High Performance of Gradient Boosting in Binding Affinity Prediction

Dmitrii Gavrilev; Nurlybek Amangeldiuly; Sergei Ivanov; Evgeny Burnaev

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arXiv · 2205.07023

High Performance of Gradient Boosting in Binding Affinity Prediction

Dmitrii Gavrilev·Nurlybek Amangeldiuly·Sergei Ivanov·Evgeny Burnaev

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Abstract

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.

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Dmitrii Gavrilev, Nurlybek Amangeldiuly, Sergei Ivanov, Evgeny Burnaev. 2022-05-14. High Performance of Gradient Boosting in Binding Affinity Prediction. https://arxiv.org/abs/2205.07023

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