20 citations · 49 across the 7 of their papers we have counts for
10 papers
Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning
Seongok Ryu, Sumin Lee
In drug discovery, aqueous solubility is an important pharmacokinetic property which affects absorption and assay availability of drug. Thus, in silico prediction of solubility has…
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation
Soojung Yang, Doyeong Hwang, Seul Lee +2
Recently, utilizing reinforcement learning (RL) to generate molecules with desired properties has been highlighted as a promising strategy for drug design. A molecular docking prog…
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…
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…
Molecular Generative Model Based On Adversarially Regularized Autoencoder
Seung Hwan Hong, Jaechang Lim, Seongok Ryu +1
Deep generative models are attracting great attention as a new promising approach for molecular design. All models reported so far are based on either variational autoencoder (VAE)…
Uncertainty quantification of molecular property prediction using Bayesian neural network models
Seongok Ryu, Yongchan Kwon, Woo Youn Kim
In chemistry, deep neural network models have been increasingly utilized in a variety of applications such as molecular property predictions, novel molecule designs, and planning c…