tinyIFD: A High-Throughput Binding Pose Refinement Workflow Through Induced-Fit Ligand Docking.
J Chem Inf Model
; 63(11): 3438-3447, 2023 06 12.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2323668
ABSTRACT
A critical step in structure-based drug discovery is predicting whether and how a candidate molecule binds to a model of a therapeutic target. However, substantial protein side chain movements prevent current screening methods, such as docking, from accurately predicting the ligand conformations and require expensive refinements to produce viable candidates. We present the development of a high-throughput and flexible ligand pose refinement workflow, called "tinyIFD". The main features of the workflow include the use of specialized high-throughput, small-system MD simulation code mdgx.cuda and an actively learning model zoo approach. We show the application of this workflow on a large test set of diverse protein targets, achieving 66% and 76% success rates for finding a crystal-like pose within the top-2 and top-5 poses, respectively. We also applied this workflow to the SARS-CoV-2 main protease (Mpro) inhibitors, where we demonstrate the benefit of the active learning aspect in this workflow.
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
COVID-19
Tipo de estudio:
Estudio pronóstico
Límite:
Humanos
Idioma:
Inglés
Revista:
J Chem Inf Model
Asunto de la revista:
Informática Médica
/
Química
Año:
2023
Tipo del documento:
Artículo
País de afiliación:
Acs.jcim.2c01530
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