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Pharmacophore Modeling in Drug Discovery and Development: An Overview
Abstract and Figures
Pharmacophore mapping is one of the major elements of drug design in the absence of structural data of the target receptor. The tool initially applied to discovery of lead molecules now extends to lead optimization. Pharmacophores can be used as queries for retrieving potential leads from structural databases (lead discovery), for designing molecules with specific desired attributes (lead optimization), and for assessing similarity and diversity of molecules using pharmacophore fingerprints. It can also be used to align molecules based on the 3D arrangement of chemical features or to develop predictive 3D QSAR models. This review begins with a brief historical overview of the pharmacophore evolution followed by a coverage of the developments in methodologies for pharmacophore identification over the period from inception of the pharmacophore concept to recent developments of the more sophisticated tools such as Catalyst, GASP, and DISCO. In addition, we present some very recent successes of the widely used pharmacophore generation methods in drug discovery.
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... 96 This method uses data or features of a set of compounds known to attach a targeted protein in order to predict other compounds with similar features. 97 The promising antibacterial activity of the compounds H1 and H2 observed in vitro and in silico analyses encouraged us to develop a ligand-based pharmacophore model from these two training molecules. The developed pharmacophore query shown in Figure 14 includes combined features selected from the two compounds H1 and H2: An acceptor/donor H-bond region and a hydrophobic region. ...
In this paper, we report the synthesis and the structure–activity relationship study of three hydrazone analogs; the Schiff base hydrazone SBH and 2, 4-dinitrophenylhydrazones H1 & H2 derived from (E)-chalcones, to identify the active fragment of each structure. This identification has been carried out following in vitro biological evaluation, which revealed that the analogs H1 and H2 showed significant antibacterial activity due to their (E)-chalcone fragments characterized by proton NMR data and demonstrated by the docked view with emphasis on the involvement of these moieties in the interaction with the DNA gyrase, and thus contributes to the pharmacophore modeling. At the same time, SBH exhibited the highest free radical DPPH scavenging power associated with hydrogen bonding and conjugated push−pull chromophores, which were elucidated by reported vibrational assignments and absorption spectra. The DFT optimizations gave rise to non-planar and distorted structures around the hydrazone group with comparable geometrical parameters. The chemical descriptors predict comparable biological activities, while the BDE necessary for the H-abstraction indicated the best antioxidant activity for the Schiff base hydrazone SBH compound.
2, 4-Dinitrophenylhydrazone analogs with (E)-chalcone and/or push-pull moieties were synthesized and characterized. The in-vitro and in-silico studies were carried out both to find the structure-activity relationship and to model the pharmacophore.
... The generation of receptor-ligand pharmacophores can reveal molecular features by converting protein properties into reciprocal ligand spaces. Hence, pharmacophore modeling enables the effective design of ligands with properties necessary for effective binding to a specific protein, [41] and numerous studies have reported that this method can accelerate the drug discovery process [42][43][44][45][46]. Thus, pharmacophore-based screening, known for its efficacy in identifying novel inhibitors that potentially exhibit biological activity [39,40,47,48,49], was employed in this study. ...
Mycobacteria are causative agents of tuberculosis (TB), which is a global health concern. Drug-resistant TB strains are rapidly emerging, thereby necessitating the urgent development of new drugs. Two-component signal transduction systems (TCSs) are signaling pathways involved in the regulation of various bacterial behaviors and responses to environmental stimuli. Applying specific inhibitors of TCSs can disrupt bacterial signaling, growth, and virulence, and can help combat drug-resistant TB. We conducted a comprehensive pharmacophore-based inhibitor screening and biochemical and biophysical examinations to identify, characterize, and validate potential inhibitors targeting the response regulators PhoP and MtrA of mycobacteria. The constructed pharmacophore model Phar-PR-n4 identified effective inhibitors of formation of the PhoP–DNA complex: ST132 (IC50 = 29 ± 1.6 µM) and ST166 (IC50 = 18 ± 1.3 µM). ST166 (KD = 18.4 ± 4.3 μM) and ST132 (KD = 14.5 ± 0.1 μM) strongly targeted PhoP in a slow-on, slow-off manner. The inhibitory potency and binding affinity of ST166 and ST132 for MtrAC were comparable to those of PhoP. Structural analyses and molecular dynamics simulations revealed that ST166 and ST132 mainly interact with the α8-helix and C-terminal β-hairpin of PhoP, with functionally essential residue hotspots for structure-based inhibitor optimization. Moreover, ST166 has in vitro antibacterial activity against Macrobacterium marinum. Thus, ST166, with its characteristic 1,2,5,6-tetrathiocane and terminal sulphonic groups, has excellent potential as a candidate for the development of novel antimicrobial agents to combat pathogenic mycobacteria.
... The use of SBPs in virtual high-throughput screening is adept at efficiently identifying potential ligands with a high binding affinity to a specific protein, thereby expediting the drug discovery process. Notably, pharmacophore-based inhibitor screening demonstrates significant power in screening novel compounds that satisfy pharmacophore requirements and exhibit potential biological activity (Gao et al., 2010;Guner, 2005;Khedkar et al., 2007;Lu et al., 2018;Yang, 2010). The current availability of the AsfvPolX-DNA complex's complex structure (PDB ID: 5HRB) facilitates the application of CADD for the screening and development of potent inhibitors. ...
The heightened transmissibility and capacity of African swine fever virus (ASFV) induce fatal diseases in domestic pigs and wild boars, posing significant economic repercussions and global threats. Despite extensive research efforts, the development of potent vaccines or treatments for ASFV remains a persistent challenge. Recently, inhibiting the AsfvPolX, a key DNA repair enzyme, emerges as a feasible strategy to disrupt viral replication and control ASFV infections. In this study, a comprehensive approach involving pharmacophore-based inhibitor screening, coupled with biochemical and biophysical analyses, were implemented to identify, characterize, and validate potential inhibitors targeting AsfvPolX. The constructed pharmacophore model, Phar-PolX-S, demonstrated efficacy in identifying a potent inhibitor, D-132 (IC50 = 2.8 ± 0.2 µM), disrupting the formation of the AsfvPolX-DNA complex. Notably, D-132 exhibited strong binding to AsfvPolX (KD = 6.9 ± 2.2 µM) through a slow-on-fast-off binding mechanism. Employing molecular modeling, it was elucidated that D-132 predominantly binds in-between the palm and finger domains of AsfvPolX, with crucial residues (R42, N48, Q98, E100, F102, and F116) identified as hotspots for structure-based inhibitor optimization. Distinctively characterized by a 1,2,5,6-tetrathiocane with modifications at the 3 and 8 positions involving ethanesulfonates, D-132 holds considerable promise as a lead compound for the development of innovative agents to combat ASFV infections.
... [13] When there is lack of structural data of the target receptor, pharmacophore mapping is used. [14] Indian traditional knowledge of Ayurveda gives us advantage of having phytochemicals as anti-microbial agents. Drug discovery based on Ayurveda follows a 'Reverse Pharmacology' path from Clinics to Laboratories. ...
... The pharmacophore models are also employed to identify molecules that meet the requirements of pharmacophore requirements before, during and after docking (Khedkar et al., 2007). The pharmacophore based modeling was started using BIOVIA Discovery Studio Visualizer Tool 2021 (Design, 2014) with protein preparation by adding hydrogen atoms and removing water molecules. ...
QSAR modelling is a powerful technique widely used in the discovery and development of new drugs and chemicals.
The approach involves the use of mathematical and statistical techniques to develop models that relate biological activity to the chemical structure of compounds. An effective strategy for QSAR modelling requires an understanding of the descriptors that are used to represent the chemical structure of compounds and the selection of appropriate prediction algorithms. In this review, we were discussed the importance of developing a well-thought-out strategy for QSAR modelling, including the strategy of appropriate descriptors and physiochemical parameters were used for QSAR. We were also provided the review on the current strategies for conducting QSAR studies and discussing their parameters, descriptors and applications in drug discovery, toxicity testing, and more.
This finding suggests how to develop an effective QSAR modelling approach that can be used to accurately predict the activity of new compounds.
Today, the world of science is constantly challenged with new genomics, which in turn is responsible for new disease-causing targets. Hence, there is a need for developing drugs acting against such targets. Computational methods are proving to be a mainstay in the drug discovery process, mainly through virtual screening. This review discusses about the recent advancements in structure-based drug design with reference to Virtual Screening along with its procedures from ligand preparation and protein preparation, docking, scoring function, databases, and virtual (VS) algorithms. Application of Structure-based VS in combination with other virtual screening techniques has also been highlighted in this review.
Background:
Colorectal cancer (CRC) is considered the second deadliest cancer in the world. One of the reasons for the occurrence of this cancer is the deregulation of the Epidermal Growth Factor Receptor (EGFR), which plays a critical role in regulating cell division, persistence, differentiation, and migration. The overexpression of the EGFR protein leads to its dysregulation and causes CRC.
Objective:
Hence, this work aims to identify and validate novel EGFR inhibitors for the treatment of colorectal cancer employing various computer aided techniques such as pharmacophore modeling, docking, molecular dynamic simulation and Quantitative Structure-Activity Relationship (QSAR) analysis.
Methods:
In this work, a shared-featured ligand-based pharmacophore model was generated using the known inhibitors of EGFR. The best model was validated and screened against ZincPharmer and Maybridge databases, and 143 hits were obtained. Pharmacokinetic and toxicological properties of these hits were studied, and the acceptable ligands were docked against EGFR. The best five protein-ligand complexes with binding energy less than -5 kcal/mol were selected. The molecular dynamic simulation studies of these complexes were conducted for 100 nanoseconds (ns), and the results were analyzed. The biological activity of this ligand was calculated using QSAR analysis.
Results:
The best complex with Root Mean Square Deviation (RMSD) 3.429 A0 and Radius of Gyration (RoG) 20.181 A0 was selected. The Root Mean Square Fluctuations (RMSF) results were also found to be satisfactory. The biological activity of this ligand was found to be 1.38 µM.
Conclusion:
This work hereby proposes the ligand 2-((1,6-dimethyl-4-oxo-1,4-dihydropyridin-3-yl)oxy)-N- (1H-indol-4-yl)acetamide as a potential EGFR inhibitor for the treatment of colorectal cancer. The wet lab analysis must be conducted, however, to confirm this hypothesis.
Designing and developing new drugs is an expensive and time-consuming process, and there is a need to discover new tools or approaches that can optimize this process. Applied Computer-Aided Drug Design: Models and Methods compiles information about the main advances in computational tools for discovering new drugs in a simple and accessible language for academic students to early career researchers. The book aims to help readers understand how to discover molecules with therapeutic potential by bringing essential information about the subject into one volume.
Key Features
. Presents the concepts and evolution of classical techniques, up to the use of modern methods based on computational chemistry in accessible format.
. Gives a primer on structure- and ligand-based drug design and their predictive capacity to discover new drugs.
. Explains theoretical fundamentals and applications of computer-aided drug design.
. Focuses on a range of applications of the computations tools, such as molecular docking; molecular dynamics simulations; homology modeling, pharmacophore modeling, quantitative structure-activity relationships (QSAR), density functional theory (DFT), fragment-based drug design (FBDD), and free energy perturbation (FEP).
. Includes scientific reference for advanced readers
Die Chemie ist ein Wissensgebiet, das besonders geeignet ist, mit Hilfe des Computers neue Möglichkeiten der Problemlösung zu erschließen. In diesem Tagungsband sind diejenigen Vorträge des Workshops "Software-Entwicklung in der Chemie" gesammelt, für die Manuskripte oder Zusammenfassungen eingingen.
This paper describes a new approach to building molecular models using reasoning by analogy. We apply symbolic reasoning to a problem previously only approached numerically. AIMB mimics the behavior of expert chemists: the more knowledge AIMB has, the faster and more accurate the models AIMB builds. AIMB has rules of analogy in the program, but all knowledge of geometry is in the knowledge base of known 3D structures. The method described here generates minimum energy models without minimization and is significantly faster than the fastest numerical minimization methods. MolFiles of models produced by AIMB and of the corresponding crystal structures are included on disk 4.
A computer program, called MAXFIT, has been developed for the automated recognition of the largest common substructure among a diverse set of chemical structures. This approach is based on a substructure search technique. Five different search levels which were defined with weighted graph representation of the chemical structures are available in the search. The search level can be specified interactively. Applications to structure-activity problems, including sweetners and opiates are presented to illustrate the abilities of the program.
Several pharmacophore models were previously formulated to account for the actions of nicotinic acetylcholinergic (nACh) agents. Most of these models were developed without express consideration of specific radioligand binding data because such data were not available at the time the models were described. In this review, the ability of these models to account for the binding of nicotinic agents at α4β2 nACh receptors (or rat brain receptors for which α4β2 receptors are the major component) is assessed. It seems that none of the early models can adequately explain the binding of these agents as a group. Furthermore, different series of nicotinic agents behave differently depending upon the nature of terminal amine substituents and the spacer that separates the amine from the pyridine ring. A region of bulk tolerance has been identified that accommodates substituents on some nicotinic ligands, but not the same substituents at seemingly corresponding locations of others. The concept of multiple modes of binding has been previously raised and, clearly, cannot yet be discarded. Nevertheless, new vector models seemingly provide a better picture of nACh receptor binding and account for many of the shortcomings associated with the earlier models.
Preclinical and clinical studies on cyclic nucleotide phosphodiesterases 4 (PDE4) inhibitors showed that these agents might be employed in the treatment of allergic diseases, in particular asthma. Unfortunately, many of these compounds such as rolipram, which belongs to the so-called “first generation”, showed undesirable side effects such as nausea and emesis. Efforts to eliminate these adverse side effects prompted the synthesis of a “second generation” of PDE4 inhibitors, with improved selectivity towards the enzyme catalytic site. So as to refine the pharmacophoric models of the catalytic site previously described in literature and better define the structural requirements which are essential for potent and selective PDE4 inhibition, we undertook the present computational study. DISCO approach was applied to generate an optimal alignment for a set of structurally diverse selective inhibitors 1–18 chosen from literature. The resulting superimposition of common pharmacophoric elements was refined by evaluating molecular field properties. A rational pharmacophoric model of the enzyme active site was thus derived and tested for its ability in predicting the degree of potency for a novel ligand. The comparison of the pharmacophoric areas common to cAMP, the natural substrate of the enzyme, and the most selective inhibitors was performed so as to better understand the binding mode of PDE4 selective inhibitors in the catalytic site.
The development of similarity methods for fast flexible ligand superposition has recently received considerable attention.
These efforts have brought similarity methods to a level of performance comparable to the well established protein-ligand
docking methods for binding mode assessment and molecular database screening. However, the strengths and intrinsic limitations
of both methodologies have been also stressed out extensively. As the number of resolved ligand-bound protein structures increases,
combining ligand-based and receptor-based approaches emerges as a consensus strategy to maximally exploit the structural information
available and improve the results obtained with either of the methods alone.
The dual acting α, β-blockers have an important place in the management of hypertension. Molecular dynamics simulations have
been carried out on all stereoisomers of seven dual acting α, β-blockers namely adimolol, amosulalol, bucindolol, carvedilol,
labetalol, medroxalol and primidolol. Three families of conformations have been identified for the group of compounds. The
pharmacophores for α and β-activity have been constructed for two of these families.