How ImageDDI Boosts Drug‑Drug Interaction Prediction with Motif Sequences and Molecular Images

The ImageDDI framework, introduced by a team from Hunan University, combines molecular motif sequences with 2D/3D molecular images using a Transformer encoder and adaptive feature fusion, achieving significantly higher accuracy and macro‑F1 scores than existing methods on multiple DDI datasets, while also providing interpretable visual explanations.

Deep LearningDrug InteractionImage Fusion

0 likes · 10 min read

How ImageDDI Boosts Drug‑Drug Interaction Prediction with Motif Sequences and Molecular Images

DataFunTalk

May 16, 2023 · Artificial Intelligence

Multimodal Deep Neural Network for Predicting Drug‑Drug Interactions

The presentation introduces a multimodal deep neural network (MDNN) that integrates drug knowledge graphs and heterogeneous drug features to predict drug‑drug interactions, demonstrates state‑of‑the‑art performance on a large IJCAI‑2021 dataset, and discusses its architecture, evaluation, and future challenges.

Drug Interactionknowledge graphmultimodal neural network

0 likes · 12 min read

Multimodal Deep Neural Network for Predicting Drug‑Drug Interactions

DataFunSummit

Nov 26, 2021 · Artificial Intelligence

Graph Machine Learning for Molecular Networks: Challenges, Methods, and Applications in Biomedicine

This talk by a Stanford PhD student explores how graph neural networks can be adapted for molecular and biomedical networks, discusses the limitations of standard GNNs, introduces novel methods such as SkipGNN and G‑Meta, and demonstrates their use for drug‑drug interaction prediction, hypothesis generation, and treatment discovery with few‑shot learning.

Biomedical ApplicationsDrug InteractionMeta Learning

0 likes · 9 min read

Graph Machine Learning for Molecular Networks: Challenges, Methods, and Applications in Biomedicine