ArticleJournal of cheminformatics2022
MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks.Frontiers in pharmacology · 2026Pooled it
- Multi-GraphDDI: Multi-Feature Fusion and Interaction for Graph-Based Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- C2M-Mamba: drug-drug interaction prediction based on cross-modal cross-Mamba.BMC bioinformatics · 2026Article
- Transformer-based graphs for drug-drug interaction with chemical knowledge embedding.Briefings in bioinformatics · 2026Article
- Contrastive learning-based multi-mechanism disentangled assessment for drug-drug interaction.BMC bioinformatics · 2025Article
- RCAN-DDI: Relation-aware cross adversarial network for drug-drug interaction prediction.Journal of pharmaceutical analysis · 2025Article
- Disentangled contrastive learning with dynamic intent adaptation for unveiling gene-drug associations.Briefings in bioinformatics · 2025Article
- SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein-Ligand Interaction Modeling for METTL3.ACS omega · 2025Article
- Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges.Frontiers in pharmacology · 2025Review
- A substructure-aware graph neural network incorporating relation features for drug-drug interaction prediction.Quantitative biology (Beijing, China) · 2024Article
- Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.iScience · 2024Article
- DDI-Transform: A neural network for predicting drug-drug interaction events.Quantitative biology (Beijing, China) · 2024Article
- MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction.Molecules (Basel, Switzerland) · 2024Article
- Sequential Contrastive and Deep Learning Models to Identify Selective Butyrylcholinesterase Inhibitors.Journal of chemical information and modeling · 2024Article
- Prediction of multiple types of drug interactions based on multi-scale fusion and dual-view fusion.Frontiers in pharmacology · 2024Article
- MASMDDI: multi-layer adaptive soft-mask graph neural network for drug-drug interaction prediction.Frontiers in pharmacology · 2024Article
- DPSP: a multimodal deep learning framework for polypharmacy side effects prediction.Bioinformatics advances · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
The joint use of multiple drugs may cause unintended drug-drug interactions (DDIs) and result in adverse consequence to the patients. Accurate identification of DDI types can not only provide hints to avoid these accidental events, but also elaborate the underlying mechanisms by how DDIs occur. Several computational methods have been proposed for multi-type DDI prediction, but room remains for improvement in prediction performance. In this study, we propose a supervised contrastive learning based method, MDDI-SCL, implemented by three-level loss functions, to predict multi-type DDIs. MDDI-SCL is mainly composed of three modules: drug feature encoder and mean squared error loss module, drug latent feature fusion and supervised contrastive loss module, multi-type DDI prediction and classification loss module. The drug feature encoder and mean squared error loss module uses self-attention mechanism and autoencoder to learn drug-level latent features. The drug latent feature fusion and supervised contrastive loss module uses multi-scale feature fusion to learn drug pair-level latent features. The prediction and classification loss module predicts DDI types of each drug pair. We evaluate MDDI-SCL on three different tasks of two datasets. Experimental results demonstrate that MDDI-SCL achieves better or comparable performance as the state-of-the-art methods. Furthermore, the effectiveness of supervised contrastive learning is validated by ablation experiment, and the feasibility of MDDI-SCL is supported by case studies. The source codes are available at https://github.com/ShenggengLin/MDDI-SCL .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.