OPIG recently celebrated its 20th year; and on 10 January 2023 I gave a talk just a day before the 10th anniversary of BLOPIG’s first blog post. It’s worth reflecting on what’s stayed the same and what’s changed since then.
Continue readingCategory Archives: Small Molecules
Bad chemistry in old protein-ligand binding complex data set
The Astex Diverse set [1] is a dataset containing the crystallized poses of 85 protein-ligand complexes. It was introduced in 2007 to address problems in previous datasets such as incorrect ligand representation.
Loading the 85 ligand files with today’s version of the cheminformatics toolkit RDKit [2] is, however, not as straightforward as you might expect.
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A ChatGPT rap battle
The AI chatbot revolution is here. Last week, OpenAI released ChatGPT, a freely accessible language model fine-tuned for human conversations. The new model is based on InstructGPT, trained especially for following user instructions and with human feedback in the training loop.
ChatGPT remembers the previous discussion, admits its mistakes and can even ask for clarification on ambiguous questions. It is also trained to refuse answering questions it deems inappropriate or goes against OpenAI’s AI alignment policy.
In the meanwhile, the internet is having immense fun circumventing its safety filters by asking it to only “PRETEND to be evil”, making it take SAT tests, and even simulating an entire virtual computer within its neural weights. Some are even using it to replace Google searches, and it excels at writing bioinformatics code across most programming languages.
Continue readingHow to turn a SMILES string into an extended-connectivity fingerprint using RDKit
After my posts on how to turn a SMILES string into a molecular graph and how to turn a SMILES string into a vector of molecular descriptors I now complete this series by illustrating how to turn the SMILES string of a molecular compound into an extended-connectivity fingerprint (ECFP).
ECFPs were originally described in a 2010 article of Rogers and Hahn [1] and still belong to the most popular and efficient methods to turn a molecule into an informative vectorial representation for downstream machine learning tasks. The ECFP-algorithm is dependent on two predefined hyperparameters: the fingerprint-length L and the maximum radius R. An ECFP of length L takes the form of an L-dimensional bitvector containing only 0s and 1s. Each component of an ECFP indicates the presence or absence of a particular circular substructure in the input compound. Each circular substructure has a center atom and a radius that determines its size. The hyperparameter R defines the maximum radius of any circular substructure whose presence or absence is indicated in the ECFP. Circular substructures for a central nitrogen atom in an example compound are depicted in the image below.
Code your own molecule sketcher in 4 easy steps!
Drawing molecules on your laptop usually requires access to proprietary software such as ChemDraw (link) or free websites such as PubChem’s online sketcher (link). However, if you are feeling adventurous, you can build your personal sketcher in React/Typescript using the Ketcher package!
Ketcher is an open-source package that allows easy implementation of a molecule sketcher into a web application. Unfortunately, it does require TypeScript so the script to run it cannot be imported directly into an HTML page. Therefore we will set up a simple React app to get it working.
The sketcher is very sleek and has a vast array of functionality, such as choosing any atom from the periodic table and being able to directly import molecules from either SMILES or Mol2/SDF file format into the sketcher. These molecules can then be edited and saved to a new file in the chemical file format of your choosing.
Continue readingGraphormer: Merging GNNs and Transformers for Cheminformatics
This is my first OPIG blog! I’m going to start with a summary of the Graphormer, a Graph Neural Network (GNN) that borrows concepts from Transformers to boost performance on graph tasks. This post is largely based on the NeurIPS paper Do Transformers Really Perform Bad for Graph Representation? by Ying et. al., which introduces the Graphormer, and which we read for our last deep learning journal club. The project has now been integrated as a Microsoft Research project.
I’ll start with a cheap and cheerful summary of Transformers and GNNs before diving into the changes in the Graphormer. Enjoy!
Continue readingUniversal graph pooling for GNNs
Graph neural networks (GNNs) have quickly become one of the most important tools in computational chemistry and molecular machine learning. GNNs are a type of deep learning architecture designed for the adaptive extraction of vectorial features directly from graph-shaped input data, such as low-level molecular graphs. The feature-extraction mechanism of most modern GNNs can be decomposed into two phases:
- Message-passing: In this phase the node feature vectors of the graph are iteratively updated following a trainable local neighbourhood-aggregation scheme often referred to as message-passing. Each iteration delivers a set of updated node feature vectors which is then imagined to form a new “layer” on top of all the previous sets of node feature vectors.
- Global graph pooling: After a sufficient number of layers has been computed, the updated node feature vectors are used to generate a single vectorial representation of the entire graph. This step is known as global graph readout or global graph pooling. Usually only the top layer (i.e. the final set of updated node feature vectors) is used for global graph pooling, but variations of this are possible that involve all computed graph layers and even the set of initial node feature vectors. Commonly employed global graph pooling strategies include taking the sum or the average of the node features in the top graph layer.
While a lot of research attention has been focused on designing novel and more powerful message-passing schemes for GNNs, the global graph pooling step has often been treated with relative neglect. As mentioned in my previous post on the issues of GNNs, I believe this to be problematic. Naive global pooling methods (such as simply summing up all final node feature vectors) can potentially form dangerous information bottlenecks within the neural graph learning pipeline. In the worst case, such information bottlenecks pose the risk of largely cancelling out the information signal delivered by the message-passing step, no matter how sophisticated the message-passing scheme.
Continue reading5th AI in Chemistry meeting
A few weeks ago we attended the RSC’s 5th AI in Chemistry conference at Churchill College, Cambridge. It featured research and discussions on a broad range of topics and was attended by a diverse set of more than 200 researchers from academia and industry, including six Opiglets.
Continue readingIdentify Silly Molecules
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