  Predicting the yield percentage of a chemical reaction is useful in many
aspects such as reducing wet-lab experimentation by giving the priority to the
reactions with a high predicted yield. In this work we investigated the use of
multiple type inputs to predict chemical reaction yield. We used simplified
molecular-input line-entry system (SMILES) as well as calculated chemical
descriptors as model inputs. The model consists of a pre-trained bidirectional
transformer-based encoder (BERT) and a multi-layer perceptron (MLP) with a
regression head to predict the yield. We experimented on two high throughput
experimentation (HTE) datasets for Buchwald-Hartwig and Suzuki-Miyaura
reactions. The experiments show improvements in the prediction on both datasets
compared to systems using only SMILES or chemical descriptors as input. We also
tested the model's performance on out-of-sample dataset splits of
Buchwald-Hartwig and achieved comparable results with the state-of-the-art. In
addition to predicting the yield, we demonstrated the model's ability to
suggest the optimum (highest yield) reaction conditions. The model was able to
suggest conditions that achieves 94% of the optimum reported yields. This
proves the model to be useful in achieving the best results in the wet lab
without expensive experimentation.
