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Molecular representation learning is pivotal in predicting molecular properties and advancing drug design.
ZINC 15–ligand discovery for everyone
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Communicative representation learning on attributed molecular graphs
Song Y, Zheng S, Niu Z, Fu ZH, Lu Y, Yang Y · 2020
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Strategies For Pre-training Graph Neural Networks
Hu W, Liu B, Gomes J, Zitnik M, Liang P, Pande V, et al · 2020
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KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction
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Graph contrastive learning with augmentations
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MoCL: data-driven molecular fingerprint via knowledge-aware contrastive learning from molecular graph
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Drug–drug interaction prediction with learnable size-adaptive molecular substructures
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Learning size-adaptive molecular substructures for explainable drug–drug interaction prediction by substructure-aware graph neural network
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A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions
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Molecule attention transformer
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https://github.com/gnn4dr/DRKG/
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Strategies For Pre-training Graph Neural Networks
Hu W, Liu B, Gomes J, Zitnik M, Liang P, Pande V, et al · 2020
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Learning Attributed Graph Representation with Communicative Message Passing Transformer
Chen J, Zheng S, Song Y, Rao J, Yang Y · 2021
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MDNN: A Multimodal Deep Neural Network for Predicting Drug-Drug Interaction Events
Lyu T, Gao J, Tian L, Li Z, Zhang P, Zhang J · 2021
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Zero-shot Learning for Preclinical Drug Screening
Li K, Liu W, Luo Y, Cai X, Wu J, Hu W · 2024
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Contrastive Learning Drug Response Models from Natural Language Supervision
Li K, Gong X, Wu J, Hu W · 2024
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Mkg-fenn: A multimodal knowledge graph fused end-to-end neural network for accurate drug–drug interaction prediction
Wu D, Sun W, He Y, Chen Z, Luo X · 2024
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Molecular fragmentation as a crucial step in the AI-based drug development pathway
Jinsong S, Qifeng J, Xing C, Hao Y, Wang L · 2024
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Dual-Channel Learning Framework for Drug-Drug Interaction Prediction via Relation-Aware Heterogeneous Graph Transformer
Su X, Hu P, You ZH, Philip SY, Hu L · 2024
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