Type III beta phosphatidylinositol 4-kinase (PI4KIIIβ) is the only clinically validated drug target in Plasmodium kinases and therefore a critical target in developing novel drugs for malaria. Current PI4KIIIβ inhibitors have solubility and off-target problems. Here we set out to identify new Plasmodium PI4K ligands that could serve as leads for the development of new antimalarial drugs by building a PPI4K homology model since there was no available three-dimensional structure of PfPI4K and virtually screened a small library of ~ 22 000 fragments against it. Sixteen compounds from the fragment-based virtual screening (FBVS) were selected based on ≤ - 9.0 kcal/mol binding free energy cut-off value. These were subjected to similarity and sub-structure searching after they had passed PAINS screening and the obtained derivatives showed improved binding affinity for PfPI4K (- 10.00 to - 13.80 kcal/mol). Moreover, binding hypothesis of the top-scoring compound (31) was confirmed in a 100 ns molecular dynamics simulation and its binding pose retrieved after the system had converged at about 10 ns into the evolution was described to lay foundation for a rationale chemical-modification to optimize binding to PfPI4K. Overall, compound 31 appears to be a viable starting point for the development of PPI4K inhibitors with antimalarial activity.
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http://dx.doi.org/10.1186/s13065-022-00812-2 | DOI Listing |
J Med Chem
January 2025
Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom.
This Perspective summarizes successful fragment-to-lead (F2L) studies that were published in 2023 and is the ninth installment in an annual series. A tabulated summary of the relevant articles published in 2023 is provided (17 entries from 16 articles), and a comparison of the target classes, screening methods, and overall fragment or lead property trends for 2023 examples and for the combined entries over the years 2015-2023 is discussed. In addition, we identify several trends and innovations in the 2023 literature that promise to further increase the success of fragment-based drug discovery (FBDD), particularly in the areas of NMR and virtual screening, fragment library design, and fragment linking.
View Article and Find Full Text PDFBMC Chem
January 2025
LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, 4169-007, Portugal.
Mood disorders affect the daily lives of millions of people worldwide. The search for more efficient therapies for mood disorders remains an active field of research. In silico approaches can accelerate the search for inhibitors against protein targets related to mood disorders.
View Article and Find Full Text PDFJ Biomol Struct Dyn
December 2024
Amity Institute of Biotechnology, Amity University, Kolkata, India.
The first FDA approved, MDR-TB inhibitory drug bedaquiline (BDQ), entraps the c-ring of the proton-translocating F region of enzyme ATP synthase of , thus obstructing successive ATP production. Present-day BDQ-resistance has been associated with cardiotoxicity and mutation(s) in the atpE gene encoding the c subunit of ATP synthase (ATPc) generating five distinct ATPc mutants: Ala63→Pro, Ile66→Met, Asp28→Gly, Asp28→Val and Glu61→Asp. We created three discrete libraries, first by repurposing bedaquiline via scaffold hopping approach, second one having natural plant compounds and the third being experimentally derived analogues of BDQ to identify one drug candidate that can inhibit ATPc activity more efficiently with less toxic properties.
View Article and Find Full Text PDFJ Cheminform
December 2024
Department of Chemistry, Seoul National University, Seoul, 08826, Republic of Korea.
The two key components of computational molecular design are virtually generating molecules and predicting the properties of these generated molecules. This study focuses on an effective method for molecular generation through virtual synthesis and global optimization of a given objective function. Using a pre-trained graph neural network (GNN) objective function to approximate the docking energies of compounds for four target receptors, we generated highly optimized compounds with 300-400 times less computational effort compared to virtual compound library screening.
View Article and Find Full Text PDFInt J Mol Sci
November 2024
Center for Research and Education in Nanobioengineering, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
The complexities inherent in drug development are multi-faceted and often hamper accuracy, speed and efficiency, thereby limiting success. This review explores how recent developments in machine learning (ML) are significantly impacting target-based drug discovery, particularly in small-molecule approaches. The Simplified Molecular Input Line Entry System (SMILES), which translates a chemical compound's three-dimensional structure into a string of symbols, is now widely used in drug design, mining, and repurposing.
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