Virtual screening is a computational technique for predicting a potent binding compound for a receptor protein from a ligand library. It has been a widely used in the drug discovery field to reduce the efforts of medicinal chemists to find hit compounds by experiments.Here, we introduce our novel structure-based virtual screening program, PL-PatchSurfer, which uses molecular surface representation with the three-dimensional Zernike descriptors, which is an effective mathematical representation for identifying physicochemical complementarities between local surfaces of a target protein and a ligand. The advantage of the surface-patch description is its tolerance on a receptor and compound structure variation. PL-PatchSurfer2 achieves higher accuracy on apo form and computationally modeled receptor structures than conventional structure-based virtual screening programs. Thus, PL-PatchSurfer2 opens up an opportunity for targets that do not have their crystal structures. The program is provided as a stand-alone program at http://kiharalab.org/plps2 . We also provide files for two ligand libraries, ChEMBL and ZINC Drug-like.
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http://dx.doi.org/10.1007/978-1-4939-7756-7_7 | DOI Listing |
J Integr Neurosci
January 2025
Department of Radiology, Huzhou Central Hospital, The Affiliated Central Hospital of Huzhou University, 313000 Huzhou, Zhejiang, China.
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January 2025
Centro de Química Médica, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Santiago 7780272, Chile.
Acute myeloid leukemia (AML) presents significant therapeutic challenges, particularly in cases driven by mutations in the FLT3 tyrosine kinase. This study aimed to develop a robust and user-friendly machine learning-based quantitative structure-activity relationship (QSAR) model to predict the inhibitory potency (pIC values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. Using a dataset which was 14 times larger than those employed in prior studies (1350 compounds with 1269 molecular descriptors), we trained a random forest regressor, chosen due to its superior predictive performance and resistance to overfitting.
View Article and Find Full Text PDFPharmaceuticals (Basel)
January 2025
Laboratory of Biotechnology, National Higher School of Biotechnology, Ville Universitaire (University of Constantine 3), Ali Mendjeli, BP E66, Constantine 25100, Algeria.
Kynurenine aminotransferase II (KAT-II) is a target for treating several diseases characterized by an excess of kynurenic acid (KYNA). Although KAT-II inactivators are available, they often lead to adverse side effects due to their irreversible inhibition mechanism. This study aimed to identify potent and safe inhibitors of KAT-II using computational and in vitro approaches.
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December 2024
Faculty of Pharmacy, "Carol Davila" University of Medicine and Pharmacy, Traian Vuia 6, 020956 Bucharest, Romania.
Aurora kinase B (AurB) is a pivotal regulator of mitosis, making it a compelling target for cancer therapy. Despite significant advances in protein kinase inhibitor development, there are currently no AurB inhibitors readily available for therapeutic use. This study introduces a machine learning-assisted drug repurposing framework integrating quantitative structure-activity relationship (QSAR) modeling, molecular fingerprints-based classification, molecular docking, and molecular dynamics (MD) simulations.
View Article and Find Full Text PDFJ Clin Med
January 2025
Physical Medicine and Rehabilitation Unit, Department of Medical and Surgical Sciences, University of Catanzaro "Magna Graecia", 88100 Catanzaro, Italy.
: The evolution of technology has continuously redefined the landscape of rehabilitation medicine. Researchers have long incorporated virtual reality (VR) as a promising intervention, providing immersive therapeutic environments for patients. The emergence of the metaverse has recently further expanded the potential applications of VR to augment the possibilities in rehabilitation.
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