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In Silico Prediction of Aqueous Solubility: A Multimodel Protocol Based on Chemical Similarity

https://doi.org/10.1021/mp300234q
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/1520-6017(200102)90:2<234::aid-jps14>3.0.co;2-vopenalex-snapshot
Estimation of the aqueous solubility I: Application to organic nonelectrolytes
inferredcites →https://doi.org/10.1002/cbdv.200900056openalex-snapshot
Improving Compound Quality through in vitro and in silico Physicochemical Profiling
inferredcites →https://doi.org/10.1002/jps.2600690814openalex-snapshot
Solubility and partitioning I: Solubility of nonelectrolytes in water
inferredcites →https://doi.org/10.1002/qsar.200390007openalex-snapshot
The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
inferredcites →https://doi.org/10.1002/qsar.200390020openalex-snapshot
Estimation of Aqueous Solubility By The General Solubility Equation (GSE) The Easy Way
inferredcites →https://doi.org/10.1016/j.bmcl.2010.10.068openalex-snapshot
Experimental solubility profiling of marketed CNS drugs, exploring solubility limit of CNS discovery candidate
inferredcites →https://doi.org/10.1016/j.drudis.2006.03.004openalex-snapshot
Biological assay challenges from compound solubility: strategies for bioassay optimization
inferredcites →https://doi.org/10.1016/s0169-409x(00)00129-0openalex-snapshot
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1
inferredcites →https://doi.org/10.1016/s0169-409x(02)00008-xopenalex-snapshot
Prediction of drug solubility from structure
inferredcites →https://doi.org/10.1016/s0169-409x(96)00423-1openalex-snapshot
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
inferredcites →https://doi.org/10.1016/s0928-0987(00)00162-7openalex-snapshot
VolSurf: a new tool for the pharmacokinetic optimization of lead compounds
inferredcites →https://doi.org/10.1016/s1359-6446(04)03365-3openalex-snapshot
Predicting aqueous solubility from structure
inferredcites →https://doi.org/10.1021/ac048767nopenalex-snapshot
Chasing Equilibrium: Measuring the Intrinsic Solubility of Weak Acids and Bases
inferredcites →https://doi.org/10.1021/ci00009a013openalex-snapshot
Estimation of aqueous solubility of organic molecules by the group contribution approach. Application to the study of biodegradation
inferredcites →https://doi.org/10.1021/ci000152dopenalex-snapshot
Estimation of the Aqueous Solubility of Organic Molecules by the Group Contribution Approach
inferredcites →https://doi.org/10.1021/ci000338copenalex-snapshot
Prediction of Drug Solubility by the General Solubility Equation (GSE)
inferredcites →https://doi.org/10.1021/ci000392topenalex-snapshot
Estimation of Aqueous Solubility of Chemical Compounds Using E-State Indices
inferredcites →https://doi.org/10.1021/ci010035yopenalex-snapshot
Prediction of Aqueous Solubility of Heteroatom-Containing Organic Compounds from Molecular Structure
inferredcites →https://doi.org/10.1021/ci0102822openalex-snapshot
Estimating the Water Solubilities of Crystalline Compounds from Their Chemical Structures Alone
inferredcites →https://doi.org/10.1021/ci020279yopenalex-snapshot
Modeling Aqueous Solubility
inferredcites →https://doi.org/10.1021/ci025515jopenalex-snapshot
Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program
inferredcites →https://doi.org/10.1021/ci034184nopenalex-snapshot
ADME Evaluation in Drug Discovery. 4. Prediction of Aqueous Solubility Based on Atom Contribution Approach
inferredcites →https://doi.org/10.1021/ci034243xopenalex-snapshot
ESOL: Estimating Aqueous Solubility Directly from Molecular Structure
inferredcites →https://doi.org/10.1021/ci0503762openalex-snapshot
Model Selection Based on Structural Similarity−Method Description and Application to Water Solubility Prediction
inferredcites →https://doi.org/10.1021/ci060164kopenalex-snapshot
Random Forest Models To Predict Aqueous Solubility
inferredcites →https://doi.org/10.1021/ci200387copenalex-snapshot
Revisiting the General Solubility Equation: In Silico Prediction of Aqueous Solubility Incorporating the Effect of Topographical Polar Surface Area
inferredcites →https://doi.org/10.1021/ci700096ropenalex-snapshot
Development of Reliable Aqueous Solubility Models and Their Application in Druglike Analysis
inferredcites →https://doi.org/10.1021/ci800058vopenalex-snapshot
Solubility Challenge: Can You Predict Solubilities of 32 Molecules Using a Database of 100 Reliable Measurements?
inferredcites →https://doi.org/10.1021/ci800436copenalex-snapshot
Findings of the Challenge To Predict Aqueous Solubility
inferredcites →https://doi.org/10.1021/ci900286sopenalex-snapshot
In Silico Prediction of Aqueous Solubility: The Solubility Challenge
inferredcites →https://doi.org/10.1021/ci960151eopenalex-snapshot
Clustering of Large Databases of Compounds: Using the MDL “Keys” as Structural Descriptors
inferredcites →https://doi.org/10.1021/ci970100xopenalex-snapshot
Aqueous Solubility Prediction of Drugs Based on Molecular Topology and Neural Network Modeling
inferredcites →https://doi.org/10.1021/jm020266bopenalex-snapshot
Prediction of Aqueous Solubility of a Diverse Set of Compounds Using Quantitative Structure−Property Relationships
inferredcites →https://doi.org/10.1021/jm101356popenalex-snapshot
Improvement in Aqueous Solubility in Small Molecule Drug Discovery Programs by Disruption of Molecular Planarity and Symmetry
inferredcites →https://doi.org/10.1021/jm701587dopenalex-snapshot
Molecular Characteristics for Solid-State Limited Solubility
inferredcites →https://doi.org/10.1021/mp0700155openalex-snapshot
On-the-Fly Selection of a Training Set for Aqueous Solubility Prediction
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1093/bioinformatics/btr333openalex-snapshot
The FAF-Drugs2 server: a multistep engine to prepare electronic chemical compound collections
inferredcites →https://doi.org/10.1517/17425255.4.6.759openalex-snapshot
Structure – ADME relationship: still a long way to go?
inferredcites →https://doi.org/10.2174/138620711795508331openalex-snapshot
Recent Advances on Aqueous Solubility Prediction

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/9781119769644.ch5openalex-snapshot
Preformulation and Physicochemical Characterization Underpinning the Development of Controlled Drug Delivery Systems
inferred← citeshttps://doi.org/10.1002/minf.201400119openalex-snapshot
Quantitative Regression Models for the Prediction of Chemical Properties by an Efficient Workflow
inferred← citeshttps://doi.org/10.1002/minf.201400144openalex-snapshot
In silico Prediction of Aqueous Solubility: a Comparative Study of Local and Global Predictive Models
inferred← citeshttps://doi.org/10.1007/978-3-030-73317-9_111-1openalex-snapshot
Physicochemical Properties of Drugs for Use in the Pharmaceutical Industry
inferred← citeshttps://doi.org/10.1007/978-3-030-73909-6_58openalex-snapshot
Application of In Silico Methods in Pharmacokinetic Studies During Drug Development
inferred← citeshttps://doi.org/10.1007/978-3-031-35529-5_111openalex-snapshot
Physicochemical Properties of Drugs for Use in the Pharmaceutical Industry
inferred← citeshttps://doi.org/10.1007/s11030-022-10465-xopenalex-snapshot
Multi-channel GCN ensembled machine learning model for molecular aqueous solubility prediction on a clean dataset
inferred← citeshttps://doi.org/10.1007/s11095-023-03597-8openalex-snapshot
Physiological Dynamics in the Upper Gastrointestinal Tract and the Development of Gastrointestinal Absorption Models for the Immediate-Release Oral Dosage Forms in Healthy Adult Human
inferred← citeshttps://doi.org/10.1016/b978-0-08-100295-7.00009-8openalex-snapshot
Prediction of perception using structure–activity models
inferred← citeshttps://doi.org/10.1016/j.aichem.2024.100064openalex-snapshot
RedPred, a machine learning model for the prediction of redox reaction energies of the aqueous organic electrolytes
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2018.11.025openalex-snapshot
Isolation and in silico prediction of potential drug-like compounds from Anethum sowa L. root extracts targeted towards cancer therapy
inferred← citeshttps://doi.org/10.1016/j.drudis.2016.02.005openalex-snapshot
Quantifying the chameleonic properties of macrocycles and other high-molecular-weight drugs
inferred← citeshttps://doi.org/10.1016/j.ijpharm.2018.12.087openalex-snapshot
In silico predictions of tablet density using a quantitative structure–property relationship model
inferred← citeshttps://doi.org/10.1016/j.isci.2020.101961openalex-snapshot
Pushing the limits of solubility prediction via quality-oriented data selection
inferred← citeshttps://doi.org/10.1016/j.microc.2026.119486openalex-snapshot
Prediction of aqueous solubility for neutral organic compounds using micellar electrokinetic chromatography retention and Abraham solvation model descriptors
inferred← citeshttps://doi.org/10.1016/j.phytochem.2019.03.014openalex-snapshot
Synthesis of potent neuroprotective butenolides based on plant smoke derived 3,4,5-Trimethylfuran-2(5H)-one and 3-methyl-2H-furo[2,3-c]pyrone-2-one
inferred← citeshttps://doi.org/10.1021/acs.chemrev.5c00332openalex-snapshot
Solubility Challenges in Battery Electrolytes
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00203openalex-snapshot
SCUBIDOO: A Large yet Screenable and Easily Searchable Database of Computationally Created Chemical Compounds Optimized toward High Likelihood of Synthetic Tractability
inferred← citeshttps://doi.org/10.1021/acsomega.2c03885openalex-snapshot
Prediction of the Aqueous Solubility of Compounds Based on Light Gradient Boosting Machines with Molecular Fingerprints and the Cuckoo Search Algorithm
inferred← citeshttps://doi.org/10.1021/acsomega.2c06702openalex-snapshot
Attention-Based Graph Neural Network for Molecular Solubility Prediction
inferred← citeshttps://doi.org/10.1021/jacs.5c07548openalex-snapshot
Data Science-Guided Development of Deoxyfluorination Reagents with Enhanced Reactivity, Practicality, and Safety
inferred← citeshttps://doi.org/10.1038/s41597-024-03105-6openalex-snapshot
Will we ever be able to accurately predict solubility?
inferred← citeshttps://doi.org/10.1038/srep46277openalex-snapshot
Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors
inferred← citeshttps://doi.org/10.1063/5.0154392openalex-snapshot
Statistic significant feature importance regressor based estimation of compound aqueous solubility
inferred← citeshttps://doi.org/10.1080/1062936x.2017.1368704openalex-snapshot
Six global and local QSPR models of aqueous solubility at pH = 7.4 based on structural similarity and physicochemical descriptors
inferred← citeshttps://doi.org/10.1080/17425255.2017.1389897openalex-snapshot
In silicoADME-Tox modeling: progress and prospects
inferred← citeshttps://doi.org/10.1093/nar/gkv353openalex-snapshot
FAF-Drugs3: a web server for compound property calculation and chemical library design
inferred← citeshttps://doi.org/10.1515/chem-2023-0188openalex-snapshot
Computational study of ADME-Tox prediction of selected phytochemicals from Punica granatum peels
inferred← citeshttps://doi.org/10.1517/17460441.2015.1043885openalex-snapshot
In silicotools used for compound selection during target-based drug discovery and development
inferred← citeshttps://doi.org/10.18097/bmcrm00060openalex-snapshot
Hydrogen Bond Contribution to Drug Bioavailability: cheminformatics approach
inferred← citeshttps://doi.org/10.21323/2618-9771-2024-7-2-312-320openalex-snapshot
QSAR of acyl alizarin red biocompound derivatives of Rubia tinctorum roots and its ADMET properties as anti-breast cancer candidates against MMP-9 protein receptor: In Silico study
inferred← citeshttps://doi.org/10.2174/1389557518666180727164417openalex-snapshot
Aqueous Drug Solubility: What Do We Measure, Calculate and QSPR Predict?
inferred← citeshttps://doi.org/10.26434/chemrxiv-2023-2ncbfopenalex-snapshot
RedPred, a machine learning model for the prediction of redox reaction energies of the aqueous organic electrolytes
inferred← citeshttps://doi.org/10.3389/fonc.2020.00121openalex-snapshot
Improved Prediction of Aqueous Solubility of Novel Compounds by Going Deeper With Deep Learning
inferred← citeshttps://doi.org/10.3390/batteries9010024openalex-snapshot
High-Throughput Virtual Screening of Quinones for Aqueous Redox Flow Batteries: Status and Perspectives
inferred← citeshttps://doi.org/10.3390/ijms20184648openalex-snapshot
A Free Web-Based Protocol to Assist Structure-Based Virtual Screening Experiments
inferred← citeshttps://doi.org/10.3390/ph15091120openalex-snapshot
Combined Ramipril and Black Seed Oil Dosage Forms Using Bioactive Self-Nanoemulsifying Drug Delivery Systems (BIO-SNEDDSs)
inferred← citeshttps://doi.org/10.5599/admet.852openalex-snapshot
ADME Prediction with KNIME: In silico aqueous solubility models based on supervised recursive machine learning approaches
inferred← citeshttps://doi.org/10.5772/intechopen.86174openalex-snapshot
ADME Profiling in Drug Discovery and a New Path Paved on Silica
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