pK_a Prediction

Knowing the pK_a of a molecule is key to understanding its structure and reactivity. Rowan's pK_a prediction workflow uses machine-learned interatomic potentials and semiempirical solvation energies to enable fast and accurate prediction of pK_a values with minimal empiricism.

How It Works

The full Rowan pK_a workflow is explained in our preprint. Here's a high-level visual overview:

"An overview of how Rowan predicts pKa values"

Accuracy

On a variety of benchmarks (also described in the preprint), Rowan pK_a displays mean absolute errors of around 1 pK_a unit, and decent predictive accuracy as assessed by Kendall's τ and r² values. Here's the performance on the SAMPL7 benchmark set:

"Our performance on SAMPL7"

For a full list of all assays surveyed, see the preprint.

Modes

Rowan pK_a can be run in three modes: careful, rapid, or reckless. Here's what selecting each mode tunes:

In general, we've found that the "careful" mode offers the best combination of speed and accuracy, but faster modes offer increased performance where high throughput is required.