Author

Nathaniel Pascual

Michigan State UniversityORCID 0000-0002-6727-2014Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:benchmarking· 2 Research Focus:protein design· 2 Assay:protein sequence· 2 Approach:statistical model· 2 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1

Frequent Co-Authors

Daniel R. WoldringiD· 3 shared Mehrsa MardikoraemiD· 3 shared Joelle N. EavesiD· 2 shared Alexander Aljets· 1 shared Anton MahamaiD· 1 shared Benjamin J. OrlandoiD· 1 shared Bruno HagenbuchiD· 1 shared Erik M. ShapiroiD· 1 shared

Activity by Year

2023-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersBioinformatics

Daniel R. WoldringORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Predicting Inhibitors of OATP1B1 via Heterogeneous OATP-Ligand Interaction Graph Neural Network (HOLIgraph)

Mehrsa Mardikoraem, Joelle N. Eaves, Theodore Belecciu, Nathaniel Pascual, Alexander Aljets, Bruno Hagenbuch, Erik M. Shapiro, Benjamin J. Orlando, Daniel R. Woldring

This paper introduces HOLIgraph, a graph neural network model that predicts OATP1B1 inhibitors by integrating ligand features and protein-ligand interactions, achieving over 90% balanced accuracy.

Journal of Cheminformatics · May 5, 2025Peer-reviewedBioengineering

EvoSeq-ML: Advancing Data-Centric Machine Learning with Evolutionary-Informed Protein Sequence Representation and Generation

Mehrsa Mardikoraem, Nathaniel Pascual, Joelle N. Eaves, Shramana Chatterjee, Anton Mahama, Patrick Finneran, Robert P. Hausinger, Daniel Woldring

This paper evaluates an ancestral sequence reconstruction-informed data augmentation strategy for improving protein sequence generation and representation learning, showing improved thermostability in generated variants.

bioRxiv · Oct 2, 2024 · PreprintBioinformatics

AP-LASR: Automated Protein Libraries from Ancestral Sequence Reconstruction

James VanAntwerp, Mehrsa Mardikoraem, Nathaniel Pascual, Daniel Woldring

AP-LASR automates ancestral sequence reconstruction and generates diverse protein libraries by leveraging ASR uncertainty, improving eLnP scores over existing methods.

bioRxiv · Oct 9, 2023 · PreprintBioinformatics