Category Archives: News

Pitfalls of AI-Generated Reviews: Case Study of a Frontiers in Microbiology Review on Anti-Influenza A bnAbs

In the last five or so years, large language models (LLMs) have transformed from a novel regurgitator of haphazardly stitched together sentences to an almost ‘human’ personality standing by our side as we tackle life. Whilst the perceived humanity of these models is the topic for perhaps a future blogpost, it is almost undeniable to understate the impact of LLMs in our daily lives. Do you need someone to proofread your essay you’ve spent hours drafting? GPT (or one of its many counterparts) has you covered. Need help drafting an email from scratch? No problem. Want to write and/or heavily edit an entire academic article which would typically require days, if not weeks, of research? Surely just needs a push of a button… right?

Despite tremendous advances in LLMs, key issues mean they are not yet a fully dependable addition to our writing endeavours. They are known to fail when asked to generate new content with only a basic prompt. Some of these failures have made headlines 1. Some of the scariest instances are those of hallucinated information 2–4 . This refers to the phenomenon where AI tools generate convincing information which is factually inaccurate or simply fabricated 2 . In Belgium, the Ghent university rector came under fire for citing quotes, supposedly from influential thinkers, which were later found to be AI-hallucinations 1.
Whilst there are numerous examples of the poorly cited and often AI-hallucinated papers falling through the cracks of the peer-review process, today we focus on a Frontiers in Microbiology review titled ‘Broadly neutralizing monoclonal antibodies against influenza A viruses: current insights and future directions’ 5. This paper attempts to provide an overview of the current landscape of monoclonal antibodies (mAbs) which are being developed to confer protection against influenza A, highlighting ‘technological advances, clinical performance, and scalability’. This paper contains many of the hallmarks of text that has been created or edited with generative AI, despite the generative AI statement stating ‘The author(s) declared that Generative AI was not used in the creation of this manuscript.’

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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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GPT-5 achieves state-of-the-art chemical intelligence

I have run ChemIQ (our chemical reasoning benchmark) on GPT-5. The model achieves state-of-the-art performance with substantial improvements in the ability to interpret SMILES strings. Read my analysis and initial findings below. Scroll to the end for some cool demos.

Figure 1: Success rates for each model on the ChemIQ reasoning benchmark. Horizontal brackets between adjacent bars indicate the result of a two-tailed McNemar’s test comparing paired outcomes for the same questions. Significance levels are shown as: n.s. (not significant, p ≥ 0.05), * (p < 0.05), ** (p < 0.01), and *** (p < 0.001).

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Open Source Pharma: From Idealism to Pragmatic Solutions

In an industry dominated by patents, proprietary data, and the race to get a first-in-class drug, the concept of open source drug development once seemed like an impossible dream. Yet as traditional pharma continues to leave many global health needs unaddressed—particularly for diseases affecting low and middle income countries1,2—the open source model has evolved from idealistic theory to pragmatic reality. In this post, I’ll lead us through how open source drug development has overcome key obstacles of funding and intellectual property (IP) management to deliver real-world solutions.

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The “AI-ntibody” Competition: benchmarking in silico antibody screening/design

We recently contributed to a communication in Nature Biotechnology detailing an upcoming competition coordinated by Specifica to evaluate the relative performance of in vitro display and in silico methods at identifying target-specific antibody binders and performing downstream antibody candidate optimisation.

Following in the footsteps of tournaments such as the Critical Assessment of Structure Prediction (CASP), which have led to substantial breakthroughs in computational methods for biomolecular structure prediction, the AI-ntibody initiative seeks to establish a periodic benchmarking exercise for in silico antibody discovery/design methods. It should help to identify the most significant breakthroughs in the space and orient future methods’ development.

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Environmentally sustainable computing 

Did you know that it is approximated that you, a scientist, have a carbon footprint which is between 2 and 12 times higher than the set carbon budget per person to keep global warming below 1.5 °C [1]? 

Background

Global temperatures are rising. This has direct effects on the planet and contributes to increasing humanitarian emergencies. These include more frequent and intense heatwaves, wildfires, and floods [2]. The impact of climate change is already severe, with around 20 million internal displaced persons in 2023 alone due to those disasters [3]. 

Global warming and climate change are caused by the emissions of carbon dioxide and methane, known as carbon emissions. There are different ways in which you could minimise your carbon footprint. For example, I try to reduce the energy usage in the house, try eating mainly plant-based, and travel by train instead of by plane to family and for holidays and conferences. However, up until organising a Green Lecture with the Department of Statistics Green Team I never thought of my computational PhD as a major contributor to my carbon footprint. That doesn’t mean the work I, and all other scientists, do is not important and necessary. But the lecture on principles for environmentally sustainable research given by Loic Lannelongue made me aware of carbon costs of computing, which I would like to share with you. 

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Conference Summary: MGMS Adaptive Immune Receptors Meeting 2024

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!

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An Open-Source CUDA for AMD GPUs – ZLUDA

Lots of work has been put into making AMD designed GPUs to work nicely with GPU accelerated frameworks like PyTorch. Despite this, getting performant code on non-NVIDIA graphics cards can be challenging for both users and developers. Even in the case where the developer has appropriately optimised for each platform there are often gaps in performance where, at the driver-level, instructions to the GPU may not be optimised fully. This is because software developed using CUDA can benefit from optimisations like operation-fusing without having to specify in many cases.

This may not be much of a concern for most researchers as we simply use what is available to us. Most of the time this is usually NVIDIA GPUs and there is hardly a choice to it. NVIDIA is aware of this and prices their products accordingly. Part of the problem is that system designers just dont have an incentive to build AMD platfroms other than for highly specialised machines.

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What can you do with the OPIG Immunoinformatics Suite? v3.0

OPIG’s growing immunoinformatics team continues to develop and openly distribute a wide variety of databases and software packages for antibody/nanobody/T-cell receptor analysis. Below is a summary of all the latest updates (follows on from v1.0 and v2.0).

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