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Predicting Organic Reactions using Neural Networks.
The intend is to solve the forward-reaction prediction problem, where the reactants are known and the interest is in generating the reaction products using Deep learning. This Graphical User Interface takes simplified molecular-input line-entry system (SMILES) as an input and generates the product SMILE & molecule.
Beam search is used in Version 2, to generate top 5 predictions.
Maximum input length for the model is 15 (excluding spaces).
A collection of cheminformatics and machine-learning software written in C++ and Python.
NOTE: the RDKit source code and downloads are now in github: https://github.com/rdkit/rdkit
The core algorithms and data structures are written in C++. Wrappers are provided to use the toolkit from either Python or Java.
Additionally, the RDKit distribution includes a PostgreSQL-based cartridge that allows molecules to be stored in relational database and retrieved via substructure and similarity searches.