Some thoughts on molecular similarity

Molecular similarity is a tricky concept, mostly because there are many ways to define and measure similarity. For example, two molecules could be considered similar because they have the same biological effect, or because they have identical molecular weight, or because they contain the same functional groups, etc., etc. A natural follow-on question from this is “what is the correct way to measure molecular similarity?” and the answer, unfortunately, is that it depends.

As an example of these complexities, Greg Landrum has a great blog post on how Tanimoto similarity changes depending on how you vectorise a molecule, and the need for authors to clarify the vectorisation method used. Variation in Tanimoto similarities is also something Ísak has written about on blopig.

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An Introduction to the Basics of Reinforcement Learning

Reinforcement learning (RL) is pretty simple in theory – “take actions, get rewards, increase likelihood of high reward actions”. However, we can quickly runs into subtle problems that don’t show up in standard supervised learning. The aim of this post is to give a gentle, concrete introduction to what RL actually is, why we might want to use it instead of (or alongside) supervised learning, and some of the headaches (figure 1) that come with it: sparse rewards, credit assignment, and reward shaping.

Figure 1: I’d like to help take you from confusion/headache 🙁 (left) to having a least some clarity 🙂 (right) with regard to what reinforcement learning is and where its useful

Rather than starting with Atari or robot arms, we’ll work through a small toy environment: a paddle catching falling balls. It’s simple enough to understand visually, but rich enough to show how different reward designs can lead to completely different behaviours, even when the underlying environment and objective are the same. Along the way, we’ll connect the code to the standard RL formalism (MDPs, returns, policy gradients), so you can see how the equations map onto something you can actually run.

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Dispatches from Lisbon

Tiles, tiles, as far as the eye can see. Conquerors on horseback storming into the breach; proud merchant ships cresting ocean waves; pious monks and shepherds tending to their flocks; Christ bearing the cross to Calvary—in intricate tones of blue and white on tin-glazed ceramic tilework. Vedi Napoli e poi muori the Sage of Weimar once wrote—to see Naples and die. But had he been to Lisbon?

The azulejos of the city’s numerous magnificent monasteries are far from the only thing for the weary PhD student to admire. Lisbon has no shortage of imposing bridges and striking towers, historically fraught monuments and charming art galleries. Crumbling old castles and revitalised industrial quarters butt up against the Airbnbs-and-expats district, somewhere between property speculation and the sea. An endearing flock of magellanic penguins paddles away an afternoon in their enclosure at the local aquarium (which is excellent), and an alarming proliferation of custard-based pastries invites one to indulge.

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Controlling the Diffusion Denoising Process: A Molecular Show

This blog post is supporting my poster at Young Modellers Forum and makes things way easier to see and understand. Underneath each GIF, is the explanation of what you should look for as things denoise throughout the diffusion trajectory. Click the GIFs for higher quality viewing!

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Design your very own drug: An introduction to structure-based small molecule drug design

Are you curious about how scientists design small molecules to treat disease using computational tools, but the words RDKit, docking, and QED mean nothing to you? Look no further than these tutorials for learning the fundamentals of computational small molecule drug design through interactive tutorials that introduce the key tools, concepts, and workflows. From generating compounds to evaluating their drug-likeness and binding potential, by the end you’ll be ready to explore how computational methods can result in the discovery of your very own (virtual) drug candidates to cure Zika!

Find the materials here: https://github.com/oxpig/dtc-struc-bio-smolecules/tree/main.

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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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I Prompt, Therefore I Am: Is Artificial Intelligence the End of Human Thought? 

Welcome to a slightly different blog post than usual. Today I am sharing an insight into my life at Keble College, Oxford. I am the Chair of Cheese and Why?, which is a talk series we host in our common room during term. The format is simple: I provide cheese and wine, and a guest speaker provides the “why”—a short, thought-provoking talk to spark discussion for the evening.

To kick off the series, I opened with the question of artificial intelligence replacing human thought. I am sharing my spoken essay below. The aim of a Cheese and Why? talk is to generate questions rather than deliver answers, so I hope you’ll forgive me if what follows doesn’t quite adhere to the rigorous structure of a traditional Oxford humanities essay. For best reading, I recommend a glass of claret and a wedge of Stilton, to recreate the full Oxford common-room experience.

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Nanobodies® galore in Utrecht

At the end of September, I had the opportunity to present at the 4th Single-Domain Antibody (sdAb/VHH) Conference hosted in the city of Utrecht. The sdAb conference is a biennial event, and was held for the first time in Bonn (2019), then in Brussels (2021) and Paris (2023), before coming to the Netherlands this year.

This was the first time I’d attended a VHH-focused conference, and I was taken aback at just how large the community is; the Jaarbeurs ‘Supernova’ event hall was completely sold out, with over 400 researchers in attendance (pictures below courtesy of the organisers). The buzz reflects the ever growing interest in sdAbs as tools to discover new fundamental biology, vectors for diagnosing disease, and as prophylactic or curative therapeutics. Most every disease indication was represented at the conference, from anticancer and antiviral sdAbs to antivenom sdAbs (both for use in lateral flow tests to diagnose the snake that bit you, and as quick ‘epipen’-like therapeutics accessible even in the most remote parts of the world).

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Using Node-RED as a front-end to your software

Node-RED is an, open-source, visual programming tool that lets you wire together hardware (such as sensors), APIs (such as REST/POST) and custom functions. However, its custom functions aren’t simply the JavaScript you write, they can also be containers!

This can provide an intuitive front-end to otherwise difficult software. For example, you’ve written your magnum opus, you’ve even documented it (though no-one will ever read it) and to ensure maximum compatibility for the widest possible audience, you’ve containerised it. But it’s still a command-line driven application. Using node-RED you can make this accessible to an inexperienced audience.

Out of the box, node-RED’s quite pretty, you can string together nodes to perform functions that are useful. In this case, it’s for monitoring a log file, if the log doesn’t grow, something’s gone wrong, so email me to take a look at it.

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