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Scores of Extended Connectivity Fingerprint as Descriptors in QSPR Study of Melting Point and Aqueous Solubility

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What this work says

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/qsar.200330822openalex-snapshot

Prediction of Aqueous Solubility of Organic Compounds by Topological Descriptors

inferredcites →https://doi.org/10.1021/cg010009sopenalex-snapshot

Perspective on the Relationship between Melting Points and Chemical Structure

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/ci010287zopenalex-snapshot

Prediction of Aqueous Solubility of Organic Compounds by the General Solubility Equation (GSE)

inferredcites →https://doi.org/10.1021/ci010289jopenalex-snapshot

Development of Quantitative Structure−Property Relationship Models for Early ADME Evaluation in Drug Discovery. 1. Aqueous Solubility

inferredcites →https://doi.org/10.1021/ci025590uopenalex-snapshot

Prediction of Aqueous Solubility of Organic Compounds Based on a 3D Structure Representation

inferredcites →https://doi.org/10.1021/ci030304fopenalex-snapshot

A Universal Molecular Descriptor System for Prediction of LogP, LogS, LogBB, and Absorption

inferredcites →https://doi.org/10.1021/ci034006uopenalex-snapshot

Prediction of Aqueous Solubility and Partition Coefficient Optimized by a Genetic Algorithm Based Descriptor Selection Method

inferredcites →https://doi.org/10.1021/ci034107sopenalex-snapshot

Support Vector Machines for the Estimation of Aqueous Solubility

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/ci049744copenalex-snapshot

Generalized Fragment-Substructure Based Property Prediction Method

inferredcites →https://doi.org/10.1021/ci0500132openalex-snapshot

General Melting Point Prediction Based on a Diverse Compound Data Set and Artificial Neural Networks

inferredcites →https://doi.org/10.1021/ci050307nopenalex-snapshot

QSPR Correlation of Melting Point for Drug Compounds Based on Different Sources of Molecular Descriptors

inferredcites →https://doi.org/10.1021/ci060149fopenalex-snapshot

Melting Point Prediction Employing k -Nearest Neighbor Algorithms and Genetic Parameter Optimization

inferredcites →https://doi.org/10.1021/ci060164kopenalex-snapshot

Random Forest Models To Predict Aqueous Solubility

inferredcites →https://doi.org/10.1021/ci700372sopenalex-snapshot

Using Molecular Fingerprint as Descriptors in the QSPR Study of Lipophilicity

inferredcites →https://doi.org/10.1021/ci980022topenalex-snapshot

QSPR Studies on Vapor Pressure, Aqueous Solubility, and the Prediction of Water−Air Partition Coefficients

inferredcites →https://doi.org/10.1021/ci9901338openalex-snapshot

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

inferredcites →https://doi.org/10.1021/ie049378mopenalex-snapshot

Estimation of Melting Points of Organic Compounds

inferredcites →https://doi.org/10.1021/ie051130popenalex-snapshot

An Improved Structure−Property Model for Predicting Melting-Point Temperatures

inferredcites →https://doi.org/10.1021/ja00226a005openalex-snapshot

Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins

inferredcites →https://doi.org/10.1023/a:1020816005910openalex-snapshot

Towards a new age of virtual ADME/TOX and multidimensional drug discovery

inferredcites →https://doi.org/10.1039/b409865jopenalex-snapshot

Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures

inferredcites →https://doi.org/10.1111/j.1742-7843.2005.pto960303.xopenalex-snapshot

In silico Predictions of Drug Solubility and Permeability: Two Rate‐limiting Barriers to Oral Drug Absorption

inferredcites →https://doi.org/10.1208/aapsj080104openalex-snapshot

Recent progress in the computational prediction of aqueous solubility and absorption

inferredcites →https://doi.org/10.2174/092986706775197917openalex-snapshot

In Silico Approaches to Prediction of Aqueous and DMSO Solubility of Drug-Like Compounds: Trends, Problems and Solutions

inferredcites →https://doi.org/10.2174/1386207003331454openalex-snapshot

Molecular Descriptors in Chemoinformatics, Computational Combinatorial Chemistry, and Virtual Screening

inferredcites →https://doi.org/10.32614/r.manualsopenalex-snapshot

R: A Language and Environment for Statistical Computing

What points at this work

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

inferred← citeshttps://doi.org/10.1002/minf.201300057openalex-snapshot

Topological Fingerprints as an Aid in Finding Structural Patterns for LRRK2 Inhibition

inferred← citeshttps://doi.org/10.1007/978-981-16-5180-9_17openalex-snapshot

Quantitative Structure-Property Relationship (QSPR) Modeling Applications in Formulation Development

inferred← citeshttps://doi.org/10.1007/s10822-008-9240-5openalex-snapshot

On the interpretation and interpretability of quantitative structure–activity relationship models

inferred← citeshttps://doi.org/10.1007/s40430-026-06739-2openalex-snapshot

Temperature-dependent QSPR models for density and refractive index of FAME blends based on molecular structure

inferred← citeshttps://doi.org/10.1016/j.chemolab.2017.08.003openalex-snapshot

PyDescriptor: A new PyMOL plugin for calculating thousands of easily understandable molecular descriptors

inferred← citeshttps://doi.org/10.1016/j.ejps.2010.04.016openalex-snapshot

Estimation of drug solubility in water, PEG 400 and their binary mixtures using the molecular structures of solutes

inferred← citeshttps://doi.org/10.1016/j.fuel.2024.131018openalex-snapshot

Inverse QSPR estimation of molar mass and unsaturation of FAME, BD, and blends from refractive index and speed of sound measurements

inferred← citeshttps://doi.org/10.1016/j.jmgm.2011.08.003openalex-snapshot

A quantum mechanical quantitative structure–property relationship study of the melting point of a variety of organosilicons

inferred← citeshttps://doi.org/10.1016/j.vascn.2019.106624openalex-snapshot

Precipitation of test chemicals in reaction solutions used in the amino acid derivative reactivity assay and the direct peptide reactivity assay

inferred← citeshttps://doi.org/10.1016/s1574-1400(09)00505-2openalex-snapshot

Chapter 5 Recent Advances on in silico ADME Modeling

inferred← citeshttps://doi.org/10.1021/acs.chemmater.4c03486openalex-snapshot

Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects

inferred← citeshttps://doi.org/10.1021/acs.chemrev.8b00728openalex-snapshot

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

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.8b00597openalex-snapshot

Molecular Similarity-Based Domain Applicability Metric Efficiently Identifies Out-of-Domain Compounds

inferred← citeshttps://doi.org/10.1021/acs.jpca.3c00112openalex-snapshot

Predicting the Melting Point of Energetic Molecules Using a Learnable Graph Neural Fingerprint Model

inferred← citeshttps://doi.org/10.1021/ci100050topenalex-snapshot

Extended-Connectivity Fingerprints

inferred← citeshttps://doi.org/10.1021/ci100364aopenalex-snapshot

Binary Classification of Aqueous Solubility Using Support Vector Machines with Reduction and Recombination Feature Selection

inferred← citeshttps://doi.org/10.1021/ci500016vopenalex-snapshot

Merging Applicability Domains for in Silico Assessment of Chemical Mutagenicity

inferred← citeshttps://doi.org/10.1021/ci800445aopenalex-snapshot

Identifying Novel Molecular Structures for Advanced Melanoma by Ligand-Based Virtual Screening

inferred← citeshttps://doi.org/10.1021/ci900308uopenalex-snapshot

Optimizing the Performance of In Silico ADMET General Models According to Local Requirements: MARS Approach. Solubility Estimations As Case Study

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/10.1038/s41597-024-03105-6openalex-snapshot

Will we ever be able to accurately predict solubility?

inferred← citeshttps://doi.org/10.1039/c2md00313aopenalex-snapshot

A medicinal chemistry perspective on melting point: matched molecular pair analysis of the effects of simple descriptors on the melting point of drug-like compounds

inferred← citeshttps://doi.org/10.1039/d2sc00291dopenalex-snapshot

Machine learning for flow batteries: opportunities and challenges

inferred← citeshttps://doi.org/10.1080/00268976.2010.532164openalex-snapshot

Prediction of melting point for drug-like compounds via QSPR methods

inferred← citeshttps://doi.org/10.1080/00498250902926906openalex-snapshot

Prediction of animal clearance using naïve Bayesian classification and extended connectivity fingerprints

inferred← citeshttps://doi.org/10.1103/physrevlett.119.208101openalex-snapshot

Optimal Design of Experiments by Combining Coarse and Fine Measurements

inferred← citeshttps://doi.org/10.1109/cac59555.2023.10450516openalex-snapshot

Compound Property Learning Based on Molecular Fingerprints and Complex Network Metrics

inferred← citeshttps://doi.org/10.1155/2021/5559338openalex-snapshot

Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning

inferred← citeshttps://doi.org/10.1186/1471-2105-10-s15-s10openalex-snapshot

Physicochemical property space distribution among human metabolites, drugs and toxins

inferred← citeshttps://doi.org/10.1186/s13321-018-0304-9openalex-snapshot

Evaluating parameters for ligand-based modeling with random forest on sparse data sets

inferred← citeshttps://doi.org/10.1517/17460441.2016.1117070openalex-snapshot

An overview of molecular fingerprint similarity search in virtual screening

inferred← citeshttps://doi.org/10.21007/etd.cghs.2010.0343openalex-snapshot

TDiscovery and Development of Novel Therapeutic Agents for Advanced Melanoma

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-2024-zr2vropenalex-snapshot

Molecular Fingerprints Optimization for Enhanced Predictive Modeling

inferred← citeshttps://doi.org/10.3103/s0146411617020043openalex-snapshot

Deep learning in pharmacy: The prediction of aqueous solubility based on deep belief network

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