Category Archives: Python

NVIDIA Reimagines CUDA for Python Developers

According to GitHub’s Open Source Survey, Python has officially become the world’s most popular programming language in 2024 – ultimately surpassing JavaScript. Due to its exceptional popularity, NVIDIA announced Python support for its CUDA toolkit at last year’s GTC conference, marking a major leap in the accessibility of GPU computing. With the latest update (https://nvidia.github.io/cuda-python/latest/) and for the first time, developers can write Python code that runs directly on NVIDIA GPUs without the need for intermediate C or C++ code.

Historically tied to C and C++, CUDA has found its way into Python code through third-party wrappers and libraries. Now, the arrival of native support means a smoother, more intuitive experience.

This paradigm shift opens the door for millions of Python programmers – including our scientific community – to build powerful AI and scientific tools without having to switch languages or learn legacy syntax.

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GUI Science

There comes a point in every software-inclined lab based grad student’s life, where they think: now is the time to write a GUI for my software, to make it fast, easy to use, generalised so that others will use it too, the new paradigm for how to do research, etc. etc.

Of course such delusions of grandeur are rarely indulged, but when executed they certainly can produce useful outputs, as a well designed (or even, designed) GUI can improve an experimentalist’s life profoundly by simplifying, automating and standardising data acquisition, and by reducing the time to see results, allowing for shorter iteration cycles (this is known in engineering as “Design, Build, Test, Learn” cycle, in software it’s called “Coding”).

Having written a few GUIs in my time, I thought it might be helpful to share some experience I have, though it is by no means broad.

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Combining Multiple Comparisons Similarity plots for statistical tests

Following on from my previous blopig post, Garrett gave the very helpful suggestion of combining Multiple Comparisons Similarity (MCSim) plots to reduce information redundancy. For example, this an MCSim plot from my previous blog post:

This plot shows effect sizes from a statistical test (specifically Tukey HSD) between mean absolute error (MAE) scores for different molecular featurization methods on a benchmark dataset. Red shows that the method on the y-axis has a greater average MAE score than the method on the x-axis; blue shows the inverse. There is redundancy in this plot, as the same information is displayed in both the upper and lower triangles. Instead, we could plot both the effect size and the p-values from a test on the same MCSim.

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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.

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Molecule Networks: data visualization using PyVis

Over the past few years I have explored different data visualization strategies with the goal of rapidly communicating information to medicinal chemists. I have recently fallen in love with “molecule networks” as an intuitive and interactive data visualization strategy. This blog gives a brief tutorial on how to start generating your own molecule networks.

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Making pretty, interactive graphs the simple way – Use Plotly.

Using an ESP8266 and some DS18B20 one-wire temperature sensors, I have been automatically recording temperature data from various parts of my pond, to see how it fluctuated with air temperature, depth and filter configuration.

Despite the help I was receiving from the feline fish monitor, I was getting a bit irked at the quality of the graphs I was getting using matplotlib.

Matplotlib has been around since 2003, more than 20 years now. It’s arguably the defacto method of producing graphs in python and it’s not going away. However, it’s also a pain to use and by default produces some quite ugly plots unless you put in the mileage. In fact, when attempting to quickly explore data, Michael L. Waskom’s frustrations with matplotlib were directly related to the production of the seaborn library. “By producing complete graphics from a single function call with minimal
arguments, seaborn facilitates rapid prototyping and exploratory data analysis.”

Seaborn makes use of matplotlib and integrates tightly with pandas provide a neat wrapper for matplotlib functions, allowing you to avoid a lot of the data herding needed to view a graph.

You may think “OK, so seaborn finally tames matplotlib, why should I use anything else?” In short, interactivity. Seaborn and Matplotlib may produce graphs, but a graph alone doesn’t really let you explore the data. If you look at a graph you’re limited to the scale the author thought made sense, you can’t zoom in or out and if one line is behind another, you’re kind of stuck.

Where plotly really shines is with just two lines you can generate your figure and then either save it as the image below, or as an interactive HTML graph such as this.

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Visualising and validating differences between machine learning models on small benchmark datasets

Introduction
Author

Sam Money-Kyrle

Introduction

An epidemic is sweeping through cheminformatics (and machine learning) research: ugly results tables. These tables are typically bloated with metrics (such as regression and classification metrics next to each other), vastly differing tasks, erratic bold text, and many models. As a consequence, results become difficult to analyse and interpret. Additionally, it is rare to see convincing evidence, such as statistical tests, for whether one model is ‘better’ than another (something Pat Walters has previously discussed). Tables are a practical way to present results and are appropriate in many cases; however, this practicality should not come at the cost of clarity.

The terror of ugly tables extends to benchmark leaderboards, such as Therapeutic Data Commons (TDC). These leaderboard tables do not show:

  1. whether differences in metrics between methods are statistically significant,
  2. whether methods use ensembles or single models,
  3. whether methods use classical (such as Morgan fingerprints) or learned (such as Graph Neural Networks) representations,
  4. whether methods are pre-trained or not,
  5. whether pre-trained models are supervised, self-supervised, or both,
  6. the data and tasks that pre-trained models are pre-trained on.

This lack of context makes meaningful comparisons between approaches challenging, obscuring whether performance discrepancies are due to variance, ensembling, overfitting, exposure to more data, or novelties in model architecture and molecular featurisation. Confirming the statistical significance of performance differences (under consistent experimental conditions!) is crucial in constructing a more lucid picture of machine learning in drug discovery. Using figures to share results in a clear, non-tabular format would also help.

Statistical validation is particularly relevant in domains with small datasets, such as drug discovery, as the small number of test samples leads to high variance in performance between different splits. Recent work by Ash et al. (2024) sought to alleviate the lack of statistical validation in cheminformatics by sharing a helpful set of guidelines for researchers. Here, we explore implementing some of the methods they suggest (plus some others) in Python.

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Controlling PyMol from afar

Do you keep downloading .pdb and .sdf files and loading them into PyMol repeatedly?

If yes, then PyMol remote might be just for you. With PyMol remote, you can control a PyMol session running on your laptop from any other machine. For example, from a Jupyter Notebook running on your HPC cluster.

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Building CLI Applications with Typer

Remember the last time you had to build a command-line tool? If you’re like me, you probably started with argparse or click, wrote boilerplate code, and still ended up with something that felt clunky. That’s where typer comes in – it’s a game-changer that lets you build CLI apps with minimal code. Although there are several other options, typer stands out because it leverages Python’s type hints to do the heavy lifting. No more manual argument parsing! The following snippet shows how to use typer in its simplest form:

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EnlighterJS 3 Syntax Highlighter
import typer
app = typer.Typer()
@app.command()
def hello(name: str):
typer.echo(f"Hello {name}!")
if __name__ == "__main__":
app()
import typer app = typer.Typer() @app.command() def hello(name: str): typer.echo(f"Hello {name}!") if __name__ == "__main__": app()
import typer

app = typer.Typer()

@app.command()
def hello(name: str):
    typer.echo(f"Hello {name}!")

if __name__ == "__main__":
    app()

And you will be able to execute it with just:

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$ python hello.py Pedro
Hello Pedro!
$ python hello.py Pedro Hello Pedro!
$ python hello.py Pedro
Hello Pedro!

In this simple example, we were only defining positional arguments, but having optional arguments is as easy as setting default values in the function signature.

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Testing python (or any!) command line applications

Through our work in OPIG, many of our projects come in the form of code bases written in Python. These can be many different things like databases, machine learning models, and other software tools. Often, the user interface for these tools is developed as both a web app and a command line application. Here, I will discuss one of my favourite tools for testing command-line applications: prysk!

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