The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions.

ABSTRACT: Accurate prediction of reaction yield is the holy grail for computer-assisted synthesis prediction, but current models have failed to generalize to large literature datasets. To understand the causes and inspire future design, we systematically benchmarked the yield prediction task. We carefully curated and augmented a literature dataset of 41 239 amide coupling reactions, each with information on reactants, products, intermediates, yields, and reaction contexts, and provided 3D structures for the molecules. We calculated molecular features related to 2D and 3D structure information, as well as physical and electronic properties. These descriptors were paired with 4 categories of machine learning methods (linear, kernel, ensemble, and neural network), yielding valuable benchmarks about fea

SUBMITTER: Liu Z 

PROVIDER: S-EPMC10566507 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature