A semi-automated material exploration scheme to predict the solubilities of tetraphenylporphyrin derivatives.

ABSTRACT: Acceleration of material discovery has been tackled by informatics and laboratory automation. Here we show a semi-automated material exploration scheme to modelize the solubility of tetraphenylporphyrin derivatives. The scheme involved the following steps: definition of a practical chemical search space, prioritization of molecules in the space using an extended algorithm for submodular function maximization without requiring biased variable selection or pre-existing data, synthesis & automated measurement, and machine-learning model estimation. The optimal evaluation order selected using the algorithm covered several similar molecules (32% of all targeted molecules, whereas that obtained by random sampling and uncertainty sampling was ~7% and ~4%, respectively) with a small number of eval

SUBMITTER: Shirasawa R 

PROVIDER: S-EPMC9814751 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature