no code implementations • EMNLP 2021 • Gihan Panapitiya, Fred Parks, Jonathan Sepulveda, Emily Saldanha
Machine learning-based prediction of material properties is often hampered by the lack of sufficiently large training data sets.
1 code implementation • 17 Jan 2023 • Gihan Panapitiya, Emily Saldanha
The ability to identify such domains enables the ability to find the confidence level of each prediction, to determine when and how the model should be employed depending on the prediction accuracy requirements of different tasks, and to improve the model for domains with high errors.
no code implementations • 20 Dec 2022 • Carter Knutson, Gihan Panapitiya, Rohith Varikoti, Neeraj Kumar
Neural Networks (GNNs) have revolutionized the molecular discovery to understand patterns and identify unknown features that can aid in predicting biophysical properties and protein-ligand interactions.
1 code implementation • 26 May 2021 • Gihan Panapitiya, Michael Girard, Aaron Hollas, Vijay Murugesan, Wei Wang, Emily Saldanha
Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications.