Author

Andrew C. Powers

University of OregonORCID 0009-0002-0935-6444Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Approach:contrastive learning· 1 Research Focus:drug response / drug discovery· 1 Research Focus:protein folding & dynamics· 1 Assay:protein sequence· 1 Assay:protein structure· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Parisa HosseinzadehiD· 2 shared P. Douglas RenfrewiD· 1 shared Vikram Khipple MulliganiD· 1 shared Yanapat Janthana· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

4 top collaborators · 4 mutual co-author links

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2 shared papersBiochemistry

Parisa HosseinzadehORCID

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2 papers

Recent·Most cited

BiochemistryDigital DiscoveryPeer-reviewedJan 1, 2026

CyclicMPNN: Stable Cyclic Peptide Sequence Generation

Andrew C. Powers, Yanapat Janthana, Parisa Hosseinzadeh

The paper introduces CyclicMPNN, a model that generates energetically stable cyclic peptide sequences by fine-tuning ProteinMPNN with structural and in silico data, improving success rates in folding into generated backbones.

4 citationstop 3%

protein sequencedeep learning

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BioengineeringbioRxivPreprintFeb 21, 2025

CyclicCAE: A Conformational Autoencoder for Efficient Heterochiral Macrocyclic Conformational Sampling

Andrew C. Powers, P. Douglas Renfrew, Parisa Hosseinzadeh, Vikram Khipple Mulligan

CyclicCAE generates energetically favorable macrocycle conformations faster than Rosetta's GeneralizedKIC, using synthetic data and enabling design and structure prediction.

3 citations

protein structuredeep learning

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