Category Archives: Proteins

Spin Lattices and Proteins – How state-based discretisations have enabled modern protein modelling

I got into protein modelling not long before AlphaFold2 first released. At that time some of the prevailing methods for protein structure prediction came from highly interpretable energy functionals that arose from a particularly beautiful intersection of statistical mechanics and biology. These “Potts” models are going to be the centre of a larger discussion in this blog on state-based discretisations of proteins, how they’ve shaped modern deep learning methods and whether there is still more to learn from them.

In the age of black box deep learning, does the Potts model still have a place?

The Potts/Ising Model

The Ising model is a well established popular theoretical physics model of ferromagnetism. Simply put, given a lattice of atoms each capable of adopting 1 of 2 spins (up and down) ferromagnetism arises when their spins align and their associated magnetic moments point in the same direction. The Ising model tries to parameterise the local and non-local relationships between atoms and their spin states such that we can learn the Hamiltonian of the system and its different configurations under the magnetic field. The Hamiltonian takes the following form for a system of N atoms


$$
E = -\sum_{i}^Nh_ix_i – \sum_{i<j}^N J_{ij}x_i x_j,
$$

where J is the “coupling energy” between any two atoms x_i and x_j, and h represents the magnetic field, or more appropriately for our purposes it can be framed as a single-site field dictating how an individual atom independently acts within the model. You might recognise the form this binary spin model takes as it arises naturally across the sciences including in Hopfield networks and graphical models.

Everything is an Ising-like model if you’re brave enough

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Revealing Nature’s Quantum Compass – Kickoff Day

Yesterday marked the kickoff for the BBSRC’s funded Strategic Longer and Larger (sLoLa) scheme “Revealing Nature’s Quantum Compass”1. The sLoLa grants are a laudable endeavor by the UK government to fund “ambitious research projects that will deepen our understanding of life’s most fundamental processes”. It is wonderful to see the UK government taking seriously the importance of blue sky basic research, appreciating that asking deep questions is what drives scientific progress, often leading to unexpected breakthroughs with application down the line.

At the kickoff event, principal investigators presented on what their research can bring to the table. Much like entering a bakery2 where everything smells delicious and it seems impossible to choose, an overwhelming range of experimental and computational techniques were presented, each bringing to bear their own unique approach to tackling the outstanding problem: mechanistically, how is that birds (and other animals) can navigate distances up to thousands of kilometers using the Earth’s magnetic field. Alongside this, my own group is interested in how we can develop biotechnologies that take advantage of magnetic field sensitive biochemistry, which has a host of applications near and long term.

The challenge of linking the biochemistry of a single protein known to be magnetic field sensitive to a behavioral phenotype will require a highly interdisciplinary approach, and excitingly for this community, machine learning is being involved from the start. Prof. Degiacomi, a member of the core team, presented how his lab is developing ML techniques to reduce the computational burden of linking experimental results to protein dynamics informed by molecular dynamics simulation. On the flip-side, I hope such techniques will develop into methods we can use for design. Similar to enzymes, the proteins we are interested have a function depending on mechanisms far more complex than only structure and binding (not to trivialize either of these!). Magnetic field sensing in this context depends on creating an environment in which quantum entanglement can exist, and being able to transduce the state of this quantum entanglement into into a biological signal – thus far this second step in particular has remained highly elusive.

Ultimately, the day concluded with much enthusiasm and excitement for all that is to come. Watch this space!

  1. https://www.ox.ac.uk/news/2025-11-19-new-project-aims-reveal-nature-s-quantum-compass ↩︎
  2. Yes, I just returned from a symposium in Germany ↩︎

A Golden Age of Nanomedicine

As someone who spent their entire academic career, from B.Sc. to M.Sc. to Ph.D., within a Kavli Institute for Nanoscience Discovery (first in Delft and now in Oxford), I’ve had the privilege of seeing firsthand just how beautifully intricate the nanoscale world can be. Now, as my research focuses on lipid nanoparticles for genetic therapeutics and vaccines, I would like to use this platform to advocate for what I believe is one of the most transformative frontiers in modern medicine: the rational design of nanomaterials for therapeutic delivery.

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SigmaDock: untwisting molecular docking with fragment-based SE(3) diffusion

Alvaro Prat, Leo Zhang, Charlotte Deane, Yee Whye Teh, & Garrett M. Morris
International Conference On Learning Representations (ICLR 2026)

Molecular docking sits at the heart of structure-based drug discovery. If we can reliably predict how a small molecule binds in a protein pocket, we can prioritize compounds faster, reason about interactions more clearly, and build better pipelines for hit discovery and lead optimization. But in practice, docking is still a difficult problem: classical methods are often robust but imperfect, while recent deep learning approaches have sometimes looked promising on headline metrics without consistently producing chemically plausible poses.

SigmaDock was built to address exactly that gap. Instead of treating docking as a problem of directly diffusing on torsion angles or unconstrained atomic coordinates, SigmaDock represents ligands as collections of rigid fragments and learns how to reassemble them inside the binding pocket using diffusion on SE(3)\text{SE}(3). In plain English: rather than trying to “wiggle” every flexible degree of freedom in a tangled way, SigmaDock breaks the ligand into chemically meaningful rigid pieces and learns where those pieces should go, and how they should reorient, to recover a valid bound pose.

Figure 1: Illustration of SigmaDock using PDB 1V4S and ligand MRK. We create an initial conformation of a query ligand where we define our mm rigid body fragments (colour coded). The corresponding forward diffusion process operates in SE(3)m\text{SE}(3)^m via independent roto-translations.
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New DPhil/PhD Programme in Pharmaceutical Science Joint with GSK!

Many OPIGlets found their way into a DPhil in Protein Informatics through our Systems Approaches to Biomedical Sciences Industrial Doctoral Landscape Award, which was open to applicants 2009-2024. This innovative course, based at the MPLS Doctoral Training Centre (DTC), offered six months of intensive taught modules prior to starting PhD-level research, allowing students to upskill across a diverse range of subjects (coding, mathematics, structural biology, etc.) and to go on to do research in areas significantly distinct from their formal Undergraduate training. All projects also benefited from direct co-supervision from researchers working in the Pharmaceutical industry, ensuring DPhil projects in areas with drug discovery translation potential. Regrettably, having twice successfully applied for renewal of funding, we were unsuccessful in our bid to refund SABS in 2024.

Happily though, we can now formally announce that our bid for a direct successor to SABS, the Transformative Technologies in Pharmaceutical Sciences IDLA, has been backed by the BBSRC, and we will shortly be opening for applications for entry this October [2026]. As someone who benefited from the interdisciplinary training and industry-adjacency of SABS, I’m thrilled to be a co-director of this new Programme and to help deliver this course to a new generation of talented students.

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What Molecular ML Can Learn from the Vision Community’s Representation Revolution

Something remarkable happened in computer vision in 2025: the fields of generative modeling and representation learning, which had developed largely independently, suddenly converged. Diffusion models started leveraging pretrained vision encoders like DINOv2 to dramatically accelerate training. Researchers discovered that aligning generative models to pretrained representations doesn’t just speed things up—it often produces better results.

As someone who works on generative models for (among other things) molecules and proteins, I’ve been watching this unfold with great interest. Could we do the same thing for molecular ML? We now have foundation models like MACE that learn powerful atomic representations. Could aligning molecular generative models to these representations provide similar benefits?

In this post, I’ll summarize what happened in vision (organized into four “phases”), and then discuss what I think are the key lessons for molecular machine learning. The punchline: many of these ideas are already starting to appear in our field, but we’re still in the early stages compared to vision.

For a more detailed treatment of the vision developments with full references and figures, see the extended blog post on my website.

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Is the molecule in the computer?

The Molecular Graphics and Modelling Society began life as the Molecular Graphics Society. It’s hard to imagine a time without computer graphics, but yes, it existed. The MGS was formed by the pioneers who made molecular graphics commonplace.

In 1994, the MGS organized an Art and Video Show (Goodsell et al., 1995), and I submitted some of my own work. One of the other images — inspired by Magritte‘s “Ceci n’est pas une pipe”, depicts a molecule with a remarkable similarity to a pipe — and to a molecule… It was submitted by Mike Hann (of GSK):

“Ceci n’est pas une molecule”, image by Mike Hann, 1994.
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Is attention all you need for protein folding?

Researchers from Apple have released SimpleFold, a protein structure prediction model which uses exclusively standard Transformer layers. The results seem to show that SimpleFold is a little less accurate than methods such as AlphaFold2, but much faster and easier to integrate into standard LLM-like workflows. SimpleFold also shows very good scaling performance, in line with other Transformer models like ESM2. So what is powering this seemingly simple development?

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Accelerating AlphaFold 3 for high-throughput structure prediction

Introduction

Recently, I have been conducting a project in which I need to predict the structures of a dataset comprising a few thousand protein sequences using AlphaFold 3. Taking a naive approach, it was taking an hour or two per entry to get a predicted structure. With a few thousand structures, it seemed that it would take months to be able to run…

In this blog post, I will go through some tips I found to help accelerate the structure predictions and make all of the predictions I needed in under a week. In general, following the tips in the AlphaFold 3 performance documentation is a useful starting place. Most of the tips I provide are related to accelerating the MSA generation portion of the predictions because this was the biggest bottleneck in my case.

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How reliable are affinity datasets in practice?

The Data Bottleneck in AI-Powered Drug Discovery

The pharmaceutical industry is undergoing a profound transformation, driven by the promise of Artificial Intelligence (AI) and Machine Learning (ML). These technologies offer the potential to escape the industry’s persistent challenges of high costs, protracted development timelines, and staggering failure rates. From accelerating the identification of novel biological targets to optimizing the properties of lead compounds, AI is poised to enhance the precision and efficiency of drug discovery at nearly every stage

Yet, this revolutionary potential is constrained by a fundamental dependency. The power of modern AI, particularly the deep learning (DL) models that excel at complex pattern recognition, is directly proportional to the volume, diversity, and quality of the data they are trained on. This creates a critical bottleneck: the high-quality experimental data required to train these models—specifically, the protein-ligand binding affinity values that quantify the strength of an interaction—are notoriously scarce, expensive to generate, and often of inconsistent quality or locked within proprietary databases.

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