Graph Neural Networks in Molecular Property Prediction

Abstract

Molecular property prediction is a key component in drug development, enabling early and accurate assessments of the chemical and biological characteristics of potential compounds. This step optimizes the selection process in vast molecular libraries and helps eliminate compounds with adverse profiles early, minimizing risks before advancing to later stages. Historically, these predictions have been made manually using molecular descriptor design, which required extensive expertise and has been prone to bias. To address these limitations, computational tools like SMILES and molecular fingerprints were introduced, automating the process and enhancing scalability. However, these approaches still retained biases, prompting the development of more advanced methods.

Recently, Graph Neural Networks (GNNs) have emerged as an innovative solution to address these challenges. GNNs model molecules directly as graphs, where atoms are nodes and bonds are edges. Through processes such as message passing, GNNs allow neighboring nodes to exchange information, enabling the construction of enriched representations that capture complex structural and spatial relationships. In the readout phase, this information is aggregated into a global vector called embedding, which is then used for tasks such as classification or regression. Despite their advantages, GNNs face challenges such as stereochemistry, and interpretability. Recent advancements, including Equivariant GNNs (EGNNs) and Graph Attention Networks (GATs), have made significant progress in mitigating these limitations and enhancing the overall performance of these networks. However, further research is required to more effectively tackle these and other persistent challenges.

Given the potential of Graph Neural Networks (GNNs) in molecular property prediction, this review provides a comprehensive analysis of their current state, highlighting their capabilities, challenges, and recent advancements. By consolidating foundational knowledge and the latest developments, this review serves as a clear and accessible resource for researchers and practitioners seeking to understand and leverage GNNs in molecular property prediction.