11 citations · 12 across the 3 of their papers we have counts for
6 papers
OPRD-Imp: a dataset of experimentally observed impurities and side products in multi-step pharmaceutical syntheses
Heejeong Kim, Seonbin Kim, Jihwan Kim +6
Predicting reaction impurities is a critical yet persistently data-limited challenge in pharmaceutical process chemistry. While machine learning models for reaction outcome predict…
Wavenumber-Aware Deep Learning for Robust Analysis of Raman Spectra
Jihwan Kim, Jaeseong Heo, Yujin Kim +7
Machine-learning models for spectroscopy usually treat a spectrum as a fixed-grid intensity vector, leaving the physical coordinate implicit in the array index. Such models are tie…
Prediction of experimental excited-state absorption spectra by using vibronic transition calculations: Its practical application to a π-conjugated molecule
Joonyoung F. Joung, Sungnam Park
Accurate theoretical prediction of excited-state absorption (ESA) spectra remains challenging. Although considerable progress has been made in computational methods for ESA spectro…
Electron flow matching for generative reaction mechanism prediction obeying conservation laws
Joonyoung F. Joung, Mun Hong Fong, Nicholas Casetti +3
Central to our understanding of chemical reactivity is the principle of mass conservation, which is fundamental for ensuring physical consistency, balancing equations, and guiding…
ASKCOS: an open source software suite for synthesis planning
Zhengkai Tu, Sourabh J. Choure, Mun Hong Fong +16
The advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planni…
Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset
Joonyoung F. Joung, Mun Hong Fong, Jihye Roh +3
Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models…