Dynamic Prediction of Drug-Target Interactions via Cross-Modal Feature Mapping with Learnable Association Information.

Abstract

Predicting drug-target interactions (DTIs) is essential for advancing drug discovery and personalized medicine. However, accurately capturing the intricate binding relationships between drugs and targets remains a significant challenge, particularly when attempting to fully leverage the vast correlation information inherent in molecular data. This complexity is further exacerbated by the structural differences and sequence length disparities between drug molecules and protein targets, which can hinder effective feature alignment and interaction modeling. To address these challenges, we propose a model named LAM-DTI. First, drug and target features are extracted from the original molecular sequence data using a multilayer convolutional neural network. To address the sequence length discrepancy between drug and target features, we apply a connectionist temporal classification module to generate normalized feature sequences. Building on this, we introduce a learnable association information matrix as a flexible intermediary, which dynamically adjusts to capture accurate DTI association information, thereby enhancing cross-modal mapping within a unified latent space. This progressive mapping strategy enables the model to form an interaction projection between drugs and targets, effectively identifying critical interaction regions and guiding the capture of complex interaction-related features. Extensive experiments on three well-known benchmark data sets demonstrate that LAM-DTI significantly outperforms previous models.