ComplexModeler

Wang, X., Zhu, H., Terashi, G., Taluja, M., & Kihara, D. (2024). DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model. Nature Methods, 21(12), 2307–2317.
https://doi.org/10.1038/s41592-024-02479-0

Wang, X., Terashi, G., & Kihara, D. (2023). CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning. Nature Methods, 20(11), 1739–1747.
https://doi.org/10.1038/s41592-023-02032-5


CryoREAD

Wang, X., Terashi, G., & Kihara, D. (2023). CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning. Nature Methods, 20(11), 1739–1747.
https://doi.org/10.1038/s41592-023-02032-5

CryoZeta

Zhang, Z., Li, S., Farheen, F., Kagaya, Y., Liu, B., Ibtehaz, N., Terashi, G., Nakamura, T., Zhu, H., Khan, K., Zhang, Y., & Kihara, D. (2026). Accurate macromolecular complex modeling for cryo-EM. bioRxiv.
https://doi.org/10.64898/2026.02.13.705846

DAQ Score

Terashi, G., Wang, X., Maddhuri Venkata Subramaniya, S. R., Tesmer, J. J., & Kihara, D. (2022). Residue-wise local quality estimation for protein models from cryo-EM maps. Nature Methods, 19(9), 1116–1125.
https://doi.org/10.1038/s41592-022-01574-4

DAQFinder

Terashi, G., Wang, X., Maddhuri Venkata Subramaniya, S. R., Tesmer, J. J., & Kihara, D. (2022). Residue-wise local quality estimation for protein models from cryo-EM maps. Nature Methods, 19(9), 1116–1125.
https://doi.org/10.1038/s41592-022-01574-4

DAQ-Refine

Terashi, G., Wang, X., & Kihara, D. (2023). Protein model refinement for cryo-EM maps using AlphaFold2 and the DAQ score. Acta Crystallographica Section D Structural Biology, 79(1), 10–21.
https://doi.org/10.1107/s2059798322011676

DeepMainMast

Terashi, G., Wang, X., Prasad, D. et al. (2024). DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction. Nature Methods, 21(1), 122–131.
https://doi.org/10.1038/s41592-023-02099-0

DiffModeler

Wang, X., Zhu, H., Terashi, G., Taluja, M., & Kihara, D. (2024). DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model. Nature Methods, 21(12), 2307–2317.
https://doi.org/10.1038/s41592-024-02479-0

DMcloud / DMcloudComplex

Terashi, G., Wang, X., Zhang, Y., Zhu, H., Park, J. H., & Kihara, D. (2026). DMcloud: Macromolecular structure modeling using local structure fitting for medium to low resolution cryo-EM maps. bioRxiv.
https://doi.org/10.64898/2026.06.12.731990

Emap2lig (Emap2lig-Find / Emap2lig-Build)

Li, S., Jain, A., Kagaya, Y., Park, J. H., & Kihara, D. (2026). Direct Detection and Atomic Modeling of Ligands in Cryo-EM Maps Using Deep Learning. bioRxiv.
https://doi.org/10.64898/2026.06.01.729423

Emap2sec

Maddhuri Venkata Subramaniya, S. R., Terashi, G., & Kihara, D. (2019). Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning. Nature Methods, 16(9), 911–917.
https://doi.org/10.1038/s41592-019-0500-1

Emap2sec+

Wang, X., Alnabati, E., Aderinwale, T. W., Subramaniya, S. R. M. V., Terashi, G., & Kihara, D. (2021). Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning. Nature Communications, 12(1), 2302.
https://doi.org/10.1038/s41467-021-22577-3

MainMast

Terashi, G., & Kihara, D. (2018). De novo main-chain modeling for EM maps using MAINMAST. Nature Communications, 9(1), 1618.
https://doi.org/10.1038/s41467-018-04053-7

VESPER

Han, X., Terashi, G., Christoffer, C., Chen, S., & Kihara, D. (2021). VESPER: global and local cryo-EM map alignment using local density vectors. Nature Communications, 12(1), 2090.
https://doi.org/10.1038/s41467-021-22401-y