937 research outputs found
Enrichment Factor Analyses on G-Protein Coupled Receptors with Known Crystal Structure
No full textG-protein coupled receptors (GPCRs) are highly relevant drug targets. Four GPCRs with known crystal structure were analyzed with docking (AutoDock4) and postdocking (MM-PBSA) in order to evaluate the ability to recognize known antagonists from a larger database of molecular decoys and to predict correct binding modes. Moreover, implications on multitarget drug screening are put forward. The results suggest that these methods may be of interest to the growing field of GPCR structure-based virtual screening
Identification of Target Associations for Polypharmacology from Analysis of Crystallographic Ligands of the Protein Data Bank
No full textThe design of a chemical entity that potently and selectively binds to a biological target of therapeutic relevance has dominated the scene of drug discovery so far. However, recent findings suggest that multitarget ligands may be endowed with superior efficacy and be less prone to drug resistance. The Protein Data Bank (PDB) provides experimentally validated structural information about targets and bound ligands. Therefore, it represents a valuable source of information to help identifying active sites, understanding pharmacophore requirements, designing novel ligands, and inferring structure-activity relationships. In this study, we performed a large-scale analysis of the PDB by integrating different ligand-based and structure-based approaches, with the aim of identifying promising target associations for polypharmacology based on reported crystal structure information. First, the 2D and 3D similarity profiles of the crystallographic ligands were evaluated using different ligand-based methods. Then, activity data of pairs of similar ligands binding to different targets were inspected by comparing structural information with bioactivity annotations reported in the ChEMBL, BindingDB, BindingMOAD, and PDBbind databases. Afterward, extensive docking screenings of ligands in the identified cross-targets were made in order to validate and refine the ligand-based results. Finally, the therapeutic relevance of the identified target combinations for polypharmacology was evaluated from comparison with information on therapeutic targets reported in the Therapeutic Target Database (TTD). The results led to the identification of several target associations with high therapeutic potential for polypharmacology
Molecular docking: Shifting paradigms in drug discovery
No full textMolecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence
Trends and Applications in Computationally Driven Drug Repurposing
No full text: Drug repurposing is a widely used approach originally developed to aid in the identification of new uses of already existing drugs outside the scope of the original medical indication [...]
Refinement and rescoring of virtual screening results
No full textHigh-throughput docking is an established computational screening approach in drug design. This methodology enables a rapid identification of biologically active hit compounds, providing an efficient and cost-effective complement or alternative to experimental high-throughput screenings. However, limitations inherent to the methodology make docking results inevitably approximate. Two major Achille’s heels include the use of approximated scoring functions and the limited sampling of the ligand-target complexes. Therefore, docking results require careful evaluation and further post-docking analyses. In this article, we will overview our approach to post-docking analysis in virtual screenings. BEAR (Binding Estimation After Refinement) was developed as a post-docking processing tool that refines docking poses by means of molecular dynamics (MD) and then rescores the ligands based on more accurate scoring functions (MM-PB(GB)SA). The tool has been validated and used prospectively in drug discovery applications. Future directions regarding refinement and rescoring in virtual screening are discussed
Improving enrichment and hit rates in virtual screening.
No full textAlthough molecular docking is a widely used technique in drug discovery for the identification of hits or lead molecules, the method still retains important weaknesses and limitations. For example, docking techniques still lack reliable simulation of the flexibility of both ligands and receptor, and scoring functions may fail to estimate ligand binding energies in reasonable agreement with experiment. In light of these observations, we focused our efforts on the development, validation and application of a new post-docking tool aimed at overcoming some of these limitations.
The method, named BEAR (Binding Estimation After Refinement), is a new automated post-docking procedure based on the conformational refinement of docking poses through molecular dynamics (MD) followed by accurate predictions of protein binding free energies using MM-PBSA and MM-GBSA
Synthesis and aldose reductase inhibitory activity of a new series of benzo[h]cinnolinone derivatives
No full textFollowing our previous studies on pyridazinone carboxylic acids as potent and selective aldose reductase (ALR2) inhibitors, a new series of benzo[h]cinnolinone carboxylic acids, variously substituted at the positions 4, 7-10 and differently modified both at the central ring and at the acidic side chain, were synthesized and tested as inhibitors of ALR2. Comparison with previously synthesized compounds allows us to define more precisely structure-activity relationships for this class of compounds. In fact, in addition to the importance of the acidic side chain, their properties are highly influenced by the substituents present on the benzo[h]cinnolinone nucleous, with potency ranging from that of Sorbinil to very weakly active compounds
Corrigendum to "Theoretical analysis of the addition of hydroxylamine to uracil and 5-fluorouracil as a model for the thymidylate synthase reaction" [J. Mol. Struct. (Theochem) 343 (1995) 1-9]
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