LogP, API Key Budgets, and a User Survey
the golden mean of logP; three approaches to predicting logP; API key budgets for low-trust delegation; a user survey and some blog posts
LogP
The logarithm of the octanol–water partition coefficient, logP, is a crucial quantity for small-molecule therapeutics. LogP measures a compound’s lipophilicity or “fattiness,” which correlates with that compound’s ability to dissolve in water, be absorbed through the intestinal wall, and permeate cell membranes. These concerns mean that a well-balanced logP is crucial for an effective drug; greasy molecules with a high logP are poorly soluble and can bind or accumulate in weird ways, while polar molecules with a low logP are often poorly bioavailable and cell-permeable.
Our new logP workflow includes three methods for calculating/predicting logP: RDKit’s simple Crippen logP model, a Chemprop D-MPNN model trained on experimental data, and a physics-based method that uses COSMO-RS to predict logP. Here’s a few details about the first two methods:
RDKit’s Crippen logP model is a venerable atom-contribution-based method that’s been a mainstay of industry cheminformatics for decades.
Our Chemprop D-MPNN method is a machine-learning model trained on the recently released SangsterLogP dataset of 23K unique experimental logP values.
In our hands, both of these methods achieve high levels of accuracy on drug-like molecules. The graphs below show their performance on the held-out prospective set from the SangsterLogP paper, with the Chemprop model having a slight edge in performance.
Like all empirical methods, we expect that both the Chemprop and Crippen models will perform better in typical drug-like regions of chemical space and struggle for exotic species.
Our third method is a physics-based approach using COSMO-RS. COSMO-RS is a method that combines quantum chemistry and thermodynamics to predict properties like solubility and activity coefficients for solvents and mixtures. Our COSMO-RS-based logP-prediction workflow generally follows the protocol developed by Wilcken and co-workers at Novartis and mirrors our solvent-dependent conformers workflow. For each input molecule:
Conformers are generated with openconf.
Each conformer is optimized with GFN2-xTB/ALPB(Water) // g-xtb/CPCM-X(Water).
Conformers are then clustered based on simple descriptors like like solvent-accessible surface area, polar surface area, radius of gyration, etc.
The lowest energy conformers from each cluster are then subjected to a BP86/def2-TZVPD DFT single-point calculation, which is used as the input to COSMO-RS.
The COSMO-RS results are used to evaluate each conformer’s free energy of solvation in both water and octanol to yield a calculated logP.
These calculations are much slower than the two above methods, but we expect the results to be far more robust to complex, out-of-distribution molecules. COSMO-RS-based workflows have previously done well in blinded logP-prediction challenges (e.g.). In our benchmarking, we’ve noticed solid performance on drug-like molecules and are interested in pushing the boundaries of these predictions.
API Key Budgets
With our agentic future looming nigh, we’re continuing to build the controls necessary to give an agent access to Rowan in a stress-free way. We’ve previously given keys strict controls so that keys can only read & write data (no deletion power) or even only read data, and API keys can be issued that are scoped to a single project within your Rowan account.
Our latest addition is the ability to give an API key a budget. Jobs started using this API key will count towards this budget, and when the API key’s budget is consumed, all the associated jobs will be stopped.
This means you can give an agent (or a person) an API key and let them run wild without worrying about any surprise billing. These budgets can be set in the GUI via the API keys page:
API key budgets can also be managed programmatically through Rowan’s Python API. When creating an API key, you can specify a budget parameter in the create_api_key call. Budgets can later be modified using update_api_key_budget. Both operations require a master API key with the can_manage_api_keys permission.
User Survey
Help shape the future of Rowan by taking our summer 2026 user survey! We really value hearing feedback from our users and want to make Rowan as useful as possible for scientists working in chemical R&D.
We expect the survey to take 5–10 minutes. After you complete it, we’ll add 500 credits to your Rowan account; these credits will expire one year after they are issued. Credits are offered in exchange for thoughtful, good-faith responses. Submissions that contain no substantive feedback or appear to have been completed at random will not be eligible.
This survey will close end of day on August 14, 2026.
More from Our Team
Corin gave AI agents challenging NMR spectra from the literature and asked them to figure out the structure. The agents were run at different reasoning levels and some were given access to Rowan’s NMR workflow. We found that use of the external NMR tool led to a dramatic improvement in agent performance. Additionally, tool integration helped the agents make the most of their reasoning level. Read the full post.
Nick wrote a post that outlines different conformer-generation and conformer-search methods. If you’re curious about how to find conformers or need help choosing the right tool for your work, read “How to Find Conformers.”
Ari wrote a practical overview of how to use computational chemistry to model reactions for the unfamiliar. If you’re not sure how to find a guess TS, confirm you’ve found the right TS, or calculate accurate barrier heights, check out “How to Model a Reaction.”
Finally, we put a guide together for developers building Rowan integrations into their user-facing applications. If you’re building a platform for your internal users at your company or an AI-for-science platform, this post outlines how to best leverage Rowan’s data model and API key features. Read the guide here.
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