arXiv AI June 9, 2026 By Juergen Dietrich

Pharmacogenomic Knowledge Graph Augmentation for Graph Neural Network-Based Drug-Drug Interaction Prediction

arXiv:2606. 07698v1 Announce Type: cross Abstract: Graph neural networks (GNNs) applied to drug-drug interaction (DDI) prediction rely exclusively on molecular structure encoded as SMILES-derived graphs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 9

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions

arXiv:2607. 07611v1 Announce Type: new Abstract: Background: Graph neural networks improve computational prediction of polypharmacy side effects, but standard binary cross-entropy training allocates equal capacity to well-classified and difficult examples, potentially missing clinically significant interactions.

By Faranak Hatami, Mousa Moradi

rag

arXiv Machine Learning

Jun 16

A polarity-aware multi-relational model for the signed interaction prediction in biological networks

arXiv:2407. 07357v3 Announce Type: replace Abstract: Predicting signed interactions in biological networks is crucial for understanding drug mechanisms and facilitating drug repurposing.

By Ziye Zhou, Meijie Wang, Lun Yu

arXiv Machine Learning

Jul 28

MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning

arXiv:2607. 24314v1 Announce Type: new Abstract: Predicting the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small molecules remains a major challenge in drug discovery.

By Tinghui Jin, Kedu Jin, Ying Li, Guanghui Ren, Jingzhi Xue, Shiyu Zhou, Xiaoli Dai, Li-bin Wei, Xijing Chen, Di Zhao, Jinfeng Liu

benchmarks safety

arXiv Machine Learning

Jul 7

CoFEND: A Cross-Modal Fusion End-to-End Network for Cold-Start Drug-Drug Interaction Prediction

arXiv:2607. 02928v1 Announce Type: new Abstract: Cold-start drug-drug interaction (DDI) prediction for new drugs is critical for minimizing unexpected adverse drug reactions.

By Di Wu, Hongyi Sun, Haichao Xu, Jia Chen, Zhong Chen, Jie Yang

multimodal safety

arXiv Machine Learning

3d ago

Benchmarking graph-based models for in-silico toxicity prediction in drug discovery

arXiv:2609.37555v1 Announce Type: new Abstract: Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical st...

By Noel Suarez-Barro, Manuel Lama, Juan C. Vidal

benchmarks safety

arXiv Machine Learning

Jul 3

An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility

arXiv:2607. 02212v1 Announce Type: cross Abstract: Aqueous solubility is a key property in early-stage drug discovery, but most predictive models merge physicochemical descriptors and molecular graph information into a single representation, obscuring whether a prediction is driven by global chemistry, molecular structure, or both.

By Sampreeti Bhattacharya, Arkaprava Roy

rag fine-tuning

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Related stories

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions

A polarity-aware multi-relational model for the signed interaction prediction in biological networks

MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning

CoFEND: A Cross-Modal Fusion End-to-End Network for Cold-Start Drug-Drug Interaction Prediction

Benchmarking graph-based models for in-silico toxicity prediction in drug discovery

An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility