A Symbolic Regression Model for the Prediction of Drug Binding to Human Liver Microsomes.

It is common practice in the early drug discovery process to conduct in vitro screening experiments using liver microsomes in order to obtain an initial assessment of test compound metabolic stability. Compounds which bind to liver microsomes are unavailable for interaction with the drug metabolizing enzymes. As such, assessment of the unbound fraction of compound available for biotransformation is an important factor for interpretation of in vitro experimental results and to improve prediction of the in vivo metabolic clearance. Various in silico methods have been proposed for the prediction of test compound binding to microsomes, from various simple lipophilicity-based models with moderate performance to sophisticated machine learning models which demonstrate superior performance at the cost of increased complexity and higher data requirements. In this work, we attempt to strike a middle ground by developing easily implementable equations with improved predictive performance. We employ a symbolic regression approach based on a medium-size in-house data set of fraction unbound in human liver microsomes measurements allowing the identification of novel equations with improved performance. We validate the model performance on an in-house held-out test set and an external validation set.

Bidirectional Graphormer for Reactivity Understanding: Neural Network Trained to Reaction Atom-to-Atom Mapping Task.

This work introduces GraphormerMapper, a new algorithm for reaction atom-to-atom mapping (AAM) based on a transformer neural network adopted for the direct processing of molecular graphs as sets of atoms and bonds, as opposed to SMILES/SELFIES sequence-based approaches, in combination with the Bidirectional Encoder Representations from Transformers (BERT) network. The graph transformer serves to extract molecular features that are tied to atoms and bonds. The BERT network is used for chemical transformation learning. In a benchmarking study with IBM RxnMapper, which is the best AAM algorithm according to our previous study, we demonstrate that our AAM algorithm is superior to it on our "Golden" benchmarking data set.