LSA-DDI: learning stereochemistry-aware drug interactions via 3D feature fusion and contrastive cross-attention

Wang, Shanshan; Yang, Chen; Chen, Lirong

doi:10.3390/ijms26146799

Cited by 65Computational Drug Discovery MethodsMachine Learning in HealthcareMachine Learning in Bioinformatics

LSA-DDI: learning stereochemistry-aware drug interactions via 3D feature fusion and contrastive cross-attention

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Wang, Shanshan, Yang, Chen, Chen, Lirong·2025·International Journal of Molecular Sciences

DOI 10.3390/ijms26146799PMC PMC12294978

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Abstract

Accurate prediction of drug-drug interactions (DDIs) is essential for ensuring medication safety and optimizing combination-therapy strategies. However, existing DDI models face limitations in handling interactions related to stereochemistry and precisely locating drug interaction sites. These limitations reduce the prediction accuracy for conformation-dependent interactions and the interpretability of molecular mechanisms, potentially posing risks to clinical safety. To address these challenges, we introduce LSA-DDI, a Spatial-Contrastive-Attention-Based Drug-Drug Interaction framework. Our 3D feature extraction method captures the spatial structure of molecules through three features-coordinates, distances, and angles-and fuses them to enhance the model of molecular spatial structures. Concurrently, we design and implement a Dynamic Feature Exchange (DFE) mechanism that dynamically regulates the flow of information across modalities via an attention mechanism, achieving bidirectional enhancement and semantic alignment of 2D topological and 3D spatial structure features. Additionally, we incorporate a dynamic temperature-regulated multiscale contrastive learning framework that effectively aligns multiscale features and enhances the model's generalizability. Experiments conducted on public drug databases under both warm-start and cold-start scenarios demonstrated that LSA-DDI achieved competitive performance, with consistent improvements over existing methods.

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Multimodal systems have shown promise in delivering fairer and more equitable care by incorporating broader patient contexts, reducing disparities.
“Furthermore, multimodal systems have shown promise in delivering fairer and more equitable care, as they can incorporate broader patient contexts, reducing disparities that often arise when models are trained on limited or homogeneous data (10–12).”
From The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities (2026) · cites this paper as [11]
The Methods (Section 2.5) describe fusion of 2D and 3D molecular features, not broader patient contexts. The Conclusions (Section 4) recommend monitoring fairness across populations, but do not say multimodal systems deliver more equitable care or reduce disparities. Searches for patient contexts, disparities, and homogeneous data found no passage addressing that claim; page numbers were unavailable. (LLM, conf=0.76)
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Authors: Wang, S., Yang, C., & Chen, L.
Affiliations: Yan'an University (CN)
College of Computer Science, Inner Mongolia University, Hohhot 010021, China
Inner Mongolia University (CN)
Corresponding author: Chen, Lirong
ORCID: Wang, Shanshan
Chen, Lirong
Venue: International Journal of Molecular Sciences
Publisher: MDPI AG
Published: 2025-07-16
Volume: 26
Issue: 14
Pages: 6799
ISSN: 1422-0067 (ISSN-L)
DOI: 10.3390/ijms26146799
PMC: PMC12294978
OpenAlex: W4412725958
Cited by: 65
References: 30
Citations by year: 2026: 61 · 2025: 4
FWCI: 33.59
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: International Journal of Molecular Sciences (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: Inner Mongolia Natural Science Foundation Project “Research on Cross-border E-commerce Product ‘Localization’ Promotion Based on Large Language Models” (2025MS07016, YAU202507636)
the research project “Social Network Public Ideology Mining and Prediction Research” (2025MS07016, YAU202507636)
Topics: Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords: drug-drug interactions, stereochemistry, 3D molecular structure, cross-attention, contrastive learning, interaction site prediction, 2D-3D feature fusion, drug safety
MeSH terms: Algorithms, Drug Interactions, Humans, Stereoisomerism

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LSA-DDI: learning stereochemistry-aware drug interactions via 3D feature fusion and contrastive cross-attention

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