HCAR1 antagonist screening based on boundary-selected negative sampling strategy and multi-level graph neural network

Hydroxycarboxylic acid receptor 1 (HCAR1), also known as the lactate receptor, is closely associated with tumorigenesis and cancer progression due to its aberrant activation, making it an attractive therapeutic target for cancer treatment. Accurate prediction of HCAR1 antagonists is therefore crucial for tumor immunotherapy. However, traditional drug screening suffers from high costs and suboptimal performance caused by imbalanced datasets and incomplete molecular representations, contributing to the scarcity of clinically available HCAR1 antagonists.

A balanced HCAR1 target activity dataset was constructed using a boundary-selected negative sampling strategy. Subsequently, a multi-level graph neural network (Multi-GNN) was proposed for HCAR1 target activity prediction, integrating multiple molecular representations, including fingerprints, molecular graphs, and fragment-level features.

Experimental results demonstrate that the proposed model outperforms eight state-of-the-art methods in comparative evaluations. Furthermore, approximately ten million compounds were screened using the trained Multi-GNN model in combination with physicochemical filtering and molecular docking, yielding five candidate compounds. Finally, in vitro cAMP antagonistic activity assays identified a promising HCAR1 inhibitor with an IC₅₀ of 22.39 μM.

This study introduces a novel artificial intelligence-based framework for HCAR1-targeted drug discovery and highlights potential lead compounds for further development.