Category Archives: Data Science

Diagnostics on the Cutting Edge, Software in the Stone Age: A Microbiology Story

The need to treat and control infectious diseases has challenged humanity for millennia, driving a series of remarkable advancements in diagnostic tools and techniques. One of the earliest known legal texts, the Code of Hammurabi, references the visual and tactile diagnosis of leprosy. For centuries, the distinct smell of infected wounds was used to identify gangrene, and in Ancient Greece and Rome, the balance of the four humors (blood, phlegm, black bile, and yellow bile) was a central theory in diagnosing infections.

The invention of the compound microscope in 1590 by Hans and Zacharias Janssen, and its refinements by Robert Hooke and Antonie van Leeuwenhoek, marked a turning point as it enabled the direct observation of microorganisms, thereby linking diseases to their microbial origins. Louis Pasteur’s introduction of liquid media aided Joseph Lister in identifying microbes as the source of surgical infections, whilst Robert Koch’s experiments with Bacillus anthracis firmly established the connection between specific microbes and diseases.

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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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Cross referencing across LaTeX documents in one project

A common scenario we come across is that we have a main manuscript document and a supplementary information document, each of which have their own sections, tables and figures. The question then becomes – how do we effectively cross-reference between the documents without having to tediously count all the numbers ourselves every time we make a change and recompile the documents?

The answer: cross referencing!

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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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Making your code pip installable

aka when to use a CutomBuildCommand or a CustomInstallCommand when building python packages with setup.py

Bioinformatics software is complicated, and often a little bit messy. Recently I found myself wading through a python package building quagmire and thought I could share something I learnt about when to use a custom build command and when to use a custom install command. I have also provided some information about how to copy executables to your package installation bin. **ChatGPT wrote the initial skeleton draft of this post, and I have corrected and edited.

Next time you need to create a pip installable package yourself, hopefully this can save you some time!

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ggPlotting tips with OPIG data

Ever wondered whether opiglets keep their ketchup in the fridge or cupboard? Perhaps you’ve wanted to know how to create nice figure to display lots of information simulataniously. Publication quality figures are easy in R with the ggplot package. We may also learn some good visualisation.

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Incorporating conformer ensembles for better molecular representation learning

Conformer ensemble of tryptophan from Seibert et. al.

The spatial or 3D structure of a molecule is particularly relevant to modeling its activity in QSAR. The 3D structural information affects molecular properties and chemical reactivities and thus it is important to incorporate them in deep learning models built for molecules. A key aspect of the spatial structure of molecules is the flexible distribution of their constituent atoms known as conformation. Given the temperature of a molecular system, the probability of each of its possible conformation is defined by its formation energy and this follows a Boltzmann distribution [McQuarrie and Simon, 1997]. The Boltzmann distribution tells us the probability of a certain confirmation given its potential energy. The different conformations of a molecule could result in different properties and activity. Therefore, it is imperative to consider multiple conformers in molecular deep learning to ensure that the notion of conformational flexibility is embedded in the model developed. The model should also be able to capture the Boltzmann distribution of the potential energy related to the conformers.

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The Tale of the Undead Logger

A picture of a scary-looking zombie in a lumberjack outfit holding an axe, in the middle of a forest at night, staring menacingly at the viewer.
Fear the Undead Logger all ye who enter here.
For he may strike, and drain the life out nodes that you hold dear.
Among the smouldering embers of jobs you thought long dead,
he lingers on, to terrorise, and cause you frightful dread.
But hark ye all my tale to save you from much pain,
and fight ye not anew the battles I have fought in vain.

Or simply…

… Tips and Tricks to Use When wandb Logger Just. Won’t. DIE.

The Weights and Biases Logger (illustrated above by DALL-E; admittedly with some artistic license) hardly requires introduction. It’s something of an industry standard at this point, well-regarded for the extensive (and extensible) functionality of its interactive dashboard; for advanced features like checkpointing model weights in the cloud and automating hyperparameter sweeps; and for integrating painlessly with frameworks like PyTorch and PyTorch Lightning. It simplifies your life as an ML researcher enormously by making it easy to track and compare experiments, monitor system resource usage, all while giving you very fun interactive graphs to play with.
Plot arbitrary quantities you may be logging against each other, interactively, on the fly, however you like. In Dark Mode, of course (you’re a professional, after all). Here’s a less artistic impression to give you an idea, should you have been living under a rock:

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Comparing pose and affinity prediction methods for follow-up designs from fragments

In any task in the realm of virtual screening, there need to be many filters applied to a dataset of ligands to downselect the ‘best’ ones on a number of parameters to produce a manageable size. One popular filter is if a compound has a physical pose and good affinity as predicted by tools such as docking or energy minimisation. In my pipeline for downselecting elaborations of compounds proposed as fragment follow-ups, I calculate the pose and ΔΔG by energy minimizing the ligand with atom restraints to matching atoms in the fragment inspiration. I either use RDKit using its MMFF94 forcefield or PyRosetta using its ref2015 scorefunction, all made possible by the lovely tool Fragmenstein.

With RDKit as the minimizer the protein neighborhood around the ligand is fixed and placements take on average 21s whereas with PyRosetta placements, they take on average 238s (and I can run placements in parallel luckily). I would ideally like to use RDKit as the placement method since it is so fast and I would like to perform 500K within a few days but, I wanted to confirm that RDKit is ‘good enough’ compared to the slightly more rigorous tool PyRosetta (it allows residues to relax and samples more conformations with the longer runtime I think).

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