SP Technology (South Korea)

Institution (Company)

Homepage ROR

Also Known As

SP Technology (South Korea), SPTek, 에스피텍

214

Publications

4.9K

Citations

Authors

Concepts

About

SP Technology (South Korea) is a company institution in Seoul, South Korea known for engineering, medicine, and computational biology.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	11	50	3.1K
2	Medicine	8	37	2.8K
3	Natural Sciences	2	8	416
4	Health Sciences	1	1	12
5	Biology	1	4	164

SP Technology (South Korea)

214

Publications

4.9K

Citations

Page 1

	Year	Citations
Uni-Mol: A Universal 3D Molecular Representation Learning Framework Gengmo Zhou, Zhifeng Gao, Qiankun Ding, EngineeringMolecular Representation LearningMolecular PropertyComputational BiologyMolecular Biology	2023	233
Novel Radiochromic Films for Clinical Dosimetry W.L. McLaughlin, James M. Puhl, Mohamad Al‐Sheikhly, Radiation Protection Dosimetry	1996	92
Uni-Mol: A Universal 3D Molecular Representation Learning Framework Gengmo Zhou, Zhifeng Gao, Qiankun Ding, EngineeringMolecular BiologyComputational ChemistryMolecular GraphicMolecular Design	2022	76
A Methodology for Drop Performance Modeling and Application for Design Optimization of Chip-Scale Packages A. Syed, Seung Mo Kim, Wei Lin, IEEE Transactions on Electronics Packaging Manufacturing EngineeringComputer ArchitectureDrop Performance ModelingDynamic ResponseStructural Engineering	2007	65
Uni-Fold: An Open-Source Platform for Developing Protein Folding Models beyond AlphaFold Ziyao Li, Xuyang Liu, Weijie Chen, Protein AssemblyBiomolecular Structure PredictionStructural BioinformaticsMultimeric Protein ModelsMolecular Biology	2022	58
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials Jinzhe Zeng, Duo Zhang, Anyang Peng, Journal of Chemical Theory and Computation	2025	45
Triboelectric filtering for air purification Jianqing Liu, Tao Jiang, Xiaohui Li, Nanotechnology Materials ScienceAir FilterElectrical EngineeringChemical EngineeringEngineering	2019	44
A Flow Cytometric Method for Rapid Selection of Novel Industrial Yeast Hybrids Philip Bell, Daniel Deere, J. Shen, Applied and Environmental Microbiology EngineeringFungal Cell BiologyGeneticsRapid SelectionMolecular Genetics	1998	41
Uni-Mol: A Universal 3D Molecular Representation Learning Framework Gengmo Zhou, Zhifeng Gao, Qiankun Ding, EngineeringMolecular BiologyComputational ChemistryMolecular GraphicMolecular Design	2022	39
Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with ab initio accuracy Qiangqiang Gu, Zhanghao Zhouyin, Shishir Kumar Pandey, Nature Communications	2024	35

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