# Recent trends in machine learning and deep learning-based prediction of G-protein coupled receptor-ligand binding affinities

## TL;DR

This paper reviews recent machine learning and deep learning methods for predicting how strongly drugs bind to GPCRs, which are important drug targets.

## Contribution

The paper systematically categorizes and analyzes recent ML/DL models for GPCR-ligand binding affinity prediction.

## Key findings

- Sequence-based models use CNNs for high-throughput screening and benefit from attention mechanisms and self-supervised learning.
- Graph-based models use GNNs and molecular contact maps to capture topological features for substructure-sensitive predictions.
- Structure-based models incorporate spatial and conformational data to build high-resolution interaction models.

## Abstract

Accurately predicting protein-ligand binding affinity is key in drug discovery. Machine Learning and Deep Learning methods used in the drug discovery process have advanced the prediction of drug–target binding affinities, particularly for G protein–coupled receptors (GPCRs), a pharmacologically significant yet structurally heterogeneous protein family. In this review, binding affinity prediction models are examined and organized according to sequence-based one-dimensional, graph-based two-dimensional, and structure-based three-dimensional frameworks. Sequence-based models utilize convolutional neural networks for high-throughput screening. Recently published models incorporated attention mechanisms and self-supervised learning, enhancing interpretability and reducing dependence on annotated datasets. Graph-based models employ graph neural networks and molecular contact maps to capture topological features, enabling substructure-sensitive predictions. Structure-based approaches integrate spatial and conformational data into high-resolution interaction models. The hybrid use of these three approaches could significantly increase the success rate of in silico models for drug discovery, particularly for GPCRs.