Author

Sachin Kadyan

Columbia UniversityORCID 0000-0002-6079-7627Unclaimed 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

Approach:deep learning· 2 Assay:protein structure· 2 Research Focus:protein structure prediction· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Research Focus:software / tool· 1 Approach:statistical model· 1

Frequent Co-Authors

Elena RivasiD· 2 shared Marcell SzikszaiiD· 2 shared Marcin MagnusiD· 2 shared Nazim BouattaiD· 2 shared Adegoke A. OjewoleiD· 1 shared Andrew M. WatkinsiD· 1 shared Arkadiusz NowaczynskiiD· 1 shared Bei WangiD· 1 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 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

2 shared papersBioinformatics

Elena RivasORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

On inputs to deep learning for RNA 3D structure prediction

Marcell Szikszai, Marcin Magnus, Sachin Kadyan, Elena Rivas

The paper identifies differences between protein and RNA structure and proposes RNA-specific methods to improve deep learning models for RNA 3D structure prediction.

bioRxiv · Feb 14, 2025 · PreprintBioinformatics

OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

Gustaf Ahdritz, Nazim Bouatta, Christina Floristean, Sachin Kadyan, Qinghui Xia, William Gerecke, Timothy J. O’Donnell, Daniel Berenberg, I. Fisk, Niccolò Zanichelli, Bo Zhang, Arkadiusz Nowaczynski, Bei Wang, Marta M. Stepniewska-Dziubinska, Shang Zhang, , Mohammed AlQuraishi

The paper trains a new version of AlphaFold2, OpenFold, and evaluates its generalization ability and learning mechanisms.

Nature Methods · May 14, 2024Peer-reviewedBioinformatics

RNA3DB: a structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction

Marcell Szikszai, Marcin Magnus, Siddhant Sanghi, Sachin Kadyan, Nazim Bouatta, Elena Rivas

The paper introduces RNA3DB, a dataset of structured RNAs designed for training and benchmarking deep learning models, with non-redundant sequence and structure splits to improve generalization.

Journal of Molecular Biology · Mar 27, 2024Peer-reviewedBioinformatics