Target predictions in the browser with RDKit MinimalLib (JS) and ONNX.js

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Some time ago we showed an example of how a model trained in Python's PyTorch could be run in a C++ backend by exporting it to the ONNX format.

Greg also showed us in his blogpost how our multitask neural network model could be used in a very nice KNIME workflow by exporting it to ONNX. That was possible thanks to RDKit's Java bindings and the ONNX Java runtime.

As a refresher, most of the most popular machine learning frameworks can export their models to this format and many programming languages can load them to run the predictions. This certainly is a beautiful example of interoperability!

In November 2019 RDKit introduced a reduced functionality Javascript library which is able to do all we need in order to use our multitask model in the browser. So, the only thing that was left to do was to combine these two awesome tools... and we did it!

Here is our demo with its available source code. Start typing a smiles into the box and enjoy!

Updated code to generate the model is also available here. This updated code takes advantage of the PyTorch Lightning library.

Comments

ようこそ、ケンブルへ！ - Welcome to 剣舞瑠 ! -

The following is written in Japanese.... ケンブルチーム(ChEMBL Team)は、欧州バイオインフォマティクス研究所( EMBL-EBI )にあり、創薬研究に有用な化合物やターゲット情報を提供するデータベースを開発しています。 ChEMBLdb は、創薬研究に有用な医薬品化合物の情報を提供するデータベースです。現在、約50万個の化合物情報、約190万件の活性情報及びそれらのターゲット情報が登録されています。ユーザーは、生物活性化合物の情報を部分構造検索や類似性検索で調査したり、また、ターゲットのアミノ酸配列からBLAST検索でアッセイ情報を収集することができます。ケンブルチームでは、キナーゼに特化したカイネースサファリ( Kinase SARfari )のサービスも開始しました。日本語でのご質問、ご要望は kaz(at)ebi.ac.uk までどうぞ。チームメンバー一同、皆さんのご利用をお待ちしています！

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