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.
Continue readingCategory Archives: Python
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 
Since in this scenario the model has never seen any of the examples in 
Automated intermolecular interaction detection using the ODDT Python Module
Detecting intermolecular interactions is often one of the first steps when assessing the binding mode of a ligand. This usually involves the human researcher opening up a molecular viewer and checking the orientations of the ligand and protein functional groups, sometimes aided by the viewer’s own interaction detecting functionality. For looking at single digit numbers of structures, this approach works fairly well, especially as more experienced researchers can spot cases where the automated interaction detection has failed. When analysing tens or hundreds of binding sites, however, an automated way of detecting and recording interaction information for downstream processing is needed. When I had to do this recently, I used an open-source Python module called ODDT (Open Drug Discovery Toolkit, its full documentation can be found here).
My use case was fairly standard: starting with a list of holo protein structures as pdb files and their corresponding ligands in .sdf format, I wanted to detect any hydrogen bonds between a ligand and its native protein crystal structure. Specifically, I needed the number and name of the the interacting residue, its chain ID, and the name of the protein atom involved in the interaction. A general example on how to do this can be found in the ODDT documentation. Below, I show how I have used the code on PDB structure 1a9u.
Continue readingHidden Markov Models in Python: A simple Hidden Markov Model with Known Emission Matrix fitted with hmmlearn
The Hidden Markov Model
Consider a sensor which tells you whether it is cloudy or clear, but is wrong with some probability. Now, the weather *is* cloudy or clear, we could go and see which it was, so there is a “true” state, but we only have noisy observations on which to attempt to infer it.
We might model this process (with the assumption of sufficiently precious weather), and attempt to make inferences about the true state of the weather over time, the rate of change of the weather and how noisy our sensor is by using a Hidden Markov Model.
The Hidden Markov Model describes a hidden Markov Chain which at each step emits an observation with a probability that depends on the current state. In general both the hidden state and the observations may be discrete or continuous.
But for simplicity’s sake let’s consider the case where both the hidden and observed spaces are discrete. Then, the Hidden Markov Model is parameterised by two matrices:
Continue readingCode 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.
Continue readingC++ 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 pybind11Continue reading
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.
ORDER!: Returning bond order information to your docked poses
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.
Continue readingTo Pickle, Or Not To Pickle? — Quickle!
Pickling in Python can be dangerous.
That’s where Quickle comes in — as long as you’re using Python 3.8 or later…
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.