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PLiSAGE: enhancing protein-ligand interaction prediction with multimodal surface and geometry encoding

Tianci Wang,Guanyu Qiao,Guohua Wang,Yang Li

Published 2025 in Bioinform.

ABSTRACT

Abstract Motivation Accurately predicting protein-ligand interactions is fundamental to elucidating molecular recognition and has far-reaching implications in drug discovery, gene regulation, and signal transduction. Conventional methods predominantly rely on internal structural or sequence-based protein representations. While these approaches have improved predictive performance, their dependence on limited labeled data restricts the capacity to learn expressive features from structural inputs. Moreover, they often neglect the intricate geometric and chemical context encoded on protein surfaces, limiting interpretability, and hindering mechanistic insights into binding interactions. Result Here, we present PLiSAGE, a multimodal framework that integrates 3D structural and surface geometric embeddings to enable accurate prediction of protein–ligand interactions. Central to our approach is the joint pretraining of structural and surface encoders through unsupervised contrastive learning and point cloud reconstruction. Protein surfaces are represented as segmented point cloud patches, allowing the model to capture fine-grained geometric and chemical cues. A Transformer-based encoder further captures both local and global spatial dependencies across patches. The incorporation of spatial topological information during pretraining facilitates the learning of stable, discriminative, and multi-scale protein representations, enhancing the expressive capacity of both modalities. An adaptive fusion module dynamically integrates structural and surface embeddings to yield complete and robust protein representations. PLiSAGE demonstrates superior performance over competitive baselines in binding affinity prediction and interaction classification tasks. Extensive ablation studies underscore the critical contributions of surface features and the pretraining strategy to the model’s generalization capabilities. Availability and implementation The source code of PLiSAGE is available at: https://github.com/catly/PLiSAGE.

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