Tag Archives: Python

GitHub actions can be useful

GitHub actions is a (relatively) novel GitHub feature that allows you to run code on GitHub when a predefined event is triggered. The most widespread use case for GitHub actions is for Continuous Integration, because it allows you to automatically test your code on any machine immediately after each push. For a great tutorial on how to use it for this see here.

But you can do so much more with them!! Basically you can set up any workflow to run after any event. An event is basically when a specific activity on GitHub happens, while a workflow is basically the script you want to run after the event has happened. For a full list of the events you can use see here. Workflow scripts are written in a .yml file and should be saved within the .github/worflows directory within your repository. I am incapable of writing a better tutorial for these than what is already on their documentation, but I will show a copy of a workflow script I recently put together and walk you through it.

In one of my previous blog posts I wrote about how to upload your code to PyPI. Hopefully I convinced you that this is quite easy, but it does require a few steps that you may not want to be doing every time you come up with a new feature (find a bug) and have to re-upload it. Luckily, you don’t have to!! Just stick the code into a GitHub actions workflow so it will automatically re-upload it for you. Here is the script I use for this:

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Post-processing for molecular docking: Assigning the correct bond order using RDKit.

AutoDock4 and AutoDock Vina are the most commonly used open-source software for protein-ligand docking. However, they both rely on a derivative of the “PDB” (Protein Data Base) file format: the “PDBQT” file (Protein Data Bank, Partial Charge (Q), & Atom Type (T)). In addition to the information contained in normal PDB files, PDBQT files have an additional column that lists the partial charge (Q) and the assigned AutoDock atom type (T) for each atom in the molecule. AutoDock atom types offer a more granular differentiation between atoms such as listing aliphatic carbons and aromatic carbons as separate AutoDock atom types.

The biggest drawback about the PDBQT format is that it does not encode for the bond order in molecules explicitly. Instead, the bond order is inferred based on the atom type, distance and angle to nearby atoms in the molecule. For normal sp3 carbons and molecules with mostly single bonds this system works fine, however, for more complex structures containing for example aromatic rings, conjugated systems and hypervalent atoms such as sulphur, the bond order is often not displayed correctly. This leads to issues downstream in the screening pipeline when molecules suddenly change their bond order or have to be discarded after docking because of impossible bond orders.

The solution to this problem is included in RDKit: The AssignBondOrdersFromTemplate function. All you need to do is load the original molecule used for docking as a template molecule and the docked pose PDBQT file into RDKIT as a PDB, without the bond order information. Then assign the original bond order from your template molecule. The following code snippet covers the necessary functions and should help you build a more accurate and reproducible protein-ligand docking pipeline:

#import RDKit AllChem
from rdkit import Chem
from rdkit.Chem import AllChem


#load original molecule from smiles
SMILES_STRING = "CCCCCCCCC" #the smiles string of your ligand
template = Chem.MolFromSmiles(SMILES_STRING)

#load the docked pose as a PDB file
loc_of_docked_pose = "docked_pose_mol.pdb" #file location of the docked pose converted to PDB file
docked_pose = AllChem.MolFromPDBFile(loc_of_docked_pose)

#Assign the bond order to force correct valence
newMol = AllChem.AssignBondOrdersFromTemplate(template, docked_pose)

#Add Hydrogens if desired. "addCoords = True" makes sure the hydrogens are added in 3D. This does not take pH/pKa into account. 
newMol_H = Chem.AddHs(newMol, addCoords=True)

#save your new correct molecule as a sdf file that encodes for bond orders correctly
output_loc = "docked_pose_assigned_bond_order.sdf" #output file name
Chem.MolToMolFile(newMol_H, output_loc)

How to turn a SMILES string into a molecular graph for Pytorch Geometric

Despite some of their technical issues, graph neural networks (GNNs) are quickly being adopted as one of the state-of-the-art methods for molecular property prediction. The differentiable extraction of molecular features from low-level molecular graphs has become a viable (although not always superior) alternative to classical molecular representation techniques such as Morgan fingerprints and molecular descriptor vectors.

But molecular data usually comes in the sequential form of labeled SMILES strings. It is not obvious for beginners how to optimally transform a SMILES string into a structured molecular graph object that can be used as an input for a GNN. In this post, we show how to convert a SMILES string into a molecular graph object which can subsequently be used for graph-based machine learning. We do so within the framework of Pytorch Geometric which currently is one of the best and most commonly used Python-based GNN-libraries.

We divide our task into three high-level steps:

  1. We define a function that maps an RDKit atom object to a suitable atom feature vector.
  2. We define a function that maps an RDKit bond object to a suitable bond feature vector.
  3. We define a function that takes as its input a list of SMILES strings and associated labels and then uses the functions from 1.) and 2.) to create a list of labeled Pytorch Geometric graph objects as its output.
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List comprehension: an elegant Python feature inspired by mathematical set theory

Even though I have now deeply entered into the fascinating world of statistical machine learning and computational chemistry, my original background is very much in pure mathematics. Having spent some of my intellectually formative years in this highly purified and abstract universe, I still love to think in terms of sets, ordered tuples and well-defined functions whenever I have the luxury of being able to do so. This might be why list comprehension is one of my favourite features in Python.

List comprehension allows you to efficiently map a function over a list using elegant notation inspired by mathematical set theory. Let us first consider a (mathematical) set

A := \{1, 3, 7 \}.

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Making your python tool as easy to install as possible

Have you ever tried to use someone else’s code and spent a whole day trying to install it? Have you ever decided not to use a tool because installing it was a massive pain? Both of those have happened to me and, to be honest, it is a massive shame. The authors may spend large amounts of time developing these tools and in the end, no one uses them because they can’t get them to work. So I have decided to try and make all code I develop as easy and painless as possible to install and use.

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Out-of-distribution generalisation and scaffold splitting in molecular property prediction

The ability to successfully apply previously acquired knowledge to novel and unfamiliar situations is one of the main hallmarks of successful learning and general intelligence. This capability to effectively generalise is amongst the most desirable properties a prediction model (or a mind, for that matter) can have.

In supervised machine learning, the standard way to evaluate the generalisation power of a prediction model for a given task is to randomly split the whole available data set X into two sets – a training set X_{\text{train}} and a test set X_{\text{test}}. The model is then subsequently trained on the examples in the training set X_{\text{train}} and afterwards its prediction abilities are measured on the untouched examples in the test set X_{\text{test}} via a suitable performance metric.

Since in this scenario the model has never seen any of the examples in X_{\text{test}} during training, its performance on X_{\text{test}} must be indicative of its performance on novel data X_{\text{new}} which it will encounter in the future. Right?

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Hosting multiple Flask apps using Apache/mod_wsgi

A common way of deploying a Flask web application in a production environment is to use an Apache server with the mod_wsgi module, which allows Apache to host any application that supports Python’s Web Server Gateway Interface (WSGI), making it quick and easy to get an application up and running. In this post, we’ll go through configuring your Apache server to host multiple Python apps in a stable manner, including how to run apps in daemon mode and avoiding hanging processes due to Python C extensions not working well with Python sub-interpreters (I’m looking at you, numpy).

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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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GEMMI: A Python Cookbook

General MacroMocelecular I/O, or GEMMI, is a C++ 11 header only library for low level crystalographic .

Because its header only it is certainly the easiest to access and use low level crystalographic C++ library, however GEMMI comes with python binding via Pybind11, making it arguably the easiest low level crystalographic library to access and use in python as well!

What follows is a cookbook of useful Python code that uses GEMMI to accomplish macromolecular crystalographic tasks.

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