Cheminformatics on the Web (2021)

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Here, I have compiled a list of some tools and possible solutions. +The web is a nice platform, it is available anywhere and just requires an internet connection. +I, personally like static websites which don't require a server side application and can be hosted on platforms like GitHub Pages. +Or, just open the HTML file and run it in your browser. +No data is required to be sent to any server and your device's computational power is used. +Even our phones have a lot of computational power now, which allows the user to run tasks on the go without needing to worry about managing dependencies. +WebAssembly (Wasm) has made running code written for other platfroms on the web relativevly easier. +Combine Wasm with some pure JavaScript libraries, and you get a platform to quickly amp up your speed in some common tasks.

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RDKit

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RDKit bundles a minimal JavaScript Wrapper in their core RDKit suite. +This is perfect for generating 2D Figures (HTML5 Canva/SVGs), Cannonical SMILES, Descriptors e.t.c

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Substructure Matching

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This can be used to flag undesirable functional groups in a given compound. +Create a simple key:value pair of name:SMARTS and use it to highlight substructure matches. +Thus, something like PostEra's Medicinal Chemistry Alert can be done with RDKit-JS alone.

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PostEra Demo

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Computing Properties

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This is useful to calculate basic properties of a given compound.

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RDKit-JS Demo

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Webina - Molecular Docking

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Webina is a JavaScript/Wasm library that runs AutoDock Vina, which can enable you to run Molecular Docking straight in the browser itself.

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Webina Demo

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Obviously, it takes a few hits in the time to complete the docking because the code is transpiled from C++ to Wasm. +But, the only major drawback (for now) is that it uses SharedArrayBuffer. +Due to Spectre, this feature was disabled on all browsers. +Currently, only Chromium-based and Firefox browsers have reimplemented and renabled it. +Hopefully, soon this will be again supported by all major browsers.

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Machine Learning

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Frameworks have now evolved enough to allow exporting models to be able to run them through JavaScript/Wasm backend. +An example task can be NER or Named-entity Recognition. +It can be used to extract compounds or diseases from a large blob of text and then matched with external refferences. +Another example is target-prediction right in the browser: CHEMBL - Target Prediction in Browser

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CHEMBL Group is first training the model using PyTorch (A Python ML Library), then converting it to the ONNX runtime. +A model like this can be directly implemented in Tensorflow, and then exported to be able to run with TensorFlow.js

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Cheminfo-to-web

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The project aims to port chemoinformatics libraries into JavaScript via Emscripten. +They have ported InChI, Indigo, OpenBabel, and OpenMD

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Kekule.js

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It is written by @partridgejiang, who is behind the Cheminfo-to-web project

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It is molecule-centric, focusing on providing the ability to represent, draw, edit, compare and search molecule structures on web browsers.
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Browser Extensions

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The previous machine learning examples can be packaged as browser-extensions to perform tasks on the article you are reading. +With iOS 15 bringing WebExtensions to iOS/iPadOS, the same browser extension source code can be now used on Desktop and Mobile Phones. +You can quickly create an extenison to convert PDB codes into links to RCSB, highlight SMILES, highlight output of NER models, e.t.c

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Conclusion

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I have not even touched all the bases of cheminformatics for the web here. +There is still a lot more to unpack. +Hopefully, this encourages you to explore the world of cheminformatics on the web.

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