  Prediction of protein-ligand (PL) binding affinity remains the key to drug
discovery. Popular approaches in recent years involve graph neural networks
(GNNs), which are used to learn the topology and geometry of PL complexes.
However, GNNs are computationally heavy and have poor scalability to graph
sizes. On the other hand, traditional machine learning (ML) approaches, such as
gradient-boosted decision trees (GBDTs), are lightweight yet extremely
efficient for tabular data. We propose to use PL interaction features along
with PL graph-level features in GBDT. We show that this combination outperforms
the existing solutions.
