37 citations · 61 across the 5 of their papers we have counts for
5 papers
A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges
Ziqiao Meng, Peilin Zhao, Yang Yu +1
Reaction and retrosynthesis prediction are fundamental tasks in computational chemistry that have recently garnered attention from both the machine learning and drug discovery comm…
Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction
Ziqiao Meng, Peilin Zhao, Yang Yu +1
Organic reaction prediction is a critical task in drug discovery. Recently, researchers have achieved non-autoregressive reaction prediction by modeling the redistribution of elect…
ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery
Lanqing Li, Liang Zeng, Ziqi Gao +11
The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD). However, real-world pharmaceutical datasets…
MARS: A Motif-based Autoregressive Model for Retrosynthesis Prediction
Jiahan Liu, Chaochao Yan, Yang Yu +4
Retrosynthesis is a major task for drug discovery. It is formulated as a graph-generating problem by many existing approaches. Specifically, these methods firstly identify the reac…
RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction
Chaochao Yan, Peilin Zhao, Chan Lu +2
The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template sel…