Category Archives: Small Molecules

Stochastic chemical kinetics – things randomly bumping into each other

In this blog post I describe the advantages of taking a stochastic view of chemical systems based on the work of D. T. Gillespie and subsequent publications. Gillespie presented his formalism for considering stochastic chemical kinetics, now referred to as the Gillespie Algorithm, in two papers published in 1976 and 1977 (Gillespie, D. T. J. Comp. Phys. 1976, Gillespie D. T. J. Phys. Chem. 1977) – if you want to see the full derivation for the Gillespie Algorithm along with many examples I recommend giving them both a read.

The essential question of chemical kinetics as stated by Gillespie is:

“If a fixed volume V contains a spatially uniform mixture of N chemical species which can inter-react through M specified chemical reaction channels, then given the numbers of molecules of each species present at some initial time, what will these molecular population levels be at any later time?”

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Model validation in Crystallographic Fragment Screening

Fragment based drug discovery is a powerful technique for finding lead compounds for medicinal chemistry. Crystallographic fragment screening is particularly useful because it informs one not just about whether a fragment binds, but has the advantage of providing information on how it binds. This information allows for rational elaboration and merging of fragments.

However, this comes with a unique challenge: the confidence in the experimental readout, if and how a fragment binds, is tied to the quality of the crystallographic model that can be built. This intimately links crystallographic fragment screening to the general statistical idea of a “model”, and the statistical ideas of goodness of fit and overfitting.

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Targeted protein degradation phenotypic studies using HaloTag CRISPR/Cas9 endogenous target tagging and HaloPROTAC

Biologists currently have several options in their arsenal when it comes to gene silencing. if you want to completely vanquish the gene in question, you can use CRISPR to knock the gene out completely. This is a great way to completely eliminate the gene, and hence compare cell phenotypes with and without the gene, but it’s less good if the gene is essential and the cells won’t grow without it in the first place. 

Otherwise you can use RNA interference, where small pieces of RNA that complement the mRNA for that gene are introduced to the cell, with the overall effect of blocking transcription of that gene’s mRNA, hence silencing it. However, this method suffers from side effects and varying levels of gene knockdown efficiency. Moreover, it does not vanquish existing protein, it just stops more from being produced.

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Benchmarks in De Novo Drug Design

I recently came across a review of “De novo molecular drug design benchmarking” by Lauren L. Grant and Clarissa S. Sit where they highlighted the recently proposed benchmarking methods including Fréchet ChemNet Distance [1], GuacaMol [2], and Molecular Sets (MOSES) [3] together with its current and future potential applications as well as the steps moving forward in terms of validation of benchmarking methods [4].

From this review, I particularly wanted to note about the issues with current benchmarking methods and the points we should be aware of when using these methods to benchmark our own de novo molecular design methods. Goal-directed models are referring to de novo molecular design methods optimizing for a particular scoring function [2].

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Getting the PDB structures of compounds in ChEMBL

Recently I was dealing with a set of compounds with known target activities from the ChEMBL database, and I wanted to find out which of them also had PDB  crystal structures in complex with that target.

Referencing this manually is very easy for cases where we are interested in 2-3 compounds, but for any larger number, using the ChEMBL and PDB web services greatly reduces the number of clicks.

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Issues with graph neural networks: the cracks are where the light shines through

Deep convolutional neural networks have lead to astonishing breakthroughs in the area of computer vision in recent years. The reason for the extraordinary performance of convolutional architectures in the image domain is their strong ability to extract informative high-level features from visual data. For prediction tasks on images, this has lead to superhuman performance in a variety of applications and to an almost universal shift from classical feature engineering to differentiable feature learning.

Unfortunately, the picture is not quite as rosy yet in the area of molecular machine learning. Feature learning techniques which operate directly on raw molecular graphs without intermediate feature-engineering steps have only emerged in the last few years in the form of graph neural networks (GNNs). GNNs, however, still have not managed to definitively outcompete and replace more classical non-differentiable molecular representation methods such as extended-connectivity fingerprints (ECFPs). There is an increasing awareness in the computational chemistry community that GNNs have not quite lived up to the initial hype and still suffer from a number of technical limitations.

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How to interact with small molecules in Jupyter Notebooks

The combination of Python and the cheminformatics toolkit RDKit has opened up so many ways to explore chemistry on a computer. Jupyter — named for the three languages, Julia, Python, and R — ties interactivity and visualization together, creating wonderful environments (Notebooks and JupyterLab) to carry out, share and reproduce research, including:

“data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.”

—https://jupyter.org

At this year’s annual RDKit UGM (User Group Meeting), Cédric Bouysset shared a tutorial explaining how to create a grid of molecules that you can interact with, using his “mols2grid“:

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Watch out when using PDBbind!

Now that PDBbind 2020 has been released, I want to draw some attention to an issue with using the SDF files that are supplied in the PDBbind refined set 2020.

Normally, SDF files save the chirality information of compounds in the atom block of the file which is shown belowas a snipped of the full sdf file for the ligand of PDB entry 4qsv. The column that defines chirality is marked in red.

As you can see, all columns shown here are 0. The SDF files supplied by PDBbind for some reason do NOT encode chirality information explicitly. This will be a problem when using RDKit to read the molecule and transform it into a smiles string. By using the following commands to read the ligand for 4qsv from PDBBind 2020 and write a SMILES string, we get:

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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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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.

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