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Estimation of Aqueous Solubility for a Diverse Set of Organic Compounds Based on Molecular Topology

https://doi.org/10.1021/ci9901338
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Molecular Connectivity in Structure–activity Analysis
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A new method for the estimation of the aqueous solubility of organic compounds
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AQUAFAC 3: aqueous functional group activity coefficients; application to the estimation of aqueous solubility
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Group contribution methods to estimate water solubility of organic chemicals
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Aqueous functional group activity coefficients (AQUAFAC) 4: Applications to complex organic compounds
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Prediction of aqueous solubility and octanol—water partition coefficient for pesticides based on their molecular structure
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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
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Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
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A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases
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Estimation of aqueous solubility of organic molecules by the group contribution approach. Application to the study of biodegradation
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Prediction of Aqueous Solubility of Organic Compounds
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Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information
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Molecular Modeling in the Discovery of Drug Leads
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Prediction of Aqueous Solubility for a Diverse Set of Heteroatom-Containing Organic Compounds Using a Quantitative Structure−Property Relationship
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Boiling Point and Critical Temperature of a Heterogeneous Data Set: QSAR with Atom Type Electrotopological State Indices Using Artificial Neural Networks
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Correlation of the Aqueous Solubility of Hydrocarbons and Halogenated Hydrocarbons with Molecular Structure
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Aqueous Solubility Prediction of Drugs Based on Molecular Topology and Neural Network Modeling
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Prediction of Aqueous Solubility of Organic Compounds from Molecular Structure
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Identification of Biological Activity Profiles Using Substructural Analysis and Genetic Algorithms
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Prediction of aqueous solubility of organic chemicals based on molecular structure
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Use of electrothermal vaporization-multiple-wavelength absorption spectrometry to qualitatively screen for the presence of polycyclic aromatic hydrocarbons
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Neural network studies. 1. Estimation of the aqueous solubility of organic compounds
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Computational studies on FK506: conformational search and molecular dynamics simulation in water
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Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors
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Neural Network Modeling for Estimation of the Aqueous Solubility of Structurally Related Drugs
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Prediction of partition coefficient based on atom‐type electrotopological state indices
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Designing Chemical Libraries for Lead Discovery
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Aqueous Solubility: Methods of Estimation for Organic Compounds

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Chapter 29 Computational Models for ADME
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Physics-Based Solubility Prediction for Organic Molecules
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Findings of the Second Challenge to Predict Aqueous Solubility
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Graph-Based Feature Selection Approach for Molecular Activity Prediction
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Fast Prediction of Lipophilicity of Organofluorine Molecules: Deep Learning-Derived Polarity Characters and Experimental Tests
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00792openalex-snapshot
GGAS2SN: Gated Graph and SmilesToSeq Network for Solubility Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c02399openalex-snapshot
Advancing Aqueous Solubility Prediction: A Machine Learning Approach for Organic Compounds Using a Curated Data Set
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00143openalex-snapshot
Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00555openalex-snapshot
Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c03055openalex-snapshot
Integration of DOPtools and CADS in a Web-Based User Interface for Structural Descriptor Calculation, Model Optimization, and Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00332openalex-snapshot
MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00625openalex-snapshot
In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01182openalex-snapshot
Data-Driven Critical Evaluation of the General Solubility Equation
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00054openalex-snapshot
Conformal Regression for Quantitative Structure–Activity Relationship Modeling—Quantifying Prediction Uncertainty
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00114openalex-snapshot
General Approach to Estimate Error Bars for Quantitative Structure–Activity Relationship Predictions of Molecular Activity
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Graph Convolutional Neural Networks as “General-Purpose” Property Predictors: The Universality and Limits of Applicability
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Neural Mulliken Analysis: Molecular Graphs from Density Matrices for QSPR on Raw Quantum-Chemical Data
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.6c00049openalex-snapshot
In Silico ADMET: From Current Practices to Novel Profilers
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Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets
inferred← citeshttps://doi.org/10.1021/acschembio.6c00120openalex-snapshot
A Novel Covalent Inhibitor Fragment for the SARS-CoV-2 Main Protease Identified by Target-Specific Deep Learning
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Pruned Machine Learning Models to Predict Aqueous Solubility
inferred← citeshttps://doi.org/10.1021/acsomega.2c00642openalex-snapshot
Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction
inferred← citeshttps://doi.org/10.1021/acsomega.2c00697openalex-snapshot
Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks
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.4c06163openalex-snapshot
Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility
inferred← citeshttps://doi.org/10.1021/acsomega.5c13630openalex-snapshot
Deciphering Molecular and Solvent Effects on Aqueous and Organic Solubility through Interpretable Machine Learning Approaches
inferred← citeshttps://doi.org/10.1021/bk-2016-1222.ch014openalex-snapshot
Steps Toward a Virtual Rat: Predictive Absorption, Distribution, Metabolism, and Toxicity Models
inferred← citeshttps://doi.org/10.1021/cg1001946openalex-snapshot
Sublimation of Molecular Crystals: Prediction of Sublimation Functions on the Basis of HYBOT Physicochemical Descriptors and Structural Clusterization
inferred← citeshttps://doi.org/10.1021/ci010287zopenalex-snapshot
Prediction of Aqueous Solubility of Organic Compounds by the General Solubility Equation (GSE)
inferred← citeshttps://doi.org/10.1021/ci020279yopenalex-snapshot
Modeling Aqueous Solubility
inferred← citeshttps://doi.org/10.1021/ci034107sopenalex-snapshot
Support Vector Machines for the Estimation of Aqueous Solubility
inferred← citeshttps://doi.org/10.1021/ci034120copenalex-snapshot
A General Treatment of Solubility. 1. The QSPR Correlation of Solvation Free Energies of Single Solutes in Series of Solvents
inferred← citeshttps://doi.org/10.1021/ci034184nopenalex-snapshot
ADME Evaluation in Drug Discovery. 4. Prediction of Aqueous Solubility Based on Atom Contribution Approach
inferred← citeshttps://doi.org/10.1021/ci0342324openalex-snapshot
Feature Selection for Descriptor Based Classification Models. 1. Theory and GA-SEC Algorithm
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ESOL: Estimating Aqueous Solubility Directly from Molecular Structure
inferred← citeshttps://doi.org/10.1021/ci0496797openalex-snapshot
LINGO, an Efficient Holographic Text Based Method To Calculate Biophysical Properties and Intermolecular Similarities
inferred← citeshttps://doi.org/10.1021/ci060164kopenalex-snapshot
Random Forest Models To Predict Aqueous Solubility
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Binary Classification of Aqueous Solubility Using Support Vector Machines with Reduction and Recombination Feature Selection
inferred← citeshttps://doi.org/10.1021/ci400187yopenalex-snapshot
Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules
inferred← citeshttps://doi.org/10.1021/ci500110vopenalex-snapshot
Structural Similarity Based Kriging for Quantitative Structure Activity and Property Relationship Modeling
inferred← citeshttps://doi.org/10.1021/ci600205gopenalex-snapshot
Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach
inferred← citeshttps://doi.org/10.1021/ci600397popenalex-snapshot
One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties
inferred← citeshttps://doi.org/10.1021/ci600541fopenalex-snapshot
Counting Clusters Using R-NN Curves
inferred← citeshttps://doi.org/10.1021/ci700096ropenalex-snapshot
Development of Reliable Aqueous Solubility Models and Their Application in Druglike Analysis
inferred← citeshttps://doi.org/10.1021/ci700307popenalex-snapshot
Why Are Some Properties More Difficult To Predict than Others? A Study of QSPR Models of Solubility, Melting Point, and Log P
inferred← citeshttps://doi.org/10.1021/ci800024copenalex-snapshot
Scores of Extended Connectivity Fingerprint as Descriptors in QSPR Study of Melting Point and Aqueous Solubility
inferred← citeshttps://doi.org/10.1021/ci800323qopenalex-snapshot
New Developments in Hydrogen Bonding Acidity and Basicity of Small Organic Molecules for the Prediction of Physical and ADMET Properties. Part 2. The Universal Solvation Equation
inferred← citeshttps://doi.org/10.1021/ci800406yopenalex-snapshot
Aqueous Solubility Prediction Based on Weighted Atom Type Counts and Solvent Accessible Surface Areas
inferred← citeshttps://doi.org/10.1021/ci9000162openalex-snapshot
Prediction of HPLC Retention Index Using Artificial Neural Networks and IGroup E-State Indices
inferred← citeshttps://doi.org/10.1021/ci900377eopenalex-snapshot
Insolubility Classification with Accurate Prediction Probabilities Using a MetaClassifier
inferred← citeshttps://doi.org/10.1021/ci900464sopenalex-snapshot
Drug- and Lead-likeness, Target Class, and Molecular Diversity Analysis of 7.9 Million Commercially Available Organic Compounds Provided by 29 Suppliers
inferred← citeshttps://doi.org/10.1021/cr900238dopenalex-snapshot
Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction
inferred← citeshttps://doi.org/10.1021/ct300035uopenalex-snapshot
The Structure, Thermodynamics, and Solubility of Organic Crystals from Simulation with a Polarizable Force Field
inferred← citeshttps://doi.org/10.1021/ie0205290openalex-snapshot
Group-Contribution-Based Estimation of Octanol/Water Partition Coefficient and Aqueous Solubility
inferred← citeshttps://doi.org/10.1021/je101061topenalex-snapshot
Representation/Prediction of Solubilities of Pure Compounds in Water Using Artificial Neural Network−Group Contribution Method
inferred← citeshttps://doi.org/10.1021/jm0706416openalex-snapshot
Poorly Soluble Marketed Drugs Display Solvation Limited Solubility
inferred← citeshttps://doi.org/10.1021/jm701407xopenalex-snapshot
Straightforward Recursive Partitioning Model for Discarding Insoluble Compounds in the Drug Discovery Process
inferred← citeshttps://doi.org/10.1021/jp1067654openalex-snapshot
Local and Bulk Hydration of Zwitterionic Glycine and its Analogues through Molecular Simulations
inferred← citeshttps://doi.org/10.1021/mp500103ropenalex-snapshot
Is Experimental Data Quality the Limiting Factor in Predicting the Aqueous Solubility of Druglike Molecules?
inferred← citeshttps://doi.org/10.1023/a:1012284411691openalex-snapshot
Simultaneous prediction of aqueous solubility and octanol/water partition coefficient based on descriptors derived from molecular structure
inferred← citeshttps://doi.org/10.1023/a:1015103914543openalex-snapshot
Estimation of Aqueous Solubility of Organic Compounds with QSPR Approach
inferred← citeshttps://doi.org/10.1023/a:1026195503465openalex-snapshot
Solubility Prediction by Recursive Partitioning
inferred← citeshttps://doi.org/10.1023/b:jcam.0000030031.81235.05openalex-snapshot
Linear and nonlinear functions on modeling of aqueous solubility of organic compounds by two structure representation methods
inferred← citeshttps://doi.org/10.1023/b:modi.0000006538.99122.00openalex-snapshot
Validation subset selections for extrapolation oriented QSPAR models
inferred← citeshttps://doi.org/10.1023/b:modi.0000047512.82293.75openalex-snapshot
New predictors for several ADME/Tox properties: Aqueous solubility, human oral absorption, and Ames genotoxicity using topological descriptors
inferred← citeshttps://doi.org/10.1038/s41467-020-19594-zopenalex-snapshot
Machine learning with physicochemical relationships: solubility prediction in organic solvents and water
inferred← citeshttps://doi.org/10.1038/s41597-019-0151-1openalex-snapshot
AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds
inferred← citeshttps://doi.org/10.1038/s41597-022-01154-3openalex-snapshot
Boosting the predictive performance with aqueous solubility dataset curation
inferred← citeshttps://doi.org/10.1038/s41597-022-01814-4openalex-snapshot
SOMAS: a platform for data-driven material discovery in redox flow battery development
inferred← citeshttps://doi.org/10.1038/s41597-022-01832-2openalex-snapshot
RedDB, a computational database of electroactive molecules for aqueous redox flow batteries
inferred← citeshttps://doi.org/10.1038/s41598-025-28272-3openalex-snapshot
Predicting drug solubility in binary solvent mixtures using graph convolutional networks: a comprehensive deep learning approach
inferred← citeshttps://doi.org/10.1038/s42256-021-00301-6openalex-snapshot
Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations
inferred← citeshttps://doi.org/10.1039/9781847558879-00240openalex-snapshot
Early ADME/T Predictions: Toy or Tool?
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