Cloud Execution
The ModelMole Core Intelligence will test millions of compounds in using enterprise-grade cloud infrastructure. Fault-tolerant, integrated workflows ensure speed, reliability, and reproducibility at scale.
4. Built-In Quality Control
Automated filtering out low-quality candidates, ensuring only drug-like, high-confidence molecules that meet all objectives advance.
5. Final Select ion
We use a multi-objective selection process to curate candidates, ensuring chemical diversity and balancing key metrics like predicted efficacy, ADMET properties, and your project’s specific goals to maximize the chances of success.
