A deep learning framework combining molecular image and protein structural representations identifies candidate drugs for pain

Cell Rep Methods. 2024 Oct 21;4(10):100865. doi: 10.1016/j.crmeth.2024.100865. Epub 2024 Sep 27.

Abstract

Artificial intelligence (AI) and deep learning technologies hold promise for identifying effective drugs for human diseases, including pain. Here, we present an interpretable deep-learning-based ligand image- and receptor's three-dimensional (3D)-structure-aware framework to predict compound-protein interactions (LISA-CPI). LISA-CPI integrates an unsupervised deep-learning-based molecular image representation (ImageMol) of ligands and an advanced AlphaFold2-based algorithm (Evoformer). We demonstrated that LISA-CPI achieved ∼20% improvement in the average mean absolute error (MAE) compared to state-of-the-art models on experimental CPIs connecting 104,969 ligands and 33 G-protein-coupled receptors (GPCRs). Using LISA-CPI, we prioritized potential repurposable drugs (e.g., methylergometrine) and identified candidate gut-microbiota-derived metabolites (e.g., citicoline) for potential treatment of pain via specifically targeting human GPCRs. In summary, we presented that the integration of molecular image and protein 3D structural representations using a deep learning framework offers a powerful computational drug discovery tool for treating pain and other complex diseases if broadly applied.

Keywords: AlphaFold2; CP: Systems biology; Evoformer; G-protein coupled receptor; GPCR; ImageMol; artificial intelligence; compound-protein interaction; deep learning; drug repurposing; gut metabolite; pain.

MeSH terms

  • Algorithms
  • Deep Learning*
  • Drug Discovery / methods
  • Humans
  • Ligands
  • Molecular Imaging / methods
  • Pain / drug therapy
  • Receptors, G-Protein-Coupled / chemistry
  • Receptors, G-Protein-Coupled / metabolism

Substances

  • Ligands
  • Receptors, G-Protein-Coupled