AI in chemistry: results you can rely on
The free guide shows on eight pages when you can trust an AI result in chemistry and how to run a first, verifiable trial in about ten minutes.
PDF, 8 pages, A4, free, by email once you have confirmed your address.
What the guide covers
- 01Model or toolWhich tasks you can leave to a language model and which a chemistry tool should calculate, from literature summaries to stability estimates.
- 02Seven questions to ask of every AI resultWhat to check before a number goes into a report, a protocol or a decision.
- 03Your first trial in about ten minutesStep by step with a molecule whose values you know, in the Sketcher or in your AI client.
- 04Six questions to ask every providerWhat to clarify about data and security as soon as structural or measurement data reach a tool.
Independently measured
AI models alone
14 to 41%
With CovaSyn tools
76 to 92%
14 to 41% without CovaSyn, 76 to 92% with CovaSyn. Measured on MolecularIQ, a benchmark we did not design (arXiv:2601.15279, Klambauer Lab, Institute for Machine Learning, JKU Linz).
Source: Bartmann C., Schimunek J., Ielanskyi M., Seidl P., Klambauer G., Luukkonen S. (2026). MolecularIQ: Characterizing Chemical Reasoning Capabilities Through Symbolic Verification on Molecular Graphs. arXiv:2601.15279. Snapshot: 2026-05-17.
Methodology and dataHow to get the guide
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