MMFi-DPBML: Multi-molecular fingerprint feature fusion for predicting ingredient-target interactions in traditional Chinese medicine.

Research Purpose: This study proposes MMFi-DPBML, a deep learning framework that in-tegrates multi-molecular fingerprint features for predicting ingredient-target interactions (ITIs) in traditional Chinese medicine (TCM). By capturing di-verse structural and physicochemical features, the model aims to enhance prediction accuracy and support TCM modernization through data-driven approaches.

Method: MMFi-DPBML fuses four types of molecular fingerprints-MACCS, ECFP4, Torsion, and 2D Pharmacophore-alongside 20 molecular descrip-tors, processed through a Deep Pyramid Convolutional Neural Network (DPCNN). Protein targets are represented by amino acid sequences and encoded using a stacked BiLSTM. A multilayer perceptron (MLP) inte-grates both feature spaces for final interaction prediction. A custom dataset (TCMTS) was constructed, and the model was evaluated against both inter-nal variants and public benchmarks (Davis, KIBA).

Results: Ablation studies confirmed the effectiveness of each component, with the full model achieving an AUC of 0.9873 and a weighted F1 score of 0.9860 on the TCMTS dataset. Comparative experiments demonstrated MMFi- DPBML's superior performance over state-of-the-art ITI models, showing robust generalization across diverse datasets. A case study on Scutellaria baicalensis further validated model predictions through molecular docking, with several predicted interactions corroborated by strong binding affinities and TCMSP annotations.

Conclusion: MMFi-DPBML effectively integrates structural, spatial, and interaction-relevant features to accurately predict compound-target interactions in TCM. Its high performance and generalization capability underscore its po-tential as a computational tool for drug discovery, especially in identifying bioactive ingredients within complex natural compounds. Future work will focus on enhancing interpretability and supporting multi-target pharmaco-logical modeling.