hcatgnet

Overview

HCat-GNet is an advanced graph neural network tool designed for optimizing ligand efficiency in asymmetric catalysis. This release introduces new capabilities for predicting the enantioselectivity of metal-ligand catalyzed reactions using only the SMILES notation of participant molecules, significantly advancing the field of computational chemistry.

Key Features

•	**Graph-Based Molecular Representation:** Automatically generates graph representations from SMILES for any asymmetric catalytic reaction.
•	**High Interpretability:** Provides insights into chiral ligand substituent effects, enhancing human understanding of structure-selectivity relationships.
•	**Performance Enhancements:** Improved prediction accuracy for Rhodium-catalyzed asymmetric 1,4-addition reactions, a critical process in pharmaceutical synthesis.
•	**Extensive Benchmarking:** HCat-GNet has been rigorously tested against traditional descriptor-based methods, demonstrating superior or comparable performance.

Updates in This Release

•	**Enhanced Learning Algorithms:** Incorporates updated graph convolutional networks that more accurately model complex molecular interactions. 
•	**Expanded Database Compatibility:** Supports a wider range of datasets, improving the model's ability to generalize across different chemical spaces.
•	**Advanced Error Analysis Tools:** Includes new functionalities for detailed error analysis, helping researchers pinpoint and address prediction inaccuracies.