Youngchun Kwon

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 8

Top Topics

Chemical Reactions

Predictive Modeling

Latent Variables

Message Passing

Top Venues

J. Cheminformatics

J. Chem. Inf. Model.

Publications

Jongmin Han, Youngchun Kwon, Youn-Suk Choi, Seokho Kang
Improving chemical reaction yield prediction using pre-trained graph neural networks. J. Cheminformatics 16 (1) (2024)
Youngchun Kwon, Sun Kim, Youn-Suk Choi, Seokho Kang
Generative Modeling to Predict Multiple Suitable Conditions for Chemical Reactions. J. Chem. Inf. Model. 62 (23) (2022)
Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Seokho Kang
Uncertainty-aware prediction of chemical reaction yields with graph neural networks. J. Cheminformatics 14 (1) (2022)
Eunji Kim, Dongseon Lee, Youngchun Kwon, Min Sik Park, Youn-Suk Choi
Valid, Plausible, and Diverse Retrosynthesis Using Tied Two-Way Transformers with Latent Variables. J. Chem. Inf. Model. 61 (1) (2021)
Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Kyoham Shin, Seokho Kang
Compressed graph representation for scalable molecular graph generation. J. Cheminformatics 12 (1) (2020)
Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Myeonginn Kang, Seokho Kang
Neural Message Passing for NMR Chemical Shift Prediction. J. Chem. Inf. Model. 60 (4) (2020)
Seokho Kang, Youngchun Kwon, Dongseon Lee, Youn-Suk Choi
Predictive Modeling of NMR Chemical Shifts without Using Atomic-Level Annotations. J. Chem. Inf. Model. 60 (8) (2020)
Youngchun Kwon, Jiho Yoo, Youn-Suk Choi, Wonjoon Song, Dongseon Lee, Seokho Kang
Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation. J. Cheminformatics 11 (1) (2019)