Deep Learning and Graph-Based Framework for Intelligent Chemical Reaction Representation and Prediction using B+ Trees

Conventional approaches of representing and searching chemical reaction databases may fail with redundancy, scalability and could fail to capture complex reaction patterns. This study would suggest a new hybrid framework for integrating deep learning, graph representation, and efficient data structure in managing intelligent chemical reactions. We represent molecules as graphs and Graph Neural Network (GNN) design generate topological and functional group information in the form of unique, continuous vector embeddings. The embeddings are then indexed with the use of a dual B+ Tree architecture which allows bidirectional search, i.e. reactants to products and the other way around, to be highly efficient. This structure removes any form of data redundancy and provides the ability to search in a logarithmic time with respect to the size of the database. Moreover, the learned reaction embeddings are a feature space base for a deep learning-based reaction prediction module. We have shown in our experiments that the proposed framework is not only capable of providing rapid retrieval, but also a high rate of accuracy in predicting the outcomes of the reaction, which make it a very comprehensive and intelligent solution to the chemical reaction representation and prediction