[Nature Machine Intelligence, 2024] This paper introduces PSICHIC, a graph neural network framework that leverages physicochemical constraints to predict protein-ligand interactions directly from sequence data. PSICHIC achieves state-of-the-art accuracy in binding affinity prediction, even surpassing existing structure-based methods in certain cases. Furthermore, its interpretable fingerprints illuminate the specific protein residues and ligand atoms involved in these interactions, offering a promising tool for virtual screening and enhancing our understanding of protein-ligand mechanisms.

[PIEEE, 2024] This paper discusses the growing relevance of graph neural networks (GNNs) across various real-world applications, from recommendation systems to drug discovery, emphasizing the need for trustworthy GNNs beyond task performance. The survey proposes a comprehensive roadmap for building such GNNs, addressing six key aspects: robustness, explainability, privacy, fairness, accountability, and environmental well-being. Additionally, it highlights the interrelations among these aspects and presents future directions for advancing trustworthy GNN research and its industrial applications.

[ICLR-2024] In this paper, we introduce Reasoning on Graphs (RoG), a novel method that enhances Large Language Models (LLMs) with Knowledge Graphs (KGs) to address their limitations in up-to-date knowledge and reasoning hallucinations, by utilizing KGs for faithful and interpretable reasoning. RoG employs a planning-retrieval-reasoning framework to generate relation paths from KGs, enabling LLMs to perform more accurate reasoning, and has shown state-of-the-art performance on benchmark KG reasoning tasks.

[ICLR-2024] This work introduces Time-LLM, a novel reprogramming framework that adapts Large Language Models (LLMs) for general time series forecasting, overcoming the challenges of data sparsity and modality alignment between time series and natural language. By reprogramming time series data with text prototypes and employing the Prompt-as-Prefix (PaP) technique for enriched input context, Time-LLM demonstrates superior forecasting performance, outshining specialized models in both few-shot and zero-shot learning scenarios.

[TKDE-2024] This article introduces a roadmap for integrating Large Language Models (LLMs) like ChatGPT and GPT4 with Knowledge Graphs (KGs) to leverage their complementary strengths in natural language processing and artificial intelligence. It outlines three frameworks for this unification: KG-enhanced LLMs, LLM-augmented KGs, and a synergistic approach, aiming to improve both factual knowledge access and interpretability while addressing the challenges of KG construction and evolution.