FGAIM: Identifying Drug-Target Activation and Inhibition Mechanisms via Inductive Graph Neural Networks Based on Fine-Grained Interaction Strategies.

Abstract

Distinguishing the activation and inhibition mechanisms between drugs and targets can reveal the potential regulatory pathways of target functions, which is crucial in drug discovery and development. Although numerous deep learning-based computational methods have been proposed, most of them extract drug and target features independently while neglecting interactions between drug molecules and protein residues. In addition, existing methods approach drugs in a relatively simple way and predominantly rely on protein sequence information. Deep learning, particularly graph neural networks (GNNs), has demonstrated unique advantages in processing data with complex graph-structured relationships. Motivated by these limitations, this study proposes a novel computational method named FGAIM, which designed to effectively identify activation and inhibition mechanisms between drugs and targets. First, a multi-scale GNN module in FGAIM is employed to learn expressive drug molecular embeddings. At the same time, protein representations are constructed by integrating pre-trained language model (PLM) embeddings and structural information derived from 3D conformations. Subsequently, FGAIM leverages a GraphSAGE module to extract features from both the primary drug graph and the fine-grained drug-protein interaction graph. Finally, the resulting drug and target embeddings are fused and fed into a Multilayer Perceptron (MLP) for classification prediction. Comparative experiments on two public datasets demonstrate that FGAIM significantly outperforms existing computational approaches and exhibits strong generalization capabilities. Further case studies reveal the advantages of FGAIM in mining previously unrecognized activation/inhibition relationships. Building upon the fine-grained interactions, we further explored the model's interpretability at the molecular structural level through analysis of attention weights. Additionally, visualization analyses confirm that FGAIM effectively captures underlying structural patterns in the data and identifies discriminative features across different categories.