4 citations · 7 across the 4 of their papers we have counts for
5 papers
Predicting Drug Solubility Using Different Machine Learning Methods -- Linear Regression Model with Extracted Chemical Features vs Graph Convolutional Neural Network
John Ho, Zhao-Heng Yin, Colin Zhang +2
Predicting the solubility of given molecules remains crucial in the pharmaceutical industry. In this study, we revisited this extensively studied topic, leveraging the capabilities…
Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport
Kyle Swanson, Lili Yu, Tao Lei
Selecting input features of top relevance has become a popular method for building self-explaining models. In this work, we extend this selective rationalization approach to text m…
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
Lior Hirschfeld, Kyle Swanson, Kevin Yang +2
Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental…
Improving Molecular Design by Stochastic Iterative Target Augmentation
Kevin Yang, Wengong Jin, Kyle Swanson +2
Generative models in molecular design tend to be richly parameterized, data-hungry neural models, as they must create complex structured objects as outputs. Estimating such models…
Deep Learning for Automated Classification and Characterization of Amorphous Materials
Kirk Swanson, Shubhendu Trivedi, Joshua Lequieu +2
It is difficult to quantify structure-property relationships and to identify structural features of complex materials. The characterization of amorphous materials is especially cha…