• Publications
  • Influence
Compound‐protein interaction prediction with end‐to‐end learning of neural networks for graphs and sequences
Motivation: In bioinformatics, machine learning‐based methods that predict the compound‐protein interactions (CPIs) play an important role in the virtual screening for drug discovery. Recently,Expand
  • 53
  • 9
Modeling and Learning Semantic Co-Compositionality through Prototype Projections and Neural Networks
We present a novel vector space model for semantic co-compositionality. Inspired by Generative Lexicon Theory (Pustejovsky, 1995), our goal is a compositional model where both predicate and argumentExpand
  • 22
  • 2
Mean-field theory of graph neural networks in graph partitioning
A theoretical performance analysis of the graph neural network (GNN) is presented. For classification tasks, the neural network approach has the advantage in terms of flexibility that it can beExpand
  • 15
Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks.
The discovery of molecules with specific properties is crucial to developing effective materials and useful drugs. Recently, to accelerate such discoveries with machine learning, deep neural networksExpand
  • 6
Quantitative estimation of properties from core-loss spectrum via neural network
Localized structures in nano- and sub-nano-scales strongly affect material properties. Thus, some spectroscopic techniques have been used to characterize local atomic and electronic structures. IfExpand
  • 4
Dual Convolutional Neural Network for Graph of Graphs Link Prediction
Graphs are general and powerful data representations which can model complex real-world phenomena, ranging from chemical compounds to social networks; however, effective feature extraction fromExpand
  • 4
Non-Linear Similarity Learning for Compositionality
Many NLP applications rely on the existence of similarity measures over text data. Although word vector space models provide good similarity measures between words, phrasal and sententialExpand
  • 6
Learning excited states from ground states by using an artificial neural network
Excited states are different quantum states from their ground states, and spectroscopy methods that can assess excited states are widely used in materials characterization. Understanding the spectraExpand