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Macro-pKa

Accurate, physics-based modeling of the aqueous ionization and speciation behavior of small molecules

Macro-pKa

Overview

Macro-pKa  is an automated solution for predicting macroscopic pKa values and pH-dependent tautomeric populations of ligands that combines physics-based DFT calculations with empirical corrections.

Key Capabilities

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Automatically identify protonatable/deprotonatable sites, enumerate the protonation states, and calculate the micro-pKa values with an enhanced scheme for generating empirical corrections 
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Calculate the complex aqueous speciation behavior of tautomerizable ligands
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Capture nuanced but important energetic effects like conformational averaging, solvation/shielding, hydrogen bonding, and stereochemistry
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Generate an easy-to-read report that includes predicted macro-pKa of a small molecule, its different constituent protonation states and their populations, and a speciation diagram for the major species in solution
FeaturedSchrödinger solutions for small molecule protonation state enumeration and pKa prediction

Schrödinger solutions for small molecule protonation state enumeration and pKa prediction

Learn about the various solutions for predicting pKa values, protonation state distribution, and derived properties that can be applied across a range of drug discovery stages, from screening through lead optimization.

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Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Life Science
Discovery of a Novel Class of d-Amino Acid Oxidase Inhibitors Using the Schr’dinger Computational Platform

Training & Resources

Online certification courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.

Tutorials

Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.