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Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/jcc.1168openalex-snapshot
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inferredcites →https://doi.org/10.1002/qsar.200330822openalex-snapshot
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inferredcites →https://doi.org/10.1002/qsar.200410011openalex-snapshot
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inferredcites →https://doi.org/10.1007/3-540-49430-8openalex-snapshot
Neural Networks: Tricks of the Trade
inferredcites →https://doi.org/10.1007/978-0-387-21606-5openalex-snapshot
The Elements of Statistical Learning
inferredcites →https://doi.org/10.1007/978-1-4419-1428-6_5195openalex-snapshot
Pattern Classification
inferredcites →https://doi.org/10.1007/978-1-4612-0745-0openalex-snapshot
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inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/978-3-642-35289-8openalex-snapshot
Neural Networks: Tricks of the Trade
inferredcites →https://doi.org/10.1007/s00894-002-0101-1openalex-snapshot
ADME evaluation in drug discovery
inferredcites →https://doi.org/10.1016/0045-6535(95)00313-wopenalex-snapshot
Generator column determination of aqueous solubilities for non-ortho and mono-ortho substituted polychlorinated biphenyls
inferredcites →https://doi.org/10.1016/j.ejps.2004.04.006openalex-snapshot
Accuracy of calculated pH-dependent aqueous drug solubility
inferredcites →https://doi.org/10.1016/s0043-1354(02)00071-4openalex-snapshot
Aqueous solubilities of non-ortho and mono-ortho PCBs at four temperatures
inferredcites →https://doi.org/10.1016/s1359-6446(04)03365-3openalex-snapshot
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inferredcites →https://doi.org/10.1021/ci000392topenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci000459copenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci010287zopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci010289jopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci010323uopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci010363yopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci0202685openalex-snapshot
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inferredcites →https://doi.org/10.1021/ci025590uopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci034006uopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci034107sopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci0341161openalex-snapshot
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inferredcites →https://doi.org/10.1021/ci034184nopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci034243xopenalex-snapshot
ESOL: Estimating Aqueous Solubility Directly from Molecular Structure
inferredcites →https://doi.org/10.1021/ci034255iopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci040020aopenalex-snapshot
ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors [J. Chem. Inf. Comput. Sci. 43, 2137−2152 (2003)]
inferredcites →https://doi.org/10.1021/ci049737oopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci049744copenalex-snapshot
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inferredcites →https://doi.org/10.1023/a:1012284411691openalex-snapshot
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inferredcites →https://doi.org/10.1023/a:1025358319677openalex-snapshot
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inferredcites →https://doi.org/10.1023/b:jcam.0000030031.81235.05openalex-snapshot
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inferredcites →https://doi.org/10.1080/10629360108035385openalex-snapshot
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inferredcites →https://doi.org/10.1093/oso/9780198538493.001.0001openalex-snapshot
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inferredcites →https://doi.org/10.1109/72.914517openalex-snapshot
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inferredcites →https://doi.org/10.1109/tbme.2003.808827openalex-snapshot
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inferredcites →https://doi.org/10.1111/j.2517-6161.1978.tb01643.xopenalex-snapshot
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inferredcites →https://doi.org/10.1145/1102351.1102479openalex-snapshot
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inferredcites →https://doi.org/10.1208/aapsj080104openalex-snapshot
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inferredcites →https://doi.org/10.2174/092986706775197917openalex-snapshot
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inferredcites →https://doi.org/10.2174/1386207023330101openalex-snapshot
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inferredcites →https://doi.org/10.2533/000942903777678641openalex-snapshot
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inferredcites →https://doi.org/10.5860/choice.31-5500openalex-snapshot
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inferredcites →https://doi.org/10.7551/mitpress/1113.001.0001openalex-snapshot
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inferredcites →https://doi.org/10.7551/mitpress/3206.001.0001openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9781118819586.ch10openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9783527623860.ch4openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9783527645121.ch1openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9783527677016.ch1openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9783527802265.ch8openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9783527832767.ch21openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/cbdv.200900148openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/cmdc.200700041openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/cmdc.200900205openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/cmdc.200900469openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.201400122openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.201400144openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-0716-1787-8_15openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00726-008-0228-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00894-019-4120-6openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-007-9125-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-007-9160-9openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-008-9193-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-010-9401-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-023-00496-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-024-00576-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10853-019-03491-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.aca.2011.02.004openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.aichem.2024.100064openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.fuel.2020.117984openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.ijmst.2023.12.005openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.medidd.2024.100176openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/s0065-7743(07)42029-2openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/s0079-6468(10)49004-9openalex-snapshot
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inferred← citeshttps://doi.org/10.1017/cbo9780511730412.013openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.cgd.4c01451openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.chemrev.2c00141openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.iecr.1c02142openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00542openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acsomega.2c06702openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci100308fopenalex-snapshot
StructRank: A New Approach for Ligand-Based Virtual Screening
inferred← citeshttps://doi.org/10.1021/ci7000633openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci700142copenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci800151mopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci800329ropenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci9000794openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci900161gopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci900377eopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci900406xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/jm701407xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/mp070030+openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/mp0700413openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41557-019-0234-9openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41597-024-03105-6openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-025-12758-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1039/c5mh00282fopenalex-snapshot
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inferred← citeshttps://doi.org/10.1039/d0np00043dopenalex-snapshot
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inferred← citeshttps://doi.org/10.1039/d3dd00217aopenalex-snapshot
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inferred← citeshttps://doi.org/10.1063/1.2793398openalex-snapshot
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inferred← citeshttps://doi.org/10.1088/2632-2153/acac01openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bib/bbaa321openalex-snapshot
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inferred← citeshttps://doi.org/10.1137/1.9781611972795.26openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1752-153x-2-s1-p23openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1758-2946-2-2openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1758-2946-6-35openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s13321-021-00571-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1515/9783110559835-003openalex-snapshot
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inferred← citeshttps://doi.org/10.1517/17425250902753261openalex-snapshot
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inferred← citeshttps://doi.org/10.1609/aaai.v24i1.7494openalex-snapshot
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inferred← citeshttps://doi.org/10.21203/rs.3.rs-949300/v1openalex-snapshot
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inferred← citeshttps://doi.org/10.2147/ijhts.s9735openalex-snapshot
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inferred← citeshttps://doi.org/10.2298/jsc090809022eopenalex-snapshot
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inferred← citeshttps://doi.org/10.26434/chemrxiv-2023-2ncbfopenalex-snapshot
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inferred← citeshttps://doi.org/10.3390/ijms12021259openalex-snapshot
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inferred← citeshttps://doi.org/10.48550/arxiv.0912.1128openalex-snapshot
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inferred← citeshttps://doi.org/10.48550/arxiv.1908.03569openalex-snapshot
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inferred← citeshttps://doi.org/10.70675/00fff70azab21z46edz8364z60031c95c00fopenalex-snapshot
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