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 readingCategory Archives: Small Molecules
Identify 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
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 reading5th Artificial Intelligence in Chemistry Symposium
The lineup for the Royal Society of Chemistry’s 5th “Artificial Intelligence in Chemistry” Symposium (Thursday-Friday, 1st-2nd September 2022) is now complete for both oral and poster presentations. It really is a fantastic selection of topics and speakers and it is clear this event is now a highlight of the scientific calendar. Our very own Prof. Charlotte M. Deane, MBE will be giving a keynote.
It marks a return to in-person meetings: it will be held at Churchill College, Cambridge, with a conference dinner at Trinity Hall.
More details are here: https://www.rscbmcs.org/events/aichem22/.
Registration for in person attendance is open until Monday 29th August 17:00 (BST).
It is also possible to register for virtual attendance; the meeting will be broadcast on Zoom.
Exploring topological fingerprints in RDKit
Finding a way to express the similarity of irregular and discrete molecular graphs to enable quantitative algorithmic reasoning in chemical space is a fundamental problem in data-driven small molecule drug discovery.
Virtually all algorithms that are widely and successfully used in this setting boil down to extracting and comparing (multi-)sets of subgraphs, differing only in the space of substructures they consider and the extent to which they are able to adapt to specific downstream applications.
A large body of recent work has explored approaches centred around graph neural networks (GNNs), which can often maximise both of these considerations. However, the subgraph-derived embeddings learned by these algorithms may not always perform well beyond the specific datasets they are trained on and for many generic or resource-constrained applications more traditional “non-parametric” topological fingerprints may still be a viable and often preferable choice .
This blog post gives an overview of the topological fingerprint algorithms implemented in RDKit. In general, they count the occurrences of a certain family of subgraphs in a given molecule and then represent this set/multiset as a bit/count vector, which can be compared to other fingerprints with the Jaccard/Dice similarity metric or further processed by other algorithms.
Continue readingViewing 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])
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 readingHow 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 readingFrom code to molecules: The future of chemical synthesis
In June, after I finish my PhD, I will be joining Chemify, a new startup based in Glasgow that aims to make chemical synthesis universally accessible, reproducible and fully automated using AI and robotics. After previously talking about “Why you should care about startups as a researcher” and a quick guide on “Commercialising your research: Where to start?” on this blog, I have now joined a science-based startup fresh out of university myself.
Chemify is a spinout from the University of Glasgow originating from the group of Prof. Lee Cronin. The core of the technology is the chemical programming language χDL (pronounced “chi DL”) that, in combination with a natural language processing AI that reads and understands chemical synthesis procedures, can be used to plan and autonomously executed chemical reactions on robotic hardware. The Cronin group has also already build the modular robotic hardware needed to carry out almost any chemical reaction, the “Chemputer”. Due to the flexibility of both the Chemputer and the χDL language, Chemify has already shown that the applications go way beyond simple synthesis and can be applied to drug formulation, the discovery of new materials or the optimisation of reaction conditions.
Armed with this transformational software and hardware, Chemify is now fully operational and is hiring exceptional talent into their labs in Glasgow. I am excited to see how smart, AI-driven automation techniques like Chemify will change how small scale chemical synthesis and chemical discovery more broadly is done in the future. I’m super excited to be part of the journey.
Paper review: “EquiBind”
Molecular docking helps us understand how small-molecules interact with proteins. This is especially useful in early drug development stages such as target identification and compound screening. Quick and accurate docking software allows researchers to focus their attention on a smaller set of lead molecules for further testing. Traditionally, docking software has employed first principles from physics and chemistry. Recently, deep learning has become all the rage for molecular docking, maybe motivated by the successful application of deep learning to molecular folding.
Method
EquiBind is a deep learning unconstrained docking method which models a fixed receptor and a ligand with selected rotatable bonds. It predicts the binding pocket and the ligand’s conformation within the pocket in one go. Under the hood, EquiBind employs two great ideas from a recent ICLR 2022 Paper: a SE3-invariant graph neural network based architecture and the idea to generate fixed sets of matching key points to define a rotation and translation between receptor and ligand. In addition, the authors innovate a fast method to project a deformed ligand onto the space spanned by the rotatable bonds of a pre-generated ligand conformation.
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