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Molecular photoswitches are the foundation of light-activated drugs. A keyphotoswitch is azobenzene, which exhibits trans-cis isomerism in response tolight. The thermal half-life of the cis isomer is of crucial importance, sinceit controls the duration of the light-induced biological effect. Here weintroduce a computational tool for predicting the thermal half-lives ofazobenzene derivatives. Our automated approach uses a fast and accurate machinelearning potential trained on quantum chemistry data. Building onwell-established earlier evidence, we argue that thermal isomerization proceedsthrough rotation mediated by intersystem crossing, and incorporate thismechanism into our automated workflow. We use our approach to predict thethermal half-lives of 19,000 azobenzene derivatives. We explore trends andtradeoffs between barriers and absorption wavelengths, and open-source our dataand software to accelerate research in photopharmacology.

Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential