Hierarchical Framework for Retrosynthesis Prediction with Enhanced Reaction Center Localization
Year: 2024
Type: preprint
Abstract: Retrosynthesis is essential for designing synthetic pathways for complex molecules and can be revolutionized by AI to automate and accelerate chemical synthesis planning for drug discovery and materials science. Here, we propose a hierarchical framework for retrosynthesis prediction that systematically integrates reaction center identification, action prediction, and termination decision into a unified pipeline. Leveraging a molecular encoder pretrained with contrastive learning, the model captures both atom and bond level representations, enabling accurate identification of reaction centers and prediction of chemical actions. The framework addresses the scarcity of multiple reaction center data through augmentation strategies, enhancing the ability of the model to generalize to diverse reaction scenarios. The proposed approach achieves competitive performance across benchmark datasets, with notably high topk accuracy and exceptional reaction center identification capabilities, demonstrating its robustness in handling complex transformations. These advancements position the framework as a promising tool for future applications in material design and drug discovery.
Source: arXiv (Cornell University)
Authors: Yun, Seongeun, Lee, Won Bo
Institution: Seoul National University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405030441