Interactive code editors have been around for a while now, and tools like GitHub Copilot have woven their way into most development pipelines, and for good reason. They’re easy to use, exceptionally helpful (at certain tasks), and have undeniably made life as a developer smoother. Recently, I decided to switch away from relying on GitHub Copilot in favour of a local model for a few key reasons. While I don’t use it all the time, it has proven to be a useful option in many situations. In this blog post, I’ll go over why I made the switch, how I set it up, and share a bit about my experience so far.
Continue readingMonthly Archives: February 2025
Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data
I’m delighted to report our collaboration (Ísak Valsson, Matthew Warren, Aniket Magarkar, Phil Biggin, & Charlotte Deane), on “Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data”, has been published in Nature’s Communications Chemistry (https://doi.org/10.1038/s42004-025-01428-y).
During his MSc dissertation project in the Department of Statistics, University of Oxford, OPIG member Ísak Valsson developed an attention-based GNN to predict protein-ligand binding affinity called “AEV-PLIG”. It featurizes a ligand’s atoms using Atomic Environment Vectors to describe the Protein-Ligand Interactions found in a 3D protein-ligand complex. AEV-PLIG is free and open source (BSD 3-Clause), available from GitHub at https://github.com/oxpig/AEV-PLIG, and forked at https://github.com/bigginlab/AEV-PLIG.
Estimating the Generalisability of Machine Learning Models in Drug Discovery
Machine learning (ML) has significantly advanced key computational tasks in drug discovery, including virtual screening, binding affinity prediction, protein-ligand structure prediction (co-folding), and docking. However, the extent to which these models generalise beyond their training data is often overestimated due to shortcomings in benchmarking datasets. Existing benchmarks frequently fail to account for similarities between the training and test sets, leading to inflated performance estimates. This issue is particularly pronounced in tasks where models tend to memorise training examples rather than learning generalisable biophysical principles. The figure below demonstrates two examples of model performance decreasing with increased dissimilarity between training and test data, for co-folding (left) and binding affinity prediction (right).
LLM Coding Tools – An Overview
We’ve come a long way since GitHub Copilot first showed us what AI-assisted coding could look like. These days, there’s a whole ecosystem of LLM coding tools out there, each with their own strengths and approaches. In this blog, I’ll give you a quick overview to help you figure out which one might work best for your workflow.