Active learning FEP

Combining fast predictive power with accurate affinity prediction to prioritize bioisosteres

Accelerate lead optimization

An enhanced new molecule discovery workflow

A powerful active learning workflow promises major advances in accelerating lead optimization time and productivity. By combining fast predictive power with rigorous and accurate affinity prediction, the workflow generates an iterative feedback loop that effectively triages candidate molecules in silico, helping medicinal chemists to focus on and prioritize only the molecules with the best chance of success.

Learn more...

Publication

Active learning FEP using 3D-QSAR for prioritizing bioisosteres in medicinal chemistry

Abstract Pacritinib, an orally bioavailable macrocyclic inhibitor of Janus Kinase 2, has shown efficacy for the treatment of myelofibrosis....

Webinar

Active learning FEP using 3D-QSAR for prioritizing bioisosteres in medicinal chemistry

We present an active learning workflow using Free Energy Perturbation to efficiently identify the strongest-binding bioisosteric replacement...

Whitepaper

Empowering medicinal chemists with AI

AI has garnered much interest and significant investment dollars in recent years but has yet to give a step change in the new molecule design process....

Accurate prediction of bioactivity at a fraction of computational cost

CSO Mark Mackey distils the results from the active learning workflow

Mark walks through the active learning workflow demonstrating the efficiency gains obtainable from the combination of physics-based computational technique free energy perturbation (FEP) with AI/ML.

Flexible implementation to drive new molecule generation

The active learning workflow is highly flexible enabling multiple methods for new molecule generation to be used in combination including AI-generative chemistry, rules based hit expansion as well as using approaches such as Cresset’s Spark™ software.

Learn how our platform can drive a step change in your discovery process

What our customers say

“As a global computational chemistry team within a contract research organisation (CRO), we aimed to improve the consistency, scalability, and scientific rigor of our molecular modelling workflows across a wide range of client projects. Cresset supported a smooth transition from a previously used commercial tool and helped address key limitations around accessibility and deployment at scale. A differentiated licensing model has enabled broad access across chemistry and biology teams while reserving advanced capabilities for computational chemistry specialists. This approach has helped balance accessibility, scientific depth, and efficient use of resources, enabling wider engagement without compromising rigor. The Cresset team have been highly responsive and knowledgeable, working closely with us to support adoption across a global user base. Training was organised according to user needs, with regular office hours providing an open forum for questions and ongoing support.

Overall, Cresset has become a valuable part of our computational chemistry toolkit, strengthening consistency, productivity, and confidence in decision-making across our organisation.”

Nicola Porta, Senior Scientist, Evotec

We are pleased to be working with Cresset’s software. This provides us with intuitive molecule design, a powerful ability to understand SAR and design plus a fresh way for generating novel and diverse structures for our projects.

Dr Kristian Birchall, Medical Research Council Technology, UK

We really like the powerful combination of excellent software tools and molecular design expertise which we get from working with Cresset.

Dr Graham Sibley, Head of Chemistry, F2G, UK

We wanted to use computational methods to suggest alternative substructures which could replace part of one of our key leads. We’d looked at other tools previously, without success, then turned to the Spark™ field-based approach. We looked at the top 50 hits from Spark and selected a subset to synthesise. Whilst the synthesised compounds exhibited a broad range of activities, we were delighted to observe that a number turned out to be nanomolar hits – with several even more potent than the original lead. One of these compounds is now our key lead against this target and further analogue synthesis is underway. Spark has certainly helped direct us towards some novel and very useful SAR on this occasion.

Laurent Rigoreau, Group Leader Medicinal Chemistry, Cancer Research Technology Discovery Laboratories

Cresset Discovery’s computational experience and high standard of scientific support is ideally suited to help drive the next stage of this project.

Dr Richard Boyce, Senior Director of Research, PhoreMost

The protein interaction potential capability [in Flare] highlighted common features across the family of targets of interest that we had not been able to visualize before. We used this information to drive ligand design into a direction we have not explored before.

Dr Martin Quibell, MAQU Chemistry Consulting, UK

We sought the scientific expertise within the Cresset Discovery team to utilize their novel field and shape-based virtual screening methods in Blaze™, and their proprietary Electrostatic Complementarity™ technique to help identify a backup series for our target effectively and efficiently. Ultimately we identified 9 novel chemotypes that were clearly distinct to those arising from docking and pharmacophore virtual screening approaches, which was an exciting outcome for us.

Dr Zara Sands, Head of CADD and Informatics, Confo Therapeutics, Belgium

Working closely with the Cresset Discovery team we aimed to identify a putative binding pose for a lead series in published PDB coordinates that was not only consistent with the kinetically deduced MOA but explained the established SAR as well. The furnished results were able to satisfy these goals and then be applied prophetically for ligand design purposes.

Twentyeight-Seven Therapeutics, USA

Medicinal chemists in my team find the covalent docking feature in Flare™ very intuitive to use.

Matthias Bauer, AstraZeneca, UK