Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry
Kenneth Atz, David F. Nippa, Alex T. Müller, Vera Jost, Andrea Anelli, Michael Reutlinger, Christian Krämer, Rainer E. Martin, Uwe Grether, Gisbert Schneider, Georg Wuitschik · RSC Medicinal Chemistry · 2024
-score for a binary classification of 79.1% (±0.9%). Validation on eight reactions revealed a receiver operating characteristic (ROC) curve (AUC) value of 0.82 (±0.07) for few-shot machine learning. On the other hand, zero-shot machine learning models achieved a mean ROC-AUC value of 0.63 (±0.16). This study positively advocates the application of few-shot machine learning-guided reaction condition selection for HTE campaigns in medicinal chemistry and highlights practical applications as well as challenges associated with zero-shot machine learning.