g-xTB pKa and Website Redesign
the flaws with Rowan's AIMNet2-based pKa method; our new g-xTB-based approach; benchmarking and availability; a logo and new website for Rowan
We’re releasing a new microscopic pKa–prediction method today at Rowan, as well as—for the first time—a logo and a major refresh of our website.
Rowan pKa (g-xTB)
pKa prediction has a special place in our heart. Microscopic pKa was the first workflow we launched, over two years ago, and we’ve kept thinking about pKa since then—we’ve released a machine-learning-based macroscopic pKa workflow, wrote a blog post about microscopic and macroscopic pKa, studied pH-dependent aqueous solubility, and seen users apply our original pKa workflow to lots of interesting scientific problems.
Our original microscopic pKa workflow used ETDKG conformers and AIMNet2/CPCM-X(water) energies to compute ∆G for an acid–base pair and then applied a quadratic free-energy relationship to convert ∆G into a predicted aqueous pKa value. While we’ve been happy with this workflow, we’ve also noticed some issues over time:
AIMNet2 doesn’t work for the whole periodic table, meaning that some molecules are totally impossible to study with Rowan pKa (e.g. anything with metals or exotic elements).
While AIMNet2 was state-of-the-art in early 2024, when we were creating v1 of Rowan pKa, it’s now far from the most accurate low-cost method that exists, particularly for tricky thermochemistry like conjugate acid/conjugate base pairs.
And the conformer-generation pipeline that we used in Rowan pKa struggles with conformationally complex molecules, often running slowly or missing important conformers.
We’ve been looking forward to addressing these issues for some time, and today we’re releasing an updated version of Rowan pKa that (we think) represents a significant improvement in accuracy, generality, and reliability.
How It Works
Rowan’s new pKa-prediction method is powered by g-xTB, the new semiempirical method developed by Stefan Grimme and co-workers. g-xTB performs well on thermochemical benchmarks and contains an adaptive charge-dependent basis, making it a good choice for handling conjugate acid/conjugate base pairs. As with our recent solvent-dependent conformers workflow, we combine g-xTB single-point energies with CPCM-X(water) solvent corrections and GFN2-xTB vibrational free-energy corrections from single-point Hessian calculations to get a final per-compound free energy.
As with our earlier AIMNet2-based method, we use ETKDG to generate conformer ensembles; however, we now use the ReSCoSS-style clustering and filtering from our solvent-dependent conformer-search workflow (detailed here) to generate diverse conformer ensembles more efficiently. We’re also now optimizing in implicit solvent, which makes geometries more physical and should attenuate unphysical intramolecular interactions. (We plan to investigate additional conformer-generation methods here in the future, as we think there are probably more efficient ways to generate conformers for pKa than ETKDG.)
Once we’ve computed ∆G for the acid–base pair, we must still convert this to an aqueous pKa value. Previously, we used a quadratic free-energy relationship with per-element and per-valence corrections to improve performance; in the new g-xTB-based method, we’ve instead switched to per–functional group linear free-energy relationships. The scaling factors were obtained by fitting to a subset of the Dwar-iBond dataset, and new functional groups were automatically added based on the Bayesian information criterion.
This new method, like our previous AIMNet2-based method (and unlike our Starling ML model), falls under the “Quantum Mechanics” family of pKa-prediction methods; it relies mainly on inference-time simulation to capture complex chemical behavior, and uses only simple linear scaling to convert from simulated values to experimental data. This makes the method relatively robust and able to handle exotic chemistry or complex systems, but also means that it will be slower and less accurate than Starling for fast inference on drug-like molecules. (If you want to run millions of pKa values to sanitize input SMILES strings, this isn’t the right method for you.)
Benchmarks
We reinvestigated the original AIMNet2-based pKa-benchmark systems to see how our new method fared in comparison. (These benchmarks were run only once, after method development was entirely finished, and not used to fine-tune any scaling factors.)
In general, we found that our new g-xTB pKa method outperformed the original AIMNet2-based method. On the Rombouts set of tricyclic amidine BACE1 inhibitors, for instance, g-xTB shrinks the MAE from 1.06 to 0.59 pKa units:
Similarly, we see modest improvements on the Miller–Doukas–Seydel folate-inhibitor dataset (shown below, top) and the Müller dataset examining the effect of α- and β- substituents like oxetanes on cyclic amine basicity (shown below, bottom).
The new pKa-prediction method is also able to handle organometallic systems, which can be helpful:
While we don’t expect to have solved all pKa-related issues with this update, we’re excited to release a new and improved version of this popular workflow. Please let us know if you have any issues and we’ll do our best to fix them in the future!
To run the new g-xTB-based microscopic pKa workflow, simply select “Rowan pKa (Wagen 2026)” from the method dropdown on the microscopic pKa workflow. As with the other pKa-prediction methods here, this is available for all users (paying and otherwise).
Logo and Website Refresh
Since launch, we’ve been using “🌳 Rowan” as a placeholder for a logo that would one day come. Today, we’re excited to introduce our new logo alongside a major refresh of our website.
The logo draws on the five-pointed symmetry of Rowan flowers and the charmingly lo-fi aesthetic of old-school orbital density plots.
The updated site features fully redesigned landing pages tailored to key Rowan use cases, including medicinal chemistry, structure-based drug design, materials science, and agentic science.
We’ve also expanded our website’s social proof sections to highlight teams using Rowan in production, including ArrePath, Orogen Therapeutics, and Syntara. We’re grateful for the opportunity to support their work. As Kurt Thorn, CTO of ArrePath, shared:
ArrePath uses Rowan as its quantum chemistry backend for Ariadne, our custom medicinal chemistry co-pilot AI. Rowan works alongside our internal ML tools as an integral part of our AI/ML drug discovery platform. It’s a great tool for computing compound pKas, structures, and other molecular properties, and helps drive real-world compound prioritization and decision making.
If you’re looking for computational tools that are ready to plug into your molecular R&D workflows and agents, we’d love to connect.
Absolutely every word of this went right over my head BUT the logo looks fresh!!