I recently had a problem where I wanted to provide an interactive visualization of multiple different protein–ligand complexes, requiring minimal setup by the user, allowing them to zoom in and out and change the visualization style, without just providing multiple PDB files or a PyMOL session.
Continue readingCategory Archives: Python
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).
Fine-tune generated molecular poses with a force field
Some molecular pose generation methods benefit from an energy relaxation post-processing step.
Here is a quick way to do this using OpenMM via a short script I prepared:
Continue readingOrganise Your ML Projects With Hydra
One of the most annoying parts of ML research is keeping track of all the various different experiments you’re running – quickly changing and keeping track of changes to your model, data or hyper-parameters can turn into an organisational nightmare. I’m normally a fan of avoiding too many different libraries/frameworks as they often break down if you to do anything even a little bit custom and days are often wasted trying to adapt yourself to a new framework or adapt the framework to you. However, my last codebase ended up straying pretty far into the chaotic side of things so I thought it might be worth trying something else out for my next project. In my quest to instil a bit more order, I’ve started using Hydra, which strikes a nice balance between giving you more structure to organise a project, while not rigidly insisting on it, and I’d highly recommend checking it out yourself.
Continue readingThe War of the Roses: Tea Edition
Picture the following: the year is 1923, and it’s a sunny afternoon at a posh garden party in Cambridge. Among the polite chatter, one Muriel Bristol—a psychologist studying the mechanisms by which algae acquire nutrients—mentions she has a preference for tea poured over milk, as opposed to milk poured over tea. In a classic example of women not being able to express even the most insignificant preference without an opinionated man telling them they’re wrong, Ronald A. Fisher, a local statistician (later turned eugenicist who dismissed the notion of smoking cigarettes being dangerous as ‘propaganda’, mind you) decides to put her claim to the test with an experiment. Bristol is given eight cups of tea and asked to classify them as milk first or tea first. Luckily, she correctly identifies all eight of them, and gets to happily continue about her life (presumably until the next time she dares mention a similarly outrageous and consequential opinion like a preferred toothpaste brand or a favourite method for filing papers). Fisher, on the other hand, is incentivized to develop Fisher’s exact test, a statistical significance test used in the analysis of contingency tables.
Continue readingPyrosetta for RFdiffusion
I will not lie: I often struggle to find a snippet of code that did something in PyRosetta or I spend hours facing a problem caused by something not working as I expect it to. I recently did a tricky project involving RFdiffusion and I kept slipping on the PyRosetta side. So to make future me, others, and ChatGTP5 happy, here are some common operations to make working with PyRosetta for RFdiffusion easier.
Continue readingExploring multilingual programming
Python is a prominent language in the ML and scientific computing space, and for good reason. Python is easy-to-learn and readable, and it offers a vast selection of libraries such as NumPy for numerical computation, Pandas for data manipulation, SciPy for scientific computing, TensorFlow, and PyTorch for deep learning, along with RDKit and Open Babel for cheminformatics. It is understandably an appealing choice for developers and researchers alike. However, a closer look at many common Python libraries reveals their foundations in C++.
Revisiting C++ Advantages
Many of Python libraries including TensorFlow, PyTorch, and RDKit are all heavily-reliant on C++. C++ allows developers to manage memory and CPU resources more effectively than Python, making it a good choice when handling large volumes of data at a fast pace. A previous post on this blog discusses C++’s speed, its utility in GPU programming through CUDA, and the complexities of managing its libraries. Despite the steeper learning curve and verbosity compared to Python, the performance benefits of C++ are undeniable, especially in contexts where execution speed and memory management are critical.
Rust: A New Contender for High-Performance Computing
Continue readingQuickly (and lazily) scale your data processing in Python
Do you use pandas for your data processing/wrangling? If you do, and your code involves any data-heavy steps such as data generation, exploding operations, featurization, etc, then it can quickly become inconvenient to test your code.
- Inconvenient compute times (>tens of minutes). Perhaps fine for a one-off, but over repeated test iterations your efficiency and focus will take a hit.
- Inconvenient memory usage. Perhaps your dataset is too large for memory, or loads in but then causes an OOM error during a mid-operation memory spike.
Mapping derivative compounds to parent hits
Whereas it is easy to say in a paper “Given the HT-Sequential-ITC results, 42 led to 113, a substituted decahydro-2,6-methanocyclopropa[f]indene”, it is frequently rather trickier algorithmically figure out which atoms map to which. In Fragmenstein, for the placement route, for example, a lot goes on behind the scenes, yet for some cases human provided mapping may be required. Here I discuss how to get the mapping from Fragmenstein and what goes on behind the scenes.
Continue readingUsing JAX and Haiku to build a Graph Neural Network
JAX
Last year, I had an opportunity to delve into the world of JAX whilst working at InstaDeep. My first blopig post seems like an ideal time to share some of that knowledge. JAX is an experimental Python library created by Google’s DeepMind for applying accelerated differentiation. JAX can be used to differentiate functions written in NumPy or native Python, just-in-time compile and execute functions on GPUs and TPUs with XLA, and mini-batch repetitious functions with vectorization. Collectively, these qualities place JAX as an ideal candidate for accelerated deep learning research [1].
JAX is inspired by the NumPy API, making usage very familiar for any Python user who has already worked with NumPy [2]. However, unlike NumPy, JAX arrays are immutable; once they are assigned in memory they cannot be changed. As such, JAX includes specific syntax for index manipulation. In the code below, we create a JAX array and change the 
