Extracting Material Property Measurement Data from Scientific Articles. - Gihan Panapitiya; Fred Parks; Jonathan Sepulveda; Emily Saldanha (EMNLP (1), 2021)

Predicting Aqueous Solubility of Organic Molecules Using Deep Learning Models with Varied Molecular Representations. - Gihan Panapitiya; Michael Girard; Aaron Hollas; Vijay Murugesan; Wei Wang; Emily Saldanha (CoRR, 2021)

Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction. - Carter Knutson; Gihan Panapitiya; Rohith Anand Varikoti; Neeraj Kumar (CoRR, 2022)

Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models. - Gihan Panapitiya; Emily Saldanha (CoRR, 2023)

Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction. - Tianzhixi Yin; Gihan Panapitiya; Elizabeth D. Coda; Emily Saldanha (J. Cheminformatics, 2023)

FragNet: A Graph Neural Network for Molecular Property Prediction with Four Layers of Interpretability. - Gihan Panapitiya; Peiyuan Gao; C. Mark Maupin; Emily Saldanha (CoRR, 2024)

Extracting Material Property Measurements from Scientific Literature with Limited Annotations. - Jessica Kong; Gihan Panapitiya; Emily Saldanha (J. Chem. Inf. Model., 2025)