Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

This offers flexibility in the reaction types that can neural networks (GNNs) to model organic chemical be queried and includes a broad condition space from all reactions. To do so, we prepared a dataset of organic chemistry. However, given the sparsity of global collection of four ubiquitous reactions from the organic datasets, reliable predictions are likely only obtained for the chemistry literature. We evaluate seven different most common conditions of each reaction type, regardless GNN architectures for classification tasks of the structural differences between inputs. This poses a pertaining to the identification of experimental severe limitation for catalytic reactions in that the optimal reagents and conditions. We find that models are conditions are often highly dependent on substrate structure able to identify specific graph features that affect (Mahatthananchai et al., 2012). It is therefore critical that reaction conditions and lead to accurate predictions.