Experience at the Computational Chemistry Gordon Research Conference

This past July I had the absolute delight of attending the Computational Chemistry Gordon Research Seminar and Conference all the way in Portland, Maine. It was my first Gordon experience, which was invigorating seven-day experience with lots of great science and meeting great people!

Since pictures and videos are not allowed at GRCs as they support the presentation of unpublished results, I’ll talk more generally about the conference as a whole and the general science themes related to my work.

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Sort and Slice Tutorial – An alternative to extended connectivity fingerprints

ggPlotting tips with OPIG data

Ever wondered whether opiglets keep their ketchup in the fridge or cupboard? Perhaps you’ve wanted to know how to create nice figure to display lots of information simulataniously. Publication quality figures are easy in R with the ggplot package. We may also learn some good visualisation.

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Conference Summary: AIRR Community Meeting VII – Learnings and Perspectives

At the start of June, we (Lewis and Benjie) attended the AIRR Community meeting in beautiful and sunny Porto, Portugal. This meeting was focused on collecting and analysing adaptive immune receptor repertoires. This comprised of two rivalling factions at the conference: the antibody (Ab) people or the T cell antigen receptor (TCR) people. The split was nearly fifty-fifty between these two topics throughout the conference. Overall, the conference was a fairly comfortable size, with approximately a hundred people in attendance, making it easy to visit all of the posters and talk with many people in your area, without feeling too niche. There was a wide variety of content formats throughout the conference including posters, scientific talks, lightning talks, software demos, and hands-on tutorials. In the following section, we highlight some of our favourite sessions to give a flavour of what this meeting entails.

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Incorporating conformer ensembles for better molecular representation learning

Conformer ensemble of tryptophan from Seibert et. al.

The spatial or 3D structure of a molecule is particularly relevant to modeling its activity in QSAR. The 3D structural information affects molecular properties and chemical reactivities and thus it is important to incorporate them in deep learning models built for molecules. A key aspect of the spatial structure of molecules is the flexible distribution of their constituent atoms known as conformation. Given the temperature of a molecular system, the probability of each of its possible conformation is defined by its formation energy and this follows a Boltzmann distribution [McQuarrie and Simon, 1997]. The Boltzmann distribution tells us the probability of a certain confirmation given its potential energy. The different conformations of a molecule could result in different properties and activity. Therefore, it is imperative to consider multiple conformers in molecular deep learning to ensure that the notion of conformational flexibility is embedded in the model developed. The model should also be able to capture the Boltzmann distribution of the potential energy related to the conformers.

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Reproducible publishing

I’m a big fan of Jupyter Notebooks. They’re a great way to document and explain code, and even better, you can run this code when connected to an appropriate kernel.

What if you want to work on something larger than a notebook? Say a chapter or even a whole book, with Python, R, Observable JS, or Julia code? Enter Quarto. You can combine Jupyter notebooks and/or plain text markdown to publish production quality articles, presentations, dashboards, website, blogs and books in HTML, PDF, Microsoft Word, ePub, and other formats. Quarto can also connect to publishing platforms like Posit Connect, Confluence Cloud, and others.

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Architectural highlights of AlphaFold3

DeepMind and Isomophic Labs recently published the methods behind AlphaFold3, the sequel to the famous AlphaFold2. The involvement of Isomorphic Labs signifies a shift that Alphabet is getting serious about drug design. To this end, AlphaFold3 provides a substantial improvement in the field of complex prediction, a major piece in the computational drug design pipeline.

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Le Tour de Farce v11.0

“They don’t make them like they used to!”

With much experience of all things farcical, it was my delight to have returned just in time for the 2024 edition of OPIG’s Tour de Farce, which took place on 11th July. This year’s route was 8 miles long and encompassed four of the finest establishments Oxford has to offer (nothing “unusually conservative” to see here Eoin).

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The Tale of the Undead Logger

A picture of a scary-looking zombie in a lumberjack outfit holding an axe, in the middle of a forest at night, staring menacingly at the viewer.
Fear the Undead Logger all ye who enter here.
For he may strike, and drain the life out nodes that you hold dear.
Among the smouldering embers of jobs you thought long dead,
he lingers on, to terrorise, and cause you frightful dread.
But hark ye all my tale to save you from much pain,
and fight ye not anew the battles I have fought in vain.

Or simply…

… Tips and Tricks to Use When wandb Logger Just. Won’t. DIE.

The Weights and Biases Logger (illustrated above by DALL-E; admittedly with some artistic license) hardly requires introduction. It’s something of an industry standard at this point, well-regarded for the extensive (and extensible) functionality of its interactive dashboard; for advanced features like checkpointing model weights in the cloud and automating hyperparameter sweeps; and for integrating painlessly with frameworks like PyTorch and PyTorch Lightning. It simplifies your life as an ML researcher enormously by making it easy to track and compare experiments, monitor system resource usage, all while giving you very fun interactive graphs to play with.
Plot arbitrary quantities you may be logging against each other, interactively, on the fly, however you like. In Dark Mode, of course (you’re a professional, after all). Here’s a less artistic impression to give you an idea, should you have been living under a rock:

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Deliberately misfolding prions to find the golden thread.

Prion are both fascinating and terrifying. They occur naturally and have a purpose, but what that purpose is we’re still not entirely sure. Gene-knockout mice which no longer code for the prion protein do live, but they ain’t born typical.

The endogenous form of the prion protein (PrPC) can, through currently unknown mechanisms, take a different conformation, the pathogenic PrPSc. PrPSc is responsible for fatal, rapidly progressing neurodegenerative disorders which in many cases can jump species.

At OPIG, we recently discussed a remarkably rigorous series of experiments outlined in the paper “A Protein Misfolding Shaking Amplification-based method for the spontaneous generation of hundreds of bona fide prions” Whilst deliberately creating new pathogenic prions may seem and odd thing to wish to achieve, the authors aimed to determine if there was a golden thread linking “infectivity determinants, interspecies transmission barriers or the structural influence of specific amino acids”.

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