Uncertainty-Aware Metabolic Stability Prediction with Dual-View Contrastive Learning

no code implementations • 1 Jun 2025 • Peijin Guo, Minghui Li, Hewen Pan, Bowen Chen, Yang Wu, Zikang Guo, Leo Yu Zhang, Shengshan Hu, Shengqing Hu

Despite recent advances in graph neural networks (GNNs) for MS prediction, current approaches face two critical limitations: (1) incomplete molecular modeling due to atom-centric message-passing mechanisms that disregard bond-level topological features, and (2) prediction frameworks that lack reliable uncertainty quantification.

Contrastive Learning Prediction +1

MIRROR: Multi-agent Intra- and Inter-Reflection for Optimized Reasoning in Tool Learning

no code implementations • 27 May 2025 • Zikang Guo, Benfeng Xu, Xiaorui Wang, Zhendong Mao

Complex tasks involving tool integration pose significant challenges for Large Language Models (LLMs), leading to the emergence of multi-agent workflows as a promising solution.

Multi-Modality Representation Learning for Antibody-Antigen Interactions Prediction

1 code implementation • 22 Mar 2025 • Peijin Guo, Minghui Li, Hewen Pan, Ruixiang Huang, Lulu Xue, Shengqing Hu, Zikang Guo, Wei Wan, Shengshan Hu

While deep learning models play a crucial role in predicting antibody-antigen interactions (AAI), the scarcity of publicly available sequence-structure pairings constrains their generalization.

Graph Attention Prediction +1

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion

no code implementations • 27 Dec 2024 • Minghui Li, Zikang Guo, Yang Wu, Peijin Guo, Yao Shi, Shengshan Hu, Wei Wan, Shengqing Hu

By incorporating virtual graph nodes, we seamlessly integrate local and global features of drug molecular structures, expanding the GNN's receptive field.

Drug Discovery Graph Neural Network