The Isomorphic Labs Drug Design Engine unlocks a new frontier beyond AlphaFold - Isomorphic Labs

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The Isomorphic Labs Drug Design Engine unlocks a new frontier beyond AlphaFold

February 10, 2026

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Today, we are excited to share an update on our progress towards a new frontier of drug design. We have unlocked a new paradigm of predictive accuracy in understanding our biomolecular world, allowing us to rationally design new medicines on a computer with unprecedented understanding and precision.

We are giving a glimpse at a subset of the powerful and expansive capabilities of the Isomorphic Labs Drug Design Engine (IsoDDE), a unified computational drug-design system, progressing beyond AlphaFold 3 (AF3) in its predictive accuracy and introducing new capabilities which bridge the gap between structure prediction and real-world drug discovery.<br>We demonstrate that our IsoDDE more than doubles the accuracy of AlphaFold 3 on a challenging protein-ligand structure prediction generalisation benchmark, predicts small molecule binding-affinities with accuracies that exceed gold-standard physics-based methods at a fraction of the time and cost, and is able to accurately identify novel binding pockets on target proteins using only the amino acid sequence as input.<br>IsoDDE offers a scalable foundation for AI drug design, providing the predictive fidelity required to navigate novel biological systems with unprecedented accuracy.<br>Since our release of AlphaFold 3 in 2024 together with Google DeepMind, the field of AI drug discovery has moved at an extraordinary pace. Whilst AlphaFold 3 delivered a dramatic leap in performance from previous generations of structure prediction models, a key challenge remained: understanding biomolecular structures alone was not sufficient for unlocking real-world drug discovery programs in silico (on a computer).<br>Progress in rational drug design - vital for solving human disease - requires highly accurate predictive models, across an expansive range of biochemical properties and interactions, that are able to work in concert with one another. Crucially, with so much of biological and chemical space still unexplored, these models need the ability to generalise their predictive power beyond their training sets to novel, unseen systems.<br>As we continue to address these challenges, we are excited to introduce the Isomorphic Labs Drug Design Engine (IsoDDE), and to preview a subset of IsoDDE's capabilities below and in our technical report.<br>Read Our Technical Report

Structure Prediction of Truly Novel Systems<br>Accurately predicting the structure of biomolecules and how they interact remains a crucial capability for rational drug design. Many critical downstream tasks are unlocked by being able to accurately model the small nuances in a protein’s geometry - whether understanding the impact of disease-causing mutations, or predicting which molecules will bind to a target protein.<br>AlphaFold 3 transformed protein-ligand structure prediction at the time of its release and the freely available AlphaFold Protein Database accelerated science on a scale that was previously unimaginable. To date, it has been used by over 3 million researchers in more than 190 countries.<br>Benchmarks have subsequently revealed that there remained a gap in accuracy for structures that were dissimilar to the examples AlphaFold 3 had been trained on. In other words, that it can struggle to generalise to unexplored regions of biomolecular space where some of the biggest challenges and opportunities in drug discovery lie.<br>IsoDDE demonstrates a step change in the ability to generalise to protein-ligand structures that are highly dissimilar to those in its training set.<br>On the 'Runs N' Poses' benchmark (Škrinjar et al. 2025) - designed specifically to test generalisation to novel pockets and ligands - IsoDDE more than doubles the accuracy of AlphaFold 3 on the most difficult systems.

Protein-ligand structure prediction accuracy on hardest generalisation category ((0-20])- most dissimilar to the training set - from the 'Runs N’ Poses' benchmarkIn the report, we demonstrate through several examples that we can successfully model complex, out-of-distribution events such as induced fits (where a protein adapts its shape to accommodate a bound ligand) and the opening of cryptic pockets (those hidden in the absence of a bound ligand) - critical biological mechanisms - even when these systems are distant from the training sets of such models.

IsoDDE is able to successfully predict the structure of a protein-protein interaction inhibitor bound to a cryptic pocket on the NKG2D homo-dimer interface (8EA6) from the lowest 0-20 similarity bin of the Runs’n’Poses test set (AlphaFold...