Category Archives: Drug Discovery

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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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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Reflections on GRC CADD 2025: A Week of Insight, Innovation, and Baseball

Henry

Back in July, some very lucky OPIGlets ventured across the pond to discover life in Southern Maine (and Boston!). For someone visiting Boston for the first time, no trip would be complete without a Red Sox game—a thoroughly enjoyable highlight (see Figure 1). While we were there, we also went to Gordon Research Conference (GRC) on Computer Aided Drug Design (CADD).

A flock of OPIGlets taking in the Fenway Park experience at a Red Sox game.
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How reliable are affinity datasets in practice?

The Data Bottleneck in AI-Powered Drug Discovery

The pharmaceutical industry is undergoing a profound transformation, driven by the promise of Artificial Intelligence (AI) and Machine Learning (ML). These technologies offer the potential to escape the industry’s persistent challenges of high costs, protracted development timelines, and staggering failure rates. From accelerating the identification of novel biological targets to optimizing the properties of lead compounds, AI is poised to enhance the precision and efficiency of drug discovery at nearly every stage

Yet, this revolutionary potential is constrained by a fundamental dependency. The power of modern AI, particularly the deep learning (DL) models that excel at complex pattern recognition, is directly proportional to the volume, diversity, and quality of the data they are trained on. This creates a critical bottleneck: the high-quality experimental data required to train these models—specifically, the protein-ligand binding affinity values that quantify the strength of an interaction—are notoriously scarce, expensive to generate, and often of inconsistent quality or locked within proprietary databases.

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Can AI help us design better viruses?

Viruses are the most abundant biological entity on the planet. They infect virtually every kind of life form including (sort of) other viruses. Viruses are intensely efficient – some viruses contain as few as 4 genes. Their strategy is typically simple: infect a cell, use its machinery to produce more viruses, and spread to other cells.

Pathogenic human viruses are terrible, but there are many other viruses which are useful for humans. For instance, many modern vaccines use viral vectors to produce antigens of other pathogenic entities. There is also growing interest in using viruses to fight off bacterial infections.

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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 business of health: research and funding from academia to big pharma

In a world in which the probability of clinical success is just 10%-20% for new medicines, pharmaceutical multinationals increasingly turn to academia and biotech as a source of “de-risked” technology for their pipelines. This exchange of ideas, equity and capital depends on firm relationships between entities having apparently divergent interests: from not-for-profit research to international commerce.

As a former pharma contract negotiator, I spent much of my past life attempting to find common ground with university researchers and biotech leadership teams. In 2021, I had the privilege of returning to academia in the UK after a prolonged hiatus, and – more recently – of working with start-ups. In this blog, I will comment on some of the surprising trends I have observed in how pharma, biotech and academics balance the conduct of meaningful research with the requirements of their respective sectors. The views herein are entirely my own.

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