AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Results indicate that incorporating molecular cost information into heuristic search can improve the practicality and economic efficiency of retrosynthetic planning.
Evaluations across three downstream tasks show that KAGT achieves strong performance relative to existing baselines in reaction classification, reaction condition prediction, and yield prediction, supporting KAGT as a transferable representation framework for AI-driven chemical synthesis.
Experimental results show that Hyp-Retro outperforms comparative methods in terms of search success rate and route yield, and it can adaptively adjust its planning strategy under different yield preferences, thereby generating high-quality retrosynthetic routes that better satisfy target-specific requirements.
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