Shuang Wang

CRAFT: A Chemical Reaction-Aware Transformer for Synthesizable Molecular Optimization via Multimodal Learning

CRAFT is a reaction-aware molecular optimization framework that integrates Monte Carlo Tree Search (MCTS) with a multimodal strategy network and explicitly encodes two-dimensional molecular topology and atom-level reaction roles, enabling chemically feasible transformations during multi-step optimization.

Fourier Kolmogorov-Arnold Network and Hypergraph Enhanced Contrastive Learning for Recommendation

This work proposes Fourier Kolmogorov-Arnold Network and Hypergraph Enhanced Contrastive Learning for recommendation and integrates these views through contrastive learning to generate robust and accurate recommendations.

From knowledge to rules: Explainable molecular optimization with Large Language Models

PSDTA: An Approach to Drug-Target Binding Affinity Prediction by Integrating Physicochemical and Structural Information to Reduce Feature Redundancy.

This work proposes a DTA prediction method, PSDTA, which integrates physicochemical properties into the initial feature representations and explicitly incorporates structural information on amino acids, thereby avoiding the risk of information leakage caused by directly using coordinates as features and enhancing the m...