Category Archives: Machine Learning

Is the race for AGI a scam? Are the tech bros con men or visionaries?

To find out, we formally introduce the Stinkometer

In research, it can be tough even for experts to tell fact from fiction. This is especially true for more speculative fields like AI. It can feel like every model is “State-of-the-Art” and that AGI (whatever that means) will be “6 months away” for at least the next 6 months. How do we know we can trust these people?

What is a visionary? What is a con man?

Visionaries sell a fringe idea of the future, usually with strong self-belief and charisma. Con men are visionaries who don’t believe what they’re saying. It’s hard to tell the difference because we can’t read minds and can’t see the future.

I solved this by adapting the insightful “New Political Compass” from Harper O’Connor, an American political YouTuber. Harper found a similar problem. He’s active in local politics and has to decide who he should build alliances with to tackle certain issues. Interestingly, the “left-right” axis wasn’t a helpful guide. Most people he canvassed were reasonable but didn’t follow politics closely enough to have a robust ideology. He realised that someone’s psychology can be more important than their stated ideology.

Therefore, it was more useful to ask: Am I talking to a reasonable person? Here, I expand his framework into the Belief Compass (or the “Stinkometer”). We can answer our question by answering three simpler ones:

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Predicting ADME Properties with Machine Learning: 82% of the Performance From Two Descriptors

A Nature survey of ~1500 scientists reported that more than 70% had failed to reproduce another scientist’s experiments, and 50% had failed to replicate their own (Baker, 2016). This study wasn’t specific to machine learning, but the crisis has its own flavour in computational drug discovery.

One aspect of this is that available datasets have known quality issues, and results built on them can be fragile. MoleculeNet and the Therapeutic Data Commons (TDC) opened drug discovery to a wider ML community, but they’re no longer sufficient for driving further advances (Wognum et al., 2024).

Amongst a number of issues, 71% of molecules in one MoleculeNet dataset (BACE) contained at least one undefined stereocenter (a point where the same atoms can sit in two different 3D arrangements), making it unclear what chemical entity is actually being modelled (Li et al., 2026).

The stakes are high. In 2017, a research group found their cancer-target inhibitor inactive from one vendor and highly active from another. Eventually they traced this to vendors selling different mixtures of the compound’s two 3D forms where only one was active on the target and mechanism they were investigating (Baker, 2017).

Most approved pharmaceuticals are relatively small chemical molecules, typically weighing under 900 g/mol, with most of the rest being biologics. Whilst in Oxford on the UNIQ+ summer internship programme, I focused on ADME, an aspect of early stage drug discovery, where assays are measured in vitro as a stand-in to predict how a compound will behave in vivo (in the human body) before clinical trials. Absorption (does it enter the body), Distribution (where does it go), Metabolism (how quickly it’s transformed) and Excretion (how quickly it’s eliminated) are processes critical to whether a candidate succeeds. 

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Beyond the Vaccine: AI’s Expanding Role in LNP mediated Drug Delivery

Lipid nanoparticles (LNPs) have evolved from a specialised drug-delivery technology into a cornerstone of modern medicine. Their success became evident during the COVID-19 pandemic, enabling the delivery of the mRNA used in the Pfizer-BioNTech and Moderna vaccines. However, their applications extend far beyond vaccination, as LNPs can also deliver siRNA, plasmid DNA and gene-editing machinery, creating opportunities to treat genetic diseases, cancer and many other conditions.

Yet a fundamental challenge remains: how do we design an LNP that delivers the right payload, to the right cells, in the right place?

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Diffusion Models Can’t Give You a Likelihood… So How Do We Score Inverse-Folded Sequences?

I’ve been working with inverse folding models for sequence design for a while now, and one question kept coming up. How would you score a sequence’s likelihood under these models? It turns out the answer depends heavily on whether the model is autoregressive or diffusion-based.

Autoregressive models, like GPT-style language models, make this straightforward. You can calculate the probability of a sequence directly, by breaking it into one prediction per position and multiplying them together. Diffusion models define a probability distribution over outputs too, but getting the likelihood of a particular output means accounting for many unobserved intermediate states, and that turns out to be much harder. This is why diffusion models typically rely on the Evidence Lower Bound, or ELBO, a computable estimate, rather than the exact likelihood itself.

The easy case: autoregressive models

An autoregressive model breaks the probability of a sequence into a product of conditional probabilities:

p(x1:n)=∏i=1np(xi|x<i)p(x_{1:n}) = \prod_{i=1}^{n} p(x_i \mid x_{<i})
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App-ready databases from Python with SQLModel and FastAPI

Python is ubiquitous for those of us coming into programming from academic research backgrounds, both for generating scientific data and for analysing it. Of course, modern data-driven research only thrives when data is FAIR (findable, accessible, interoperable, and reusable) and traditional relational databases and APIs for interacting with them are a tried-and-true method of ensuring FAIRness. (And in the age of machine learning, the machines need things to be FAIR arguably even more than we do…)

“But I’m a data scientist/computational chemist/bioinformatician!” you protest. “I just (ab)use databases using Python, I don’t make them for someone else to use!” Understandable, but the barrier to entry may be much lower than you think, thanks to two related Python libraries: SQLModel and FastAPI. These powerful tools by developer Sebastián Ramírez (@tiangolo on GitHub) work in concert to make relational databases and APIs much easier to implement for Python natives. Let’s see how they can help…

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Peering Inside the Black Box: A Beginner’s Introduction to Mechanistic Interpretability

Over the last few years, large language models (LLMs) have gone from being curiosities tucked away in research labs to something most of us interact with on a daily basis; whether for drafting emails, debugging code, or simply pondering the meaning of life at 2am. And yet, for all our reliance on these systems, a rather inconvenient truth lingers in the background: nobody, not even the people who built them, can fully explain what is going on inside.

This is where mechanistic interpretability comes in.

In essence, mechanistic interpretability is the approach of explaining complex machine learning systems through the behaviour of their functional units (Kästner and Crook, 2024) by reverse-engineering them into their more elementary computations (Rai et al., 2025). The aim is not simply to know that a model gives the right answer, but to pull apart the underlying machinery and uncover the causal relationships between input and output. Think of it as neuroscience for neural networks, except we can read every neuron at any moment, rewind, replay, and intervene mid-thought.

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Will TurboQuant save us from the RAM apocalypse?

The LLM boom is causing a global shortage of the very same computer memory it needs to sustain itself. Reports suggest OpenAI’s Stargate project alone could consume up to 40% of global DRAM output. Frontier labs like Google DeepMind need to make their models more memory-efficient.


One such technique is TurboQuant, released by Google. TurboQuant is an example of an online “quantisation” method. LLMs represent information using large tensors of numerical values, where each number typically uses 64 or 32 bits. However, many values do not require full numerical precision, so we can “round” them using fewer bits and less memory. We can see this in the example below:

The rounded value now requires 4x less memory. Source

Some quantisation methods are applied offline before inference begins. TurboQuant is ‘online’ because it compresses the KV cache dynamically during inference.

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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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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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