Protein-ligand structures are the core data required for structure-based drug design (SBDD). Understanding the error present in this data is essential to the successful development of SBDD tools. Methods for assessing protein-ligand structure quality and a new set of identification criteria are presented here. When these criteria were applied to a set of 728 structures previously used to validate molecular docking software, only 17% were found to be acceptable. Structures were re-refined to maintain internal consistency in the comparison and assessment of the quality criteria. This process resulted in Iridium, a highly trustworthy protein-ligand structure database to be used for development and validation of structure-based design tools for drug discovery.
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http://dx.doi.org/10.1016/j.drudis.2012.06.011 | DOI Listing |
J Cheminform
January 2025
Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK.
Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that 'stitches' the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein-ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Chemical and Physical Biology, Vanderbilt University, Nashville, Tennessee 37232, United States.
Machine learning (ML) models now play a crucial role in predicting properties essential to drug development, such as a drug's logscale acid-dissociation constant (p). Despite recent architectural advances, these models often generalize poorly to novel compounds due to a scarcity of ground-truth data. Further, these models lack interpretability.
View Article and Find Full Text PDFPhys Chem Chem Phys
January 2025
Center for Advanced Materials Research, Beijing Normal University at Zhuhai, Zhuhai, 519087, China.
Understanding the molecular mechanism of inhibitor binding to prostate-specific membrane antigen (PSMA) is of fundamental importance for designing targeted drugs for prostate cancer. Here we designed a series of PSMA-targeting inhibitors with distinct molecular structures, which were synthesized and characterized using both experimental and computational approaches. Microsecond molecular dynamics simulations revealed the structural and thermodynamic details of PSMA-inhibitor interactions.
View Article and Find Full Text PDFChem Biol Interact
January 2025
Department of Informatics and Information Science, University of Konstanz, Germany; Faculty of Information Technology, Monash University, Australia. Electronic address:
Microcystins (MCs) occur frequently during cyanobacterial blooms worldwide, representing a group of currently about 300 known MC congeners, which are structurally highly similar. Human exposure to MCs via contaminated water, food or dietary supplements can lead to severe intoxications with ensuing high morbidity and in some cases mortality. Currently, one MC congener (MC-LR) is almost exclusively considered for risk assessment (RA) by the WHO.
View Article and Find Full Text PDFJ Med Chem
January 2025
Hangzhou Carbonsilicon AI Technology Company Limited, Hangzhou 310018, Zhejiang, China.
Applying artificial intelligence techniques to flexibly model the binding between the ligand and protein has attracted extensive interest in recent years, but their applicability remains improved. In this study, we have developed CarsiDock-Flex, a novel two-step flexible docking paradigm that generates binding poses directly from predicted structures. CarsiDock-Flex consists of an equivariant deep learning-based model termed CarsiInduce to refine ESMFold-predicted protein pockets with the induction of specific ligands and our existing CarsiDock algorithm to redock the ligand into the induced binding pockets.
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