Category Archives: Python

Code that I am grateful for

To address some of the karmic imbalance created by computational scientists complaining about other people’s code, I am listing here some (not all) of other people’s code that I love.

IgBLAST

IgBLAST is a sequence alignment tool for immunoglobulin sequences implemented in the NCBI C++ toolkit – it applies the classic BLAST algorithm to searching immunoglobulin germline gene databases. It always impresses me how quickly it works. The paper is here, and the authors are Jian Ye, Ning Ma, Thomas L. Madden and James M. Ostell.

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C++ python bindings in 5 minutes

You don’t even need to use CMake!

Most of the time, we can use libraries like numpy (which is largely written in C) to speed up our calculations, which works when we are dealing with matrices or vectors – but sometimes loops are unavoidable. In those instances, it would be nice if we could use a compiled language such as C++ to remove the bottleneck.

This can be achieved extremely easily using pybind11, which enables us to export C++ functions and classes as importable python objects. We can do all of this very easily, without using CMake, using pybind11’s Pybind11Extension class, along with a modified setup.py. Pybind11 can be compiled from source or installed using:

pip install pybind11
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Better understanding of correlation

Although correlation is often used as the linear relationship between two sets of points, I will in the following text use it more broadly to mean any relationship between two sets of points.

You have tasked yourself with finding the correlation between the different features in your dataset. Your purpose could be to remove highly correlated features or just improve your understanding of your data. Nonetheless, calculating and using the Pearson Correlation Coefficient (PCC) or the Spearman’s rank Correlation Coefficient (SCC) to get an overview of the correlations might be the first thing that comes to your mind.

Unfortunately, both of these are limited to linear (PCC) or monotonic (SCC) relationships. In datasets with many and complex features, many of them will be highly correlated, just not linearly (or monotonic). Instead these correlations can be non-linear which, as seen in the third row in the below figure, does not get detected with PCC.

Figure: PCC of different sets of x and y points. https://en.wikipedia.org/wiki/Correlation_and_dependence
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ORDER!: Returning bond order information to your docked poses

John Bercow Order Remix - YouTube

Common docking software, such as AutoDock Vina or AutoDock 4, require the ligand and receptor files to be converted into the PDBQT format. Once a correct pose has been identified, the pose will be produced also as a .pdbqt file.

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Plotly for interactive 3D plotting

An recently wrote a post on how to use the seaborn library. I really like seaborn and use it a lot for 2D plots. However, recently I have been dealing with 3D data and have found plotly to be best. When used in a jupyter notebook, it allows you to easily generate 3D interactive plots. This is extremely useful to visualize structural data.

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Calculating symmeterised small molecule RMSDs using graph automorphisms in python with GEMMI and NetworkX

When a ring flips, how do we calculate RMSD?

This surprisingly simple question leads to a very interesting problem! If we take a benzene molecule, say, and rotate it 180 degrees, then we have the exact same molecule, but if we have a data structure in which our atoms are labelled, and we apply the same transformation to the atomic positions, the numbering does not reflect that symmetry. If we were then naively to calculate the RMSD it would be huge, despite the fact that the molecule is, chemically speaking, identical.

How can we make our RMSD calculations reflect these symmetries?

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Tracking machine learning projects with Weights & Biases

Optimising machine learning models requires extensive comparison of architectures and hyperparameter combinations. There are many frameworks that make logging and visualising performance metrics across model runs easier. I recently started using Weights & Biases. In the following, I give a brief overview over some basic code snippets for your machine learning python code to get started with this tool.

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