activity
20162022
most citedDeeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network

79 citations · 149 across the 11 of their papers we have counts for

collaborators
Showing 2020Show all

2 papers · 1 filter

cs.LG2020★ 4 cited

A benchmark study on reliable molecular supervised learning via Bayesian learning

Doyeong Hwang, Grace Lee, Hanseok Jo +2

Virtual screening aims to find desirable compounds from chemical library by using computational methods. For this purpose with machine learning, model outputs that can be interpret…

cs.LG2020★ 4 cited

A comprehensive study on the prediction reliability of graph neural networks for virtual screening

Soojung Yang, Kyung Hoon Lee, Seongok Ryu

Prediction models based on deep neural networks are increasingly gaining attention for fast and accurate virtual screening systems. For decision makings in virtual screening, resea…