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Ualign: pushing the limit of template-free retrosynthesis prediction with unsupervised SMILES alignment.
Zeng, Kaipeng; Yang, Bo; Zhao, Xin; Zhang, Yu; Nie, Fan; Yang, Xiaokang; Jin, Yaohui; Xu, Yanyan.
Afiliación
  • Zeng K; MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China.
  • Yang B; Frontiers Science Center for Transformative Molecules (FSCTM), Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China.
  • Zhao X; MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China.
  • Zhang Y; MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China.
  • Nie F; Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China.
  • Yang X; MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China.
  • Jin Y; MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China. jinyh@sjtu.edu.cn.
  • Xu Y; MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China. yanyanxu@sjtu.edu.cn.
J Cheminform ; 16(1): 80, 2024 Jul 15.
Article en En | MEDLINE | ID: mdl-39010144
ABSTRACT
MOTIVATION Retrosynthesis planning poses a formidable challenge in the organic chemical industry, particularly in pharmaceuticals. Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recent years due to advancements in AI for science. Various deep learning-based methods have been proposed for this task in recent years, incorporating diverse levels of additional chemical knowledge dependency.

RESULTS:

This paper introduces UAlign, a template-free graph-to-sequence pipeline for retrosynthesis prediction. By combining graph neural networks and Transformers, our method can more effectively leverage the inherent graph structure of molecules. Based on the fact that the majority of molecule structures remain unchanged during a chemical reaction, we propose a simple yet effective SMILES alignment technique to facilitate the reuse of unchanged structures for reactant generation. Extensive experiments show that our method substantially outperforms state-of-the-art template-free and semi-template-based approaches. Importantly, our template-free method achieves effectiveness comparable to, or even surpasses, established powerful template-based methods. SCIENTIFIC CONTRIBUTION We present a novel graph-to-sequence template-free retrosynthesis prediction pipeline that overcomes the limitations of Transformer-based methods in molecular representation learning and insufficient utilization of chemical information. We propose an unsupervised learning mechanism for establishing product-atom correspondence with reactant SMILES tokens, achieving even better results than supervised SMILES alignment methods. Extensive experiments demonstrate that UAlign significantly outperforms state-of-the-art template-free methods and rivals or surpasses template-based approaches, with up to 5% (top-5) and 5.4% (top-10) increased accuracy over the strongest baseline.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Cheminform Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Cheminform Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido