Motivation: Structure-based drug discovery methods exploit protein structural information to design small molecules binding to given protein pockets. This work proposes a purely data driven, structure-based approach for imaging ligands as spatial fields in target protein pockets. We use an end-to-end deep learning framework trained on experimental protein-ligand complexes with the intention of mimicking a chemist's intuition at manually placing atoms when designing a new compound. We show that these models can generate spatial images of ligand chemical properties like occupancy, aromaticity and donor-acceptor matching the protein pocket.
Results: The predicted fields considerably overlap with those of unseen ligands bound to the target pocket. Maximization of the overlap between the predicted fields and a given ligand on the Astex diverse set recovers the original ligand crystal poses in 70 out of 85 cases within a threshold of 2 Å RMSD. We expect that these models can be used for guiding structure-based drug discovery approaches.
Availability And Implementation: LigVoxel is available as part of the PlayMolecule.org molecular web application suite.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/bty583 | DOI Listing |
Chem Biodivers
December 2024
University of Nizwa, Chemsitry, University of Nizwa, Nizwa, Oman, NIzwa, 616, NIzwa, OMAN.
Two new (1, 2) and nine known (3-11) compounds were isolated from the rutaceous plant Haplophyllum tuberculatum and characterised by extensive NMR spectroscopic techniques and HR-ESI-MS. After structural elucidation, nine compounds were evaluated for their ability to inhibit α-glucosidase, a target for the treatment of type-2 diabetes. Among them, three compounds (7, 5 and 1) exhibited noteable inhbition with IC50 values of 3.
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Department of Biological Sciences, Sunandan Divatia School of Science, SVKM's Narsee Monjee Institute of Management Studies (NMIMS) Deemed-to-be University, Mumbai, Maharashtra, India.
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Department of Science and Technology, Virology and Vaccine Research Program, Industrial Technology Development Institute, Taguig City, Philippines.
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December 2024
Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas 75390, United States.
Malaria remains a serious global health challenge, yet treatment and control programs are threatened by drug resistance. Dihydroorotate dehydrogenase (DHODH) was clinically validated as a target for treatment and prevention of malaria through human studies with DSM265, but currently no drugs against this target are in clinical use. We used structure-based computational tools including free energy perturbation (FEP+) to discover highly ligand efficient, potent, and selective pyrazole-based DHODH inhibitors through a scaffold hop from a pyrrole-based series.
View Article and Find Full Text PDFJ Chem Inf Model
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Orion Pharma, Orionintie 1A, 02101 Espoo, Finland.
Given the size of the relevant chemical space for drug discovery, working with fully enumerated compound libraries (especially in three-dimensional (3D)) is unfeasible. Nonenumerated virtual chemical spaces are a practical solution to this issue, where compounds are described as building blocks which are then connected by rules. One concrete example of such is the BioSolveIT chemical spaces file format (.
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