Current Software Versions

What's New in PhysChem Suite Version 2023

Version 2023 introduces significant improvements to the Classic algorithm for pK_a prediction in PhysChem Suite which also impacts other predictions on the Percepta Platform. The updated algorithm increases the accuracy of prediction for broader chemical space than the previous algorithm and offers significant increase in calculation speed—also impacting other prediction models. Read below for details, and contact us for help upgrading your software.

Improvements in pK_a
Improved Accuracy of LogD & Aqueous Solubility Predictions
Other Improvements

Increased Speed of Calculation for “Apparent pK_a”

You will notice up to a 5–10-fold increase in the speed of calculation of Apparent pK_a values—the pK_a value equivalent to that observed experimentally. This will be particularly noticeable in batch calculations and for compounds with multiple ionizable centers. The improved performance is a result of:

A new method for calculation of apparent pK_a where one preferred protonation/deprotonation pathway is chosen for the calculation
Optimization of calculations for pK_a micro constants

Predict pK_a More Accurately for Expanded Chemical Space

You can have greater confidence in calculations from a significant expansion of the pK_a Classic algorithm training set. Improvements comprise:

Inclusion of ~4000 environmental, pharmaceutical, and chemical compounds to the training set
An increase in the number of experimental pK_a values to more than 40,000
Rebuilding of existing Hammet-type and new equations for novel chemical classes
Addition of new ionization centers

The linear regression scatter plots below (Figure 1) show the comparison of experimental versus predicted pK_a, before and after training, for a dataset of 373 pharmaceutical compounds with 578 ionization centers. The average error of prediction is significantly lower in v2023 (0.26 log units versus 1.48 log units in v2022), and R² is closer to 1 (0.98 versus 0.52 in v2022). Three ionization centers were not predicted by the v2022 algorithm.

Correlation of Experimental and Predicted pKa v2022 — Figure 1. Linear regression plots demonstrate the improved correlation between experimental and predicted pK_a (pK_a Classic algorithm) for 578 ionization centers from v2022 to v2023.

Training of the algorithm resulted in significant improvements in prediction accuracy for these 373 novel pharmaceutical compounds (see Figure 2). 84% of pK_a values for these compounds are now predicted within 0.5 log units (99% within 1 log unit). This compares to 38% within 0.5 log units (54% within 1 log unit) with three values not predicted at all with the v2022 Classic algorithm.

Improvement in pKa prediction accuracy — Figure 2. Improvement in prediction accuracy (pKa Classic algorithm) for 578 ionization centers in 373 pharmaceutical compounds in v2023.

Compare Acidity/Basicity of Ionization Centers More Easily

You can now choose between two methods for the calculation of pK_a with the Classic algorithm.

Apparent pK_a—simulates observed ionization of a molecule in aqueous solution and takes into account the protonation states of other ionizable groups at the relevant pH. (Default in previous versions.)
Single pK_a—a theoretical estimation of pK_a for a single ionization center as though it is the only ionizable group in the molecule. All other protonation/deprotonation sites are assumed to be neutral.

Improvements to the pK_a Classic algorithm directly impact predictions of logD and pH-dependent aqueous solubility. You can now expect greater accuracy for these calculations and expanded chemical space coverage.

Create Batch PDF Reports

You can now generate batch PDF reports from each module in PhysChem Suite, for all molecules in your active project. Previously, batch reports were only available from the Spreadsheet, including the values available there.

Unicode Support

You can now store textual data, written in your local language, in the Spreadsheet.

ACD/Labs’ development team is eager to collaborate with organizations to improve predictions for novel compounds. Do you have accurately measured experimental values for the predictions we support? Contact us to discuss how we may work together.

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