ModelMole

ModelMole We combine cutting-edge computational chemistry with advanced ML/AI algorithms to identify novel therapeutic targets.

14/05/2026

While it can be difficult to directly compare technologies we can get decent estimates in the baseline unit of currency in this business from what machines different orgs use and for how long they use them.

Here we see how a traditional bruteforce screening approach uses huge amounts of energy, and while traditional AI approaches are better, ModelMole was able to blaze past both in efficiency using its Search in the Dark technology to achieve the same results at 50x less energy usage!

Lucky to be seated next to at the RSC Investment Catalyst Event. Great conversation and a very productive discussion.
10/03/2026

Lucky to be seated next to at the RSC Investment Catalyst Event. Great conversation and a very productive discussion.

22/01/2026

A year ago i asked an LLM to convert some of my python to CUDA, it took 5 hours and a lot of manual intervention.

Today I asked to convert some python to multithreaded rust and it did the whole thing in 1 shot in under 10 minutes.

🌟 Key Highlights of 2025:✅ Fueling Innovation: We were honored to receive a £90k grant from the UK Government, recognizi...
02/01/2026

🌟 Key Highlights of 2025:
✅ Fueling Innovation: We were honored to receive a £90k grant from the UK Government, recognizing our mission and potential to drive change in AI directed drug discovery.
✅ Scaling Up: With the support of significant private-sector investment, we’re now better equipped to accelerate our vision and bring even more value to our partners.
✅ Global Reach: This year, we welcomed new clients across the Nordics, EU, and China.
✅ Team Growth: Our team expanded its capacity by transitioning all members to full-time roles.

Both rely on iterative improvement:In quantum chemistry, we start with a guess for orbitals or electron density, then up...
30/12/2025

Both rely on iterative improvement:
In quantum chemistry, we start with a guess for orbitals or electron density, then update them until the solution converges (the SCF loop).

In AI, we start with random weights and iteratively adjust them to minimize loss.

It’s the same mindset: make a guess, measure error, refine, and repeat until you reach a consistent, useful solution.

And here’s where it gets powerful: DFT isn’t just like AI, it fuels AI. High-quality DFT calculations generate reliable data on molecular energies, reaction barriers, and material properties.

That data trains machine learning models to predict real-world behavior without running expensive simulations every time, from drug discovery to battery design. So while DFT and AI both “learn” through iteration, together they’re accelerating science faster than either could alone.

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Lynwood House, Crofton Road
Orpington
BR68QE

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