Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.

Suzuki cross-coupling reactions are considered a valuable tool for constructing carbon-carbon bonds in small molecule drug discovery. However, the synthesis of chemical matter often represents a time-consuming and labour-intensive bottleneck. We demonstrate how machine learning methods trained on high-throughput experimentation (HTE) data can be leveraged to enable fast reaction condition selection for novel coupling partners. We show that the trained models support chemists in determining suitable catalyst-solvent-base combinations for individual transformations including an evaluation of the need for HTE screening. We introduce an algorithm for designing 96-well plates optimized towards reaction yields and discuss the model performance of zero- and few-shot machine learning. The best-performing machine learning model achieved a three-category classification accuracy of 76.3% (±0.2%) and an

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Déclaration de conflit d'intérêts

G. S. declares a potential financial and non-financial conflict of interest as co-founder of https://inSili.com LLC, Zurich and in his role as a scientific consultant to the pharmaceutical industry. K. A., D. F. N., A. T. M., V. J., M. R. U. G., R. E. M., C. K. and G. W. declare a potential financial and non-financial conflict of interest as full employees of F. Hoffmann-La Roche Ltd.