On 5th April 2024, over 60 researchers braved the train strikes and gusty weather to gather at Lady Margaret Hall in Oxford and engage in a day full of scientific talks, posters and discussions on the topic of adaptive immune receptor (AIR) analysis! The day started with a comprehensive opening keynote from Professor Charlotte Deane [University of Oxford], in which she surveyed her group’s suite of tools for AIR analysis before focusing on recent work on affinity prediction (Chinery et al. 2024), antibody language modelling (AbLang2), inverse folding (AntiFold), structural modelling (ABodyBui ..read more

As a first year PhD student, it is not an uncommon thing to be asked to write a review paper on your subject area. It is both a great way to get acquainted with your research field and to get the background portion of your thesis completed early. However, it can seem like a daunting task to go from knowing almost nothing about your research field to producing something of interest for experts who have spent years studying your subject matter. In my first year, I was exactly in this position and I found very little online to help guide this process. Thus, here is my reflective look at writing a ..read more

An excellent previous blog post from Sarah [1] describes the gender data gap and touches on the fact that women experience poorer healthcare outcomes. This arises from, amongst other things, the historical exclusion of women from clinical trials and this idea of the ‘male default’, where, for example, drug dosages and diagnostic thresholds are benchmarked against men, or even surgical instruments are designed to fit male hands [2]. I thought I would follow up on Sarah’s blog post and discuss how FemTech can help to close this gender health gap. What is FemTech? The FemTech industry has been gr ..read more

One of the nice things about OPIG, is that you can talk about something which is outside of your wheelhouse without feeling that the specialists in the group are going to eat your lunch. Last week, I gave an overview of the Hubbell group‘s Nature paper Synthetically glycosylated antigens for the antigen-specific suppression of established immune responses. I am not an immunologist by any stretch of the imagination, but sometimes you come across a piece of really interesting science and just want to say to people: Have you seen this, look at this, it’s really clever! When most people think of t ..read more

JAX Last year, I had an opportunity to delve into the world of JAX whilst working at InstaDeep. My first blopig post seems like an ideal time to share some of that knowledge. JAX is an experimental Python library created by Google’s DeepMind for applying accelerated differentiation. JAX can be used to differentiate functions written in NumPy or native Python, just-in-time compile and execute functions on GPUs and TPUs with XLA, and mini-batch repetitious functions with vectorization. Collectively, these qualities place JAX as an ideal candidate for accelerated deep learning research [1]. JAX i ..read more

Short and selfish blog here. Probably been done before, but I shall carry on regardless. I am going to review some metrics relevant to our area of Immunoinformatics. In other words, I will try dissect things such as perplexity, logits, pTM, pLDDT and the ABodyBuilder2 confidence score. These numbers can help inform us on the likelihood of predictions, and whether we should have confidence in them. Logits: A raw logit is just a number associated with an event happening. So, if we are predicting the next amino acid in a sequence, we will have a value associated with each of the 20 potential amin ..read more

Alphafold is great, however it’s not suited for large batch predictions for 2 main reasons. Firstly, there is no native functionality for predicting structures off multiple fasta sequences (although a custom batch prediction script can be written pretty easily). Secondly, the multiple sequence alignment (MSA) step is heavy and running MSAs for, say, 10,000 sequences at a tractable speed requires some serious hardware. Fortunately, an alternative to Alphafold has been released and is now widely used; Colabfold. For many, Colabfold’s primary strength is being cloud-based and that prediction requ ..read more

The power of machine learning (ML) techniques has captivated the field of small molecule drug discovery. Increasingly, researchers and organisations have employed ML to create more accurate algorithms to improve the efficiency of the discovery process. To be published, methods have to prove they have improved upon others. Often, methods are tested against the same benchmarks within a field, allowing us to track progress over time. To explore the rate of improvement, I curated the performance on three popular benchmarks. The first benchmark is CASF 2016, used to test the accuracy of methods tha ..read more

I attended RSC Fragments 2024 (Hinxton, 4–5 March 2024), a conference dedicated to fragment-based drug discovery. The various talks were really good, because they gave overviews of projects involving teams across long stretches of time. As a result there were no slides discussing wet lab protocol optimisations and not a single Western blot was seen. The focus was primarily either illustrating a discovery platform or recounting a declassified campaign. The latter were interesting, although I’d admit I wish there had been more talk of organic chemistry —there was not a single moan/gloat about a ..read more

Discovering a library that massively simplifies the exact thing you just did right after you’ve finished doing the thing you needed to do has to be one of the top 14 worst things about writing code. You might think it’s a part of the life we’ve all chosen, but it doesn’t have to be. Beyond the popular libraries you already know lies a treasure trove of under appreciated packages waiting to be wielded. Being the saint I am, I’ve scoured the depths of pypi.org to find some underrated and hopefully useful packages to make your life a little easier. 1. Arrow: Mastering the Art of Dates and Times E ..read more