Category Archives: AI

Three Resources I Keep Coming Back to for Learning Deep Learning

There is no shortage of AI content online, but over time I have found myself returning to the same handful of resources again, and I wanted to share the three that have helped me the most.

AI Summer

This one I would recommend to anyone who is earlier in their journey. AI Summer at theaisummer.com is a free platform run by Sergios Karagiannakos and Nikolas Adaloglou, and it covers everything from the basics of neural networks through to building and deploying real ML systems. The tone is friendly and practical, and there are proper code examples throughout. It is one of those rare resources that manages to be beginner-friendly without feeling watered down.

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Analyzing AlphaFold 3’s Diffusion Trajectory

A useful way to understand AlphaFold 3’s sampling behavior is to look not only at the final predicted structure, but at what happens along the reverse diffusion trajectory itself. If we track quantities such as the physical energy of samples, noise scale, and update magnitude over time, a very clear pattern emerges: structures remain physically imperfect for most of sampling, and only take proper global shape in the final low-noise steps.

This behavior is a result of the diffusion procedure implemented in Algorithm 18, Sample Diffusion, which follows an EDM-style sampler with churn. Rather than simply marching monotonically from noise to structure, the sampler repeatedly perturbs the current coordinates, denoises them, and then takes a Euler-like update step. Because of the churn mechanism, AlphaFold 3 deliberately injects additional noise during part of the trajectory, which encourages exploration but also delays local geometric convergence. This mechanism is shown in step 4 -7 of the Sample Diffusion Algorithm from Alphafold3 Supplementary Information.

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No Pretraining, No Equivariant Architecture – Learning MLIPs without Explicit Equivariance

Paper
🤗 TransIP-L checkpoint
Code

Machine-learned interatomic potentials (MLIPs) have become a cornerstone of modern computational chemistry, enabling simulations that approach quantum accuracy at a fraction of the cost of traditional methods such as density functional theory (DFT). However, a central challenge in designing MLIPs lies in respecting the fundamental symmetries of molecular systems, especially rotational and translational invariance, while maintaining scalability and flexibility.

In our recent work, we introduced TransIP, a novel framework that formulates how symmetry is incorporated into molecular models by learning symmetry directly in the latent space of an atomic transformer model, in which we treat atoms as tokens, instead of hard-coding equivariance into the neural network architecture.

At the core of TransIP is a simple yet powerful idea: instead of enforcing SO(3) equivariance through specialized layers, the model is trained with a contrastive objective that aligns representations of rotated molecular configurations. A learned transformation network maps latent embeddings under rotations, encouraging the model to discover symmetry-consistent representations implicitly. This design preserves the flexibility and scalability of standard Transformers while still capturing the geometric structure of molecular systems.

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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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Building a “Second Brain” – A Functional Knowledge Stack with Obsidian

Whilst I always enjoy the acquisition of knowledge, I’ve always struggled with depositing it usefully. From pen and paper notes with a 20 colour theme which lost value with each additional colour, to OneNote or iPad GoodNotes based emulations of pen and paper, it’s been a constant quest for the optimal note taking schema. Personally there are 3 key objectives I need my note taking to achieve:

  1. It must be digitally compatible and accessible from any device.
  2. It must comfortably handle math and images.
  3. It must be something I look forward to – the software needs to be aesthetically clean, lightweight with none of the chunkiness of Microsoft apps, and highly customisable.

For me the solution to this was Obsidian, the perhaps more cultified sibling to Notion. Obsidian is a note taking application that uses markdown with a surprising amount flexibility, including the ability to partner it with an LLM which I’ll explore in this blog, alongside my vault organisation do or dies, and favourite customisations.

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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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Democratising the Dark Arts: Writing Triton Kernels with Claude

Why would you ever want to leave the warm, fuzzy embrace of torch.nn? It works, it’s differentiable, and it rarely causes your entire Python session to segfault without a stack trace. The answer usually comes down to the “Memory Wall.” Modern deep learning is often less bound by how fast your GPU can do math (FLOPS) and more bound by how fast it can move data around (Memory Bandwidth). When you write a sequence of simple PyTorch operations, something like x = x * 2 + y the GPU often reads x from memory, multiplies it, writes it back, reads it again to add y, and writes it back again. It’s the computational equivalent of making five separate trips to the grocery store because you forgot the eggs, then the milk, then the bread. Writing a custom kernel lets you “fuse” these operations. You load the data once, perform a dozen mathematical operations on it while it sits in the ultra-fast chip registers, and write it back once. The performance gains can be massive (often 2x-10x for specific layers).But traditionally, the “cost” of accessing those gains, learning C++, understanding warp divergence, and manual memory management, was just too high for most researchers. That equation is finally changing.

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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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Scientific Acceleration with Agentic Coding in 2026

In the past month we have surpassed a critical threshold with the capabilities of agentic coding models. What previously sounded like science fiction has now become reality, and I don’t believe any of us are ready for what is to come. In this blog post I share a summary of the breakthrough I am referring to, I give an insight into how I use agents to accelerate my research, and I make some predictions for the year. With pride, I can say this entire blog post was 100% written by me without any support from ChatGPT (except spell checking and the image below).

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Can we make Boltz predict allosteric binding?

Orthosteric vs Allosteric binding (Nano Banana generated)

(While this post is meant to shed light on the problem of making AI structure prediction models like Boltz become better for allosteric binding, it is also an open call for collaborating on this problem.)

I recently took part in a Boltz hackathon organised by the MIT Jameel Clinic. I worked on improving Boltz 2 predictions for allosteric binders. The validation dataset provided was from a recent paper, Co-folding, the future of docking – prediction of allosteric and orthosteric ligands, which benchmarks some of the recent state-of-the-art AI structure prediction models on a curated set of allosteric and orthosteric binders. Generally, all AI structure prediction models are trained mostly on orthosteric binding cases, which means that their performance on allosteric binding is significantly worse.

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