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pKa Prediction of Monoprotic Small Molecules the SMARTS Way

https://doi.org/10.1021/ci8001815
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What this work says

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

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Absorption and Drug Development
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A Rigorous Test for SPARC's Chemical Reactivity Models: Estimation of More Than 4300 Ionization pKas
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Receptor-Based Prediction of Binding Affinities
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pK a Prediction for Organic Acids and Bases
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Discovery of 4-substituted-8-(2-hydroxy-2-phenyl-cyclohexyl)-2,8-diaza-spiro[4.5]decan-1-one as a novel class of highly selective GlyT1 inhibitors with improved metabolic stability
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Computational determination of pKa values. A comparison of different theoretical approaches and a novel procedure
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QSPR study for estimation of acidity constants of some aromatic acids derivatives using multiple linear regression (MLR) analysis
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Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited
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New and Original pKa Prediction Method Using Grid Molecular Interaction Fields
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First Principles Calculations of Aqueous pKaValues for Organic and Inorganic Acids Using COSMO−RS Reveal an Inconsistency in the Slope of the pKaScale
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Prediction of the pKa of Carboxylic Acids Using the ab Initio Continuum-Solvation Model PCM-UAHF
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Compound Optimization in Early and Late Phase Drug Discovery: Acceptable Pharmacokinetic Properties Utilizing Combined Physicochemical, In Vitro and In Vivo Screens
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What points at this work

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

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A reliable and efficient first principles‐based method for predicting pKa values. 4. organic bases
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AMBIT‐SMARTS: Efficient Searching of Chemical Structures and Fragments
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Blind prediction of distribution in the SAMPL5 challenge with QM based protomer and pK a corrections
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Machine learning methods for pKa prediction of small molecules: Advances and challenges
inferred← citeshttps://doi.org/10.1016/j.ejmech.2010.06.026openalex-snapshot
Extending pKa prediction accuracy: High-throughput pKa measurements to understand pKa modulation of new chemical series
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Establishment of a quantitative bioanalytical method for an acetylcholinesterase inhibitor Ethyl 3-(2-(4-fluorophenyl) amino)-4-phenylthiazol-5-yl)-3-oxopropanoate including its physicochemical characterization and in vitro metabolite profiling using Liquid Chromatography-Mass Spectrometry
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inferred← citeshttps://doi.org/10.1021/acs.jcim.6c00255openalex-snapshot
p K a Predicting Models Trained with a Tautomer-Compatible Graph Dataset with Quantum Chemical Features
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00537openalex-snapshot
Weighted Averaging Scheme and Local Atomic Descriptor for pKa Prediction Based on Density Functional Theory
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00498openalex-snapshot
Prediction of pKa Using Machine Learning Methods with Rooted Topological Torsion Fingerprints: Application to Aliphatic Amines
inferred← citeshttps://doi.org/10.1021/acs.jctc.6b00805openalex-snapshot
Multiconformation, Density Functional Theory-Based p K a Prediction in Application to Large, Flexible Organic Molecules with Diverse Functional Groups
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Evaluation of pKa Estimation Methods on 211 Druglike Compounds
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Predicting pKa
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Constant pH Replica Exchange Molecular Dynamics in Biomolecules Using a Discrete Protonation Model
inferred← citeshttps://doi.org/10.1021/jp9067087openalex-snapshot
A Reliable and Efficient First Principles-Based Method for Predicting pKa Values. 2. Organic Acids
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Theoretical Study on Acidities of (S)-Proline Amide Derivatives in DMSO and Its Implications for Organocatalysis
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