Category Archives: Code

Retrieving AlphaFold models from AlphaFoldDB

There are now nearly a million AlphaFold [1] protein structure predictions openly available via AlphaFoldDB [2]. This represents a huge set of new data that can be used for the development of new methods. The options for downloading structures are either in bulk (sorted by genome), or individually from the webpage for a prediction.

If you want just a few hundred or a few thousand specific structures, across different genomes, neither of these options are particularly practical. For example, if you have several thousand experimental structures for which you have their PDB [3] code, and you want to obtain the equivalent AlphaFold predictions, there is another way!

If we take the example of the PDB’s current molecule of the month, pyruvate kinase (PDB code 4FXF), this is how you can go about downloading the equivalent AlphaFold prediction programmatically.

  1. Query UniProt [4] for the corresponding accession number – an example python script is shown below:
Continue reading →

Filtering molecules with long linkers

Recently I was tasked with filtering out ‘stringy’ molecules that were being produced with the fragment merging method I’m working on (that is, molecules with lots of consecutive non-ring bonds that weren’t necessarily caught with my rotatable bond filter). While this is quite a niche/specific task, through this I discovered a couple of RDKit functions that I wasn’t previously aware of but might be helpful for other people regularly looking at small molecules. The demo adapts code from this helpful blogpost on cutting a molecule into rings and linkers from ‘Is life worth living?’ (which is a useful source of cheminformatics wisdom; https://iwatobipen.wordpress.com/2020/01/23/cut-molecule-to-ring-and-linker-with-rdkit-rdkit-chemoinformatics-memo/). Obviously in practice you may be applying lots of different filters to enumerated molecules, but this is just a small example of something I found useful. 

The Jupyter Notebook can be found at: 

https://github.com/stephwills/Demo-removing-stringy-molecules/blob/main/Molecule%20filter.ipynb

Happy coding, 

Steph 

Meeko: Docking straight from SMILES string

When docking, using software like AutoDock Vina, you must prepare your ligand by protonating the molecule, generating 3D coordinates, and converting it to a specific file format (in the case of Vina, PDBQT). Docking software typically needs the protein and ligand file inputs to be written on disk. This is limiting as generating 10,000s of files for a large virtual screen can be annoying and hinder the speed at which you dock.

Fortunately, the Forli group in Scripps Research have developed a Python package, Meeko, to prepare ligands directly from SMILES or other molecule formats for docking to AutoDock 4 or Vina, without writing any files to disk. This means you can dock directly from a single file containing all the SMILES of the ligands you are investigating!

Continue reading →

Visualise with Weight and Biases

Understanding what’s going on when you’ve started training your shiny new ML model is hard enough. Will it work? Have I got the right parameters? Is it the data? Probably.  Any tool that can help with that process is a Godsend. Weights and biases is a great tool to help you visualise and track your model throughout your production cycle. In this blog post, I’m going to detail some basics on how you can initialise and use it to visualise your next project.

Installation

To use weights and biases (wandb), you need to make an account. For individuals it is free, however, for team-oriented features, you will have to pay. Wandb can then be installed using pip or conda.

$ 	conda install -c conda-forge wandb

or 

$   pip install wandb

To initialise your project, import the package, sign in, and then use the following command using your chosen project name and username (if you want):

import wandb

wandb.login()

wandb.init(project='project1')

In addition to your project, you can also initialise a config dictionary with starting parameter values:

Continue reading →

Viewing fragment elaborations in RDKit

As a reasonably new RDKit user, I was relieved to find that using its built-in functionality for generating basic images from molecules is quite easy to use. However, over time I have picked up some additional tricks to make the images generated slightly more pleasing on the eye!

The first of these (which I definitely stole from another blog post at some point…) is to ask it to produce SVG images rather than png:

#ensure the molecule visualisation uses svg rather than png format
IPythonConsole.ipython_useSVG=True

Now for something slightly more interesting: as a fragment elaborator, I often need to look at a long list of elaborations that have been made to a starting fragment. As these have usually been docked, these don’t look particularly nice when loaded straight into RDKit and drawn:

#load several mols from a single sdf file using SDMolSupplier
#add these to a list
elabs = [mol for mol in Chem.SDMolSupplier('frag2/elabsTestNoRefine_Docked_0.sdf')]

#get list of ligand efficiencies so these can be displayed alongside the molecules
LEs = [(float(mol.GetProp('Gold.PLP.Fitness'))/mol.GetNumHeavyAtoms()) for mol in elabs]

Draw.MolsToGridImage(elabs, legends = [str(LE) for LE in LEs])
Fig. 1: Images generated without doing any tinkering

Two quick changes that will immediately make this image more useful are aligning the elaborations by a supplied substructure (here I supplied the original fragment so that it’s always in the same place) and calculating the 2D coordinates of the molecules so we don’t see the twisty business happening in the bottom right of Fig. 1:

Continue reading →

Making better plots with matplotlib.pyplot in Python3

The default plots made by Python’s matplotlib.pyplot module are almost always insufficient for publication. With a ~20 extra lines of code, however, you can generate high-quality plots suitable for inclusion in your next article.

Let’s start with code for a very default plot:

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(1)
d1 = np.random.normal(1.0, 0.1, 1000)
d2 = np.random.normal(3.0, 0.1, 1000)
xvals = np.arange(1, 1000+1, 1)

plt.plot(xvals, d1, label='data1')
plt.plot(xvals, d2, label='data2')
plt.legend(loc='best')
plt.xlabel('Time, ns')
plt.ylabel('RMSD, Angstroms')
plt.savefig('bad.png', dpi=300)

The result of this will be:

Plot generated with matplotlib.pyplot defaults

The fake data I generated for the plot look something like Root Mean Square Deviation (RMSD) versus time for a converged molecular dynamics simulation, so let’s pretend they are. There are a number of problems with this plot: it’s overall ugly, the color scheme is not very attractive and may not be color-blind friendly, the y-axis range of the data extends outside the range of the tick labels, etc.

We can easily convert this to a much better plot:

Continue reading →

Dealing with multiple compilers

I don’t know you, but when I am compiling a complicated program and everything goes straightforward I feel a mixture of joy and surprise. Let’s face it, compiling can be quite frustrating, and if you need to compile something relatively old, chances are that you will spend hours and hours trying to understand the compiler error messages.

Several such compiler errors, that in many cases can be quite convoluted, tell you that your program requires an older version, so you first need to install it. I am going to assume that you have sudo rights, otherwise, we will be playing the game of compiling a compiler, something that I recommend you to do at least and at most once in your life.

In common Linux distributions like Ubuntu, installing an older compiler is as easy as using apt or yum:

#Ubuntu
$ sudo apt install build-essential
$ sudo apt install gcc-7 g++-7
Continue reading →

How to turn a SMILES string into a vector of molecular descriptors using RDKit

Molecular descriptors are quantities associated with small molecules that specify physical or chemical properties of interest. They can be used to numerically describe many different aspects of a molecule such as:

  • molecular graph structure,
  • lipophilicity (logP),
  • molecular refractivity,
  • electrotopological state,
  • druglikeness,
  • fragment profile,
  • molecular charge,
  • molecular surface,
  • …

Vectors whose components are molecular descriptors can be used (amongst other things) as high-level feature representations for molecular machine learning. In my experience, molecular descriptor vectors tend to fall slightly short of more low-level molecular representation methods such as extended-connectivity fingerprints or graph neural networks when it comes to predictive performance on large and medium-sized molecular property prediction data sets. However, one advantage of molecular descriptor vectors is their interpretability; there is a reasonable chance that the meaning of a physicochemical descriptor can be intuitively understood by a chemical expert.

A wide variety of useful molecular descriptors can be automatically and easily computed via RDKit purely on the basis of the SMILES string of a molecule. Here is a code snippet to illustrate how this works:

Continue reading →

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:

Continue reading →

Make your code do more, with less

When you wrangle data for a living, you start to wonder why everything takes so darn long. Through five years of introspection, I have come to conclude that two simple factors limit every computational project. One is, of course, your personal productivity. Your time of focused work, minus distractions (and yes, meetings figure here), times your energy and mental acuity. All those things you have little control over, unfortunately. But the second is the productivity of your code and tools. And this, in principle, is a variable that you have full control over.

Even quick calculations, when applied to tens of millions of sequences, can take quite some time!

This is a post about how to increase your productivity, by helping you navigate all those instances when the progress bar does not seem to go fast enough. I want to discuss actionable tools to make your code run faster, and generate more results, with less effort, in less time. Instructions to tinker less and think more, so you can do the science that you truly want to be doing. And, above all, I want to give out advice that is so counter-intuitive that you should absolutely consider following it.

Continue reading →