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A database of >17,000 compounds, representing >32,000 pKa values, is used in determination of pKa.
Hammet-type equations for popular ionizable functional groups, and carefully derived electronic substituent constants (σ) are used to predict the most accurate pKa values.
Tautomeric equilibria, covalent hydration, resonance effects, and α, β-unsaturated systems are taken into consideration in the calculations.
The internal training set is comprised of >17,500 compounds representing >20, 000 ionization centres.
A database of ionization centres, interaction constants, and interaction calculation methods are used to simulate a complete distribution plot of produce a full range of protonation states of the molecule at different pH conditions. pH dependency of net molecular charge, distribution of protonation states, and the average charge of each ionization centre is provided.
Experimentally determined pKa values can be used to train the algorithms using the machine learning capabilities of the software. This both improves prediction accuracy and makes the model more relevant to your chemical space or project.
Both the Classic and GALAS algorithms offer machine learning (training) and you don't have to be a computational chemist to use them.
Do you have a large curated set of experimentally measured pKa values?
Our development team is happy to collaborate with you to expand the applicability domain of our algorithms.
Desktop/Thick client
Software installations for individual computers with a graphical user interface. Full physicochemical, ADME and toxicity calculator modules are available (with training capabilities) including the PhysChem Profiler bundle.
Batch
Screen tens of thousands of compounds with minimal user intervention—compatible with Microsoft Windows and Linux operating systems (OS). Plug-in to corporate intranets or workflow tools such as Pipeline Pilot.
Percepta Portal/Thin client
Web-based application for prediction of molecular properties (PhysChem, ADME, and toxicity) and data analysis. KNIME integration components available.
Host on your corporate intranet or the cloud. Available for Linux and Windows OS.
This application note discusses the importance of using the machine learning (model training) capabilities of predictive models to improve accuracy.
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Comparison of experimental and published pKa values for 88 cephalosporin antibiotics with ACD/pKa and other prediction software.
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Software from ACD/Labs was used in the prediction of physical and chemical properties, and retention modelling and optimization.
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The acid dissociation constant (Ka, also known as the acid-ionization constant) is a quantitative measure of the strength of an acid in solution (i.e. a measure of the tendency of a molecule to release H+ and generate a hydronium ion (H3O+). A strong acid will completely dissociate in water (equilibrium favors the right hand of the equation below), while weak acids will not.
HA + H2O ⇌ H3O+ + A-
Ka is the equilibrium constant that describes the dissociation of a molecule and is expressed as a ratio of concentrations of the various species present
The acid ionization constant (Ka) varies by orders of magnitude. It is therefore more intuitive to refer to such extreme numbers on a logarithmic scale. By convention the p in pKa was introduced to denote the negative logarithm (base 10).
pKa = -log10 Ka
Ka values are converted to pKa values as follows:
Phenol:
pKa = -log (1. 8 × 10-10) = 10.0
Acetic acid:
pKa = -log (1.8 × 10-5) = 4.8
There is no intrinsic reason to rule out pKa values less than 0 or greater than 14. For example, sulfuric acid, H2SO4, has a negative pKa for the loss of its first proton:
H2SO4 → HSO4- + H+ (pKa < 0)
Experimental determination of pKa values, however, is usually limited to between 1 and 13.
pKa (the acid dissociation constant) describes the inherent property of a compound or ionizable functional group to lose H+ and generate hydronium ions (H3O+).
pH measures the concentration of hydronium ions (H3O+) in aqueous solution
pH = -log [H3O+]
Synthetic chemists use the acid dissociation constant to understand what substances can be used to protonate or deprotonate a compound, to assist a reaction. In biochemistry pKa helps scientists understand the activity of enzymes and the stability of proteins.
In pharmacology, ionization of a compound changes its physical behavior and affects macro properties such as aqueous solubility and lipophilicity. pKa values are also used to understand more complex ADME characteristics.
pKa is used by chromatographers to select the appropriate pH of the mobile phase (buffer) for separations.
In environmental sciences acid-base equilibria of humic acids help establish the potability and treatment of water and provide information about the health of waterways such as lakes and rivers.
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