Category Archives: Cheminformatics

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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Chemical Languages in Machine Learning

For more than a century, chemists have been trying to squeeze the beautifully messy, quantum-smeared reality of molecules into tidy digital boxes, “formats” such as line notations, connection tables, coordinate files, or even the vaguely hieroglyphic Wiswesser Line Notation. These formats weren’t designed for machine learning; some weren’t even designed for computers. And yet, they’ve become the wedged into the backbones of modern drug discovery, materials design and computational chemistry.

The emergent use of large language models and natural language processing in chemistry posits the immediate question: What does it mean for a molecule to have a “language,” and how should machines speak it?

if molecules are akin to words and sentences, what alphabet and grammatical rules should they follow?

What follows is a tour through the evolving world of chemical languages, why we use them, why our old representations keep breaking our shiny new models, and what might replace them.

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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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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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Fragment-to-Lead Successes in 2023

Back in 2021, I highlighted the annual fragment-to-lead (F2L) success stories from 2019 [Blog post] [Paper]. This is one of my favourite annual publications, and I’m delighted to see that it’s still going strong. In this post, I’ll discuss the 2023 edition that was published in at the start of 2025 [Paper].

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Extracting 3D Pharmacophore Points with RDKit

Pharmacophores are simplified representations of the key interactions ligands make with proteins, such as hydrogen bonds, charge interactions, and aromatic contacts. Think of them as the essential “bumps and grooves” on a key that allow it to fit its lock (the protein). These maps can be derived from ligands or protein–ligand complexes and are powerful tools for virtual screening and generative models. Here, we’ll see how to extract 3D pharmacophore points from a ligand using RDKit.
(Code adapted from Dr. Ruben Sanchez.)

Why pharmacophore “points”?

RDKit represents each pharmacophore feature (donor, acceptor, aromatic, etc.) as a point in 3D space, located at the feature center. These points capture the essential interaction motifs of a ligand without requiring the full atomic detail.

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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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A more robust way to split data for protein-ligand tasks?

As I was recently reading through the paper on the PLINDER dataset while preparing for my next project, one of the aspects of the dataset that caught my attention was how the dataset splits were done to ensure minimal leakage for various protein-ligand tasks that PLINDER could be used for. They had task-specific splits as the notion of data leakage differed from task to task. For instance, in rigid body docking, having a similar protein in the train and test may not be considered leakage if the binding pocket location, conformation, or pocket interactions with a ligand are significantly different. On the other hand, in the case of co-folding, having similar proteins in the train and test sets would be considered data leakage, as predicted protein structures play a significant role in accuracy scoring. The effort that went into creating task-specific splits resonates strongly with OPIG’s view on ensuring minimal data leakage for validating the generalisability of protein-ligand models. However, it may become tedious to create task-specific dataset splits for every protein-ligand task when dealing with a large suite of such tasks. This had me thinking of potential avenues to streamline the dataset split process across the tasks, and one way to do this is by using protein-ligand interaction fingerprints or PLIFs.

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ChatGPT can now use RDKit!

All chemistry LLM enthusiasts were treated to a pleasant surprise on Friday when Greg Brockman tweeted that ChatGPT now has access to RDKit. I’ve spent a few hours playing with the updated models and I have summarized some of my findings in this blog.

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Geometric Deep Learning meets Forces & Equilibrium

Introduction

Graphs provide a powerful mathematical framework for modelling complex systems, from molecular structures to social networks. In many physical and geometric problems, nodes represent particles, and edges encode interactions, often acting like springs. This perspective aligns naturally with Geometric Deep Learning, where learning algorithms leverage graph structures to capture spatial and relational patterns.

Understanding energy functions and the forces derived from them is fundamental to modelling such systems. In physics and computational chemistry, harmonic potentials, which penalise deviations from equilibrium positions, are widely used to describe elastic networks, protein structures, and even diffusion processes. The Laplacian matrix plays a key role in these formulations, linking energy minimisation to force computations in a clean and computationally efficient way.

By formalising these interactions using matrix notation, we gain not only a compact representation but also a foundation for more advanced techniques such as Langevin dynamics, normal mode analysis, and graph-based neural networks for physical simulations.

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