Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions. - Juno Nam; Jurae Kim (CoRR, 2016)

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)

Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials. - Juno Nam; Rafael Gómez-Bombarelli (CoRR, 2024)

Understanding active learning of molecular docking and its applications. - Jeonghyeon Kim; Juno Nam; Seongok Ryu (CoRR, 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)

Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search. - Akshay Subramanian; James K. Damewood; Juno Nam; Kevin P. Greenman; Avni P. Singhal; Rafael Gómez-Bombarelli (CoRR, 2024)

Think while You Generate: Discrete Diffusion with Planned Denoising. - Sulin Liu; Juno Nam; Andrew Campbell; Hannes Stärk; Yilun Xu; Tommi S. Jaakkola; Rafael Gómez-Bombarelli (ICLR, 2025)

Transferable Learning of Reaction Pathways from Geometric Priors. - Juno Nam; Miguel Steiner; Max Misterka; Soojung Yang; Avni P. Singhal; Rafael Gómez-Bombarelli (CoRR, 2025)