Soojung Yang

According to our database, Soojung Yang authored at least 11 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

A Priori Sampling of Transition States with Guided Diffusion.

Hyukjun Lim, Soojung Yang, Lucas Pinède, Miguel Steiner, Yuanqi Du, Rafael Gómez-Bombarelli

CoRR, March, 2026

2025

Universally Converging Representations of Matter Across Scientific Foundation Models.

Sathya Edamadaka, Soojung Yang, Ju Li, Rafael Gómez-Bombarelli

CoRR, December, 2025

Learning Collective Variables from Time-lagged Generation.

Seonghyun Park, Kiyoung Seong, Soojung Yang, Rafael Gómez-Bombarelli, Sungsoo Ahn

CoRR, July, 2025

Transferable Learning of Reaction Pathways from Geometric Priors.

Juno Nam, Miguel Steiner, Max Misterka, Soojung Yang, Avni P. Singhal, Rafael Gómez-Bombarelli

CoRR, April, 2025

2024

Flow Matching for Accelerated Simulation of Atomic Transport in Materials.

Juno Nam, Sulin Liu, Gavin Winter, KyuJung Jun, Soojung Yang, Rafael Gómez-Bombarelli

CoRR, 2024

Learning Collective Variables for Protein Folding with Labeled Data Augmentation through Geodesic Interpolation.

Soojung Yang, Juno Nam, Johannes C. B. Dietschreit, Rafael Gómez-Bombarelli

CoRR, 2024

2023

Chemically Transferable Generative Backmapping of Coarse-Grained Proteins.

Soojung Yang, Rafael Gómez-Bombarelli

Proceedings of the International Conference on Machine Learning, 2023

2021

Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation.

Soojung Yang, Doyeong Hwang, Seul Lee, Seongok Ryu, Sung Ju Hwang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks.

Doyeong Hwang, Soojung Yang, Yongchan Kwon, Kyung-Hoon Lee, Grace Lee, Hanseok Jo, Seyeol Yoon, Seongok Ryu

J. Chem. Inf. Model., 2020

PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions.

Seokhyun Moon, Wonho Zhung, Soojung Yang, Jaechang Lim, Woo Youn Kim

CoRR, 2020

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

Soojung Yang, Kyung-Hoon Lee, Seongok Ryu

CoRR, 2020