Vote for your favorite mineral in #MinCup25! - Taenite vs. Nontronite
It's a mineral found mostly in the farthest reaches of space vs one so common an exhaustive list of localities is nigh impossible as alloy #taenite faces off against clay #nontronite.
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Liu, Tao, Simine, Lena (2024) DeltaGzip: Computing Biopolymer–Ligand Binding Affinity via Kolmogorov Complexity and Lossless Compression. Journal of Chemical Information and Modeling, 64 (14) 5617-5623 doi:10.1021/acs.jcim.4c00461

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleDeltaGzip: Computing Biopolymer–Ligand Binding Affinity via Kolmogorov Complexity and Lossless Compression
JournalJournal of Chemical Information and Modeling
AuthorsLiu, TaoAuthor
Simine, LenaAuthor
Year2024 (July 22)Volume64
Page(s)5617-5623Issue14
PublisherAmerican Chemical Society (ACS)
DOIdoi:10.1021/acs.jcim.4c00461Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID17505986Long-form Identifiermindat:1:5:17505986:9
GUID0
Full ReferenceLiu, Tao, Simine, Lena (2024) DeltaGzip: Computing Biopolymer–Ligand Binding Affinity via Kolmogorov Complexity and Lossless Compression. Journal of Chemical Information and Modeling, 64 (14) 5617-5623 doi:10.1021/acs.jcim.4c00461
Plain TextLiu, Tao, Simine, Lena (2024) DeltaGzip: Computing Biopolymer–Ligand Binding Affinity via Kolmogorov Complexity and Lossless Compression. Journal of Chemical Information and Modeling, 64 (14) 5617-5623 doi:10.1021/acs.jcim.4c00461
In(2024, July) Journal of Chemical Information and Modeling Vol. 64 (14) American Chemical Society (ACS)

See Also

These are possibly similar items as determined by title/reference text matching only.

Zhang, Han, Im, Wonpil (2024) Ligand Binding Affinity Prediction for Membrane Proteins with Alchemical Free Energy Calculation Methods. Journal of Chemical Information and Modeling, 64 (14) 5671-5679 doi:10.1021/acs.jcim.4c00764
Kilgour, Michael, Liu, Tao, Walker, Brandon D., Ren, Pengyu, Simine, Lena (2021) E2EDNA: Simulation Protocol for DNA Aptamers with Ligands. Journal of Chemical Information and Modeling, 61 (9) 4139-4144 doi:10.1021/acs.jcim.1c00696
Luo, Ding, Liu, Dandan, Qu, Xiaoyang, Dong, Lina, Wang, Binju (2024) Enhancing Generalizability in Protein–Ligand Binding Affinity Prediction with Multimodal Contrastive Learning. Journal of Chemical Information and Modeling, 64 (6) 1892-1906 doi:10.1021/acs.jcim.3c01961
Du, Yu (2024) Binding Curve Viewer: Visualizing the Equilibrium and Kinetics of Protein–Ligand Binding and Competitive Binding. Journal of Chemical Information and Modeling, 64 (10) 4180-4192 doi:10.1021/acs.jcim.4c00130
Qu, Xiaoyang, Dong, Lina, Luo, Ding, Si, Yubing, Wang, Binju (2024) Water Network-Augmented Two-State Model for Protein–Ligand Binding Affinity Prediction. Journal of Chemical Information and Modeling, 64 (7) 2263-2274 doi:10.1021/acs.jcim.3c00567
Mqawass, Ghaith, Popov, Petr (2024) graphLambda: Fusion Graph Neural Networks for Binding Affinity Prediction. Journal of Chemical Information and Modeling, 64 (7) 2323-2330 doi:10.1021/acs.jcim.3c00771
Sriramulu, Dinesh Kumar, Lee, Sun-Gu (2020) Combinatorial Effect of Ligand and Ligand-Binding Site Hydrophobicities on Binding Affinity. Journal of Chemical Information and Modeling, 60 (3) 1678-1684 doi:10.1021/acs.jcim.9b01143
Gorantla, Rohan, Kubincová, Alžbeta, Suutari, Benjamin, Cossins, Benjamin P., Mey, Antonia S. J. S. (2024) Benchmarking Active Learning Protocols for Ligand-Binding Affinity Prediction. Journal of Chemical Information and Modeling, 64 (6) 1955-1965 doi:10.1021/acs.jcim.4c00220
Liu, Ran, Liu, Xiang, Wu, Jie (2023) Persistent Path-Spectral (PPS) Based Machine Learning for Protein–Ligand Binding Affinity Prediction. Journal of Chemical Information and Modeling, 63 (3) 1066-1075 doi:10.1021/acs.jcim.2c01251
Sabanés Zariquiey, Francesc, Galvelis, Raimondas, Gallicchio, Emilio, Chodera, John D., Markland, Thomas E., De Fabritiis, Gianni (2024) Enhancing Protein–Ligand Binding Affinity Predictions Using Neural Network Potentials. Journal of Chemical Information and Modeling, 64 (5) 1481-1485 doi:10.1021/acs.jcim.3c02031
Zhang, Lingrong; Liu, Taigang (2025) ATP-Pred: Prediction of Protein-ATP Binding Residues via Fusion of Residue-Level Embeddings and Kolmogorov–Arnold Network. Journal of Chemical Information and Modeling, 65 (7). doi:10.1021/acs.jcim.5c00016
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 15, 2025 19:27:05
Go to top of page