  This study assesses the efficiency of several popular machine learning
approaches in the prediction of molecular binding affinity: CatBoost, Graph
Attention Neural Network, and Bidirectional Encoder Representations from
Transformers. The models were trained to predict binding affinities in terms of
inhibition constants $K_i$ for pairs of proteins and small organic molecules.
First two approaches use thoroughly selected physico-chemical features, while
the third one is based on textual molecular representations - it is one of the
first attempts to apply Transformer-based predictors for the binding affinity.
We also discuss the visualization of attention layers within the Transformer
approach in order to highlight the molecular sites responsible for
interactions. All approaches are free from atomic spatial coordinates thus
avoiding bias from known structures and being able to generalize for compounds
with unknown conformations. The achieved accuracy for all suggested approaches
prove their potential in high throughput screening.
