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Results indicate that incorporating molecular cost information into heuristic search can improve the practicality and economic efficiency of retrosynthetic planning.
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.
A multimodal dual-contrastive learning framework for peptide property prediction is proposed, which improves both the structural encoder and the contrastive learning strategy to enhance the quality of joint sequence-structure representations.
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