Producción CyT

Graph Neural Networks and Molecular Docking as Two Complementary Approaches for Virtual Screening: A Case Study on Cruzain

Artículo

Autoría:

Gómez Chávez, José Leonardo ; Luchi, Adriano Martín ; Villafañe, Roxana Noelia ; CONTI, GERMAN ANDRÉS ; Perez, Ernesto Rafael ; Angelina, Emilio Luis ; Peruchena, Nélida María

Fecha:

2024

Editorial y Lugar de Edición:

Wiley-VCH GmbH

Revista:

ChemistrySelect, vol. 9 - ISSN 2365-6549
Wiley-VCH GmbH

ISSN:

2365-6549

Resumen *

Molecular docking is one of the most widely used techniques for virtual screening (VS) of potential drug candidates. Despite its popularity, docking accuracy is often limited due to the trade- off between speed and precision required for screening large compound libraries. In the present work, we leverage graph convolutional networks (GCNs), a state-of-the-art deep neural network architecture, to enhance docking capacity for prioritiz- ing active compounds from a library of ∼200,000 compounds screened against Cruzain. We propose strategies to integrate both techniques into a single VS pipeline. By applying the GCN as a pre-docking filter, the compound library was enriched with active molecules, resulting in higher hit rates in subsequent docking screenings. Additionally, to further enhance the docking performance, the GCN-learned atomic embeddings were directly incorporated into the docking process through pharmacophoric restraints. Unlike common approaches that use deep learning (DL) scoring functions to rank pre-generated docking poses, the approaches we propose here have the advantage that only com- pounds that passed the DL filters need to be screened by the more computationally demanding docking method. This work might serve as a proof of concept for combining deep learning and classical docking in drug discovery. Información suministrada por el agente en SIGEVA

Palabras Clave

Computer- aided drug discoveryChagas diseaseArtificial intelligenceEmbeddings Deep learning