Next generation 3D pharmacophore modeling

3D pharmacophore models are three‐dimensional ensembles of chemically defined interactions of a ligand in its bioactive conformation. They represent an elegant way to decipher chemically encoded ligand information and have therefore become a valuable tool in drug design. In this review, we provide an overview on the basic concept of this method and summarize key studies for applying 3D pharmacophore models in virtual screening and mechanistic studies for protein functionality. Moreover, we discuss recent developments in the field. The combination of 3D pharmacophore models with molecular dynamics simulations could be a quantum leap forward since these approaches consider macromolecule–ligand interactions as dynamic and therefore show a physiologically relevant interaction pattern. Other trends include the efficient usage of 3D pharmacophore information in machine learning and artificial intelligence applications or freely accessible web servers for 3D pharmacophore modeling. The recent developments show that 3D pharmacophore modeling is a vibrant field with various applications in drug discovery and beyond

STRUCTURAL EXPLORATION AND PHARMACOPHORIC INVESTIGATION OF PYRAZOLE BASED ANALOGS AS NOVEL HISTONE DEACETYLASE 1 INHIBITOR USING COMBINATORIAL STUDIES

Objective: Histone deacetylase inhibitors (HDACi) have four essential pharmacophores as cap group, connecting unit, a linker moiety and zinc binding group for their anticancer and histone deacetylase (HDAC) inhibition activity. On the basis of this fact, the objective of this research was to evaluate the exact role of pyrazole nucleus as connecting unit and its role in the development of newer HDACi.Methods: Ligand and structure-based computer-aided drug design strategies such as pharmacophore and atom based 3D QSAR modelling, molecular docking and energetic based pharmacophore mapping have been frequently applied to design newer analogs in a precise manner. Herein, we have applied these combinatorial approaches to develop the structure-activity correlation among novel pyrazole-based derivatives.Results: the Pharmacophore-based 3D-QSAR model was developed employing Phase module and e-pharmacophore on compound 1. This 3D-QSAR model provides fruitful information regarding favourable and unfavourable substitution on pyrazole-based analogs for HDAC1 inhibition activity. Molecular docking studies indicated that all the pyrazole derivatives bind with HDAC1 proteins and showed critical hydrophobic interaction with 5ICN and 4BKX HDAC1 proteins.Conclusion: The outcome of the present research work clearly indicated that pyrazole nucleus added an essential hydrophobic feature in cap group and could be employed to design the ligand molecules more accurately

SCREENING AND MOLECULAR DOCKING STUDIES OF NEW NATURAL AGONISTS AGAINST PEROXISOME PROLIFERATOR-ACTIVATED RECEPTOR-ALPHA TARGETED TO TREAT OBESITY

ABSTRACTObjective: Obesity was considered as a serious health concern apart from the age group in today's population globally. The percentage of obese peoplein the world's population is increasing at a faster rate, and health issues arising due to obesity are gradually increasing. Our present insilico study wasaimed to screen out natural molecules against the peroxisome proliferator-activated receptor (PPAR), especially alpha aids in triggering the obesity.Methods: Several targets for treating obesity were identified, and one among such promising target was PPAR. Using the insilico applications such asnatural database was screened and the molecules were further evaluated based on their docking score parameter with the receptor.Results: The docking methodology suggested that two molecules zinc02091671 and zinc02137525 were found to reproduce the similar type ofinteractions such as that of the known inhibitor and crystal ligand.Conclusion: The reported two molecules were found to be promising agonists based on the computational studies and can be advanced the in vitrobased evaluation.Keywords: Obesity, Peroxisome proliferator-activated receptor, e-pharmacophore, QikProp, Docking

Rational Design of Small-Molecule Inhibitors of Protein-Protein Interactions: Application to the Oncogenic c-Myc/Max Interaction

Protein-protein interactions (PPIs) constitute an emerging class of targets for pharmaceutical intervention pursued by both industry and academia. Despite their fundamental role in many biological processes and diseases such as cancer, PPIs are still largely underrepresented in today's drug discovery. This dissertation describes novel computational approaches developed to facilitate the discovery/design of small-molecule inhibitors of PPIs, using the oncogenic c-Myc/Max interaction as a case study.First, we critically review current approaches and limitations to the discovery of small-molecule inhibitors of PPIs and we provide examples from the literature.Second, we examine the role of protein flexibility in molecular recognition and binding, and we review recent advances in the application of Elastic Network Models (ENMs) to modeling the global conformational changes of proteins observed upon ligand binding. The agreement between predicted soft modes of motions and structural changes experimentally observed upon ligand binding supports the view that ligand binding is facilitated, if not enabled, by the intrinsic (pre-existing) motions thermally accessible to the protein in the unliganded form.Third, we develop a new method for generating models of the bioactive conformations of molecules in the absence of protein structure, by identifying a set of conformations (from different molecules) that are most mutually similar in terms of both their shape and chemical features. We show how to solve the problem using an Integer Linear Programming formulation of the maximum-edge weight clique problem. In addition, we present the application of the method to known c-Myc/Max inhibitors.Fourth, we propose an innovative methodology for molecular mimicry design. We show how the structure of the c-Myc/Max complex was exploited to designing compounds that mimic the binding interactions that Max makes with the leucine zipper domain of c-Myc.In summary, the approaches described in this dissertation constitute important contributions to the fields of computational biology and computer-aided drug discovery, which combine biophysical insights and computational methods to expedite the discovery of novel inhibitors of PPIs

Study of ligand-based virtual screening tools in computer-aided drug design

Virtual screening is a central technique in drug discovery today. Millions of molecules can be tested in silico with the aim to only select the most promising and test them experimentally. The topic of this thesis is ligand-based virtual screening tools which take existing active molecules as starting point for finding new drug candidates. One goal of this thesis was to build a model that gives the probability that two molecules are biologically similar as function of one or more chemical similarity scores. Another important goal was to evaluate how well different ligand-based virtual screening tools are able to distinguish active molecules from inactives. One more criterion set for the virtual screening tools was their applicability in scaffold-hopping, i.e. finding new active chemotypes. In the first part of the work, a link was defined between the abstract chemical similarity score given by a screening tool and the probability that the two molecules are biologically similar. These results help to decide objectively which virtual screening hits to test experimentally. The work also resulted in a new type of data fusion method when using two or more tools. In the second part, five ligand-based virtual screening tools were evaluated and their performance was found to be generally poor. Three reasons for this were proposed: false negatives in the benchmark sets, active molecules that do not share the binding mode, and activity cliffs. In the third part of the study, a novel visualization and quantification method is presented for evaluation of the scaffold-hopping ability of virtual screening tools.Siirretty Doriast

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued e orts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature

Computer modeling of dapsone-mediated heteroactivation of flurbiprofen metabolism by CYP2C9

The occurrence of atypical kinetics in cytochrome P450 reactions can confound in vitro determinations of a drug\u27s kinetic parameters. During drug development, inaccurate kinetic parameter estimates can lead to incorrect decisions about a lead compound\u27s potential for success. It has become widely accepted that in certain CYP subfamilies more than one molecule can occupy the active site simultaneously, in some cases resulting in enhanced substrate turnover (heteroactivation). However, the specific mechanism(s) by which dual-compound binding results in heteroactivation remain unclear. It is known that orientation of the substrate in the active site, as dictated by interactions with active site residues, plays a large role in metabolic outcome. Effector compounds have been shown in vitro to alter substrate position in the active site. Here, data obtained via in silico methods including docking, molecular dynamics, semi-empirical and ab initio quantum mechanics indicate that direct interaction between effector and substrate can play a role in stabilizing the substrate in an alternative conformation conducive to oxidation. In this study a high-throughput screening computer model of heteroactivation of flurbiprofen metabolism by CYP2C9 has been developed for the purpose of elucidating key interactions between substrate, effector, and enzyme responsible for heteroactivation in this system, as well as to predict as yet unknown activators