DOCSLIB.ORG

A Computational Quantitative Structure-Activity Relationship Study of Carbamate Anticonvulsants Using Quantum Pharmacological Methods

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Seizure 1998; 7: 347-354 A computational quantitative structure-activity relationship study of carbamate anticonvulsants using quantum pharmacological methods JENNIFER L. KNIGHT* & DONALD F. WEAVER-f Department of Chemistry*, Department of Medicine? (Neurology), Queen’s University, Kingston, Ontario, Canada, K7L 3N6 Correspondence to: Donald F. Weaver, Department of Medicine (Neurology), Queen’s University, Kingston, Ontario, Canada, K7L 3N6. A pattern recognition quantitative structure-activity relationship (QSAR) study has been performed to determine the molecular features of carbamate anticonvulsants which influence biological activity. Although carbamates, such as felbamate, have been used to treat epilepsy, their mechanisms of efficacy and toxicity are not completely understood. Quantum and classical mechanics calculations have been exploited to describe 46 carbamate drugs. Employing a principal component analysis and multiple linear regression calculations, five crucial structural descriptors were identified which directly relate to the bioactivity of the carbamate family. With the resulting mathematical model, the biological activity of carbamate analogues can be predicted with 85-90% accuracy. Key words: felbamate, anticonvulsant, structure-activity. INTRODUCTION carbamate fragment capable of producing desired anticonvulsant biological effects. The pattern recognition approach to QSAR The underlying mechanisms of action for the attempts to determine reasons for biological agents used to treat epilepsy are incompletely activity in some compounds and inactivity in understood. For example, felbamate (2-phenyl- others within the same drug class. This approach 1,3-propanediol dicarbamate), a newly-developed assumes the following: (1) a wide variety of a potential anticonvulsant, has an unknown mecha- drug’s physical and chemical properties can be nism of actionim5; moreover, a number of patients reduced to numerical quantities, termed who received this medication died from either descriptors’, (2) the descriptors which influence hepatoxicity or aplastic anemia6. Thus, just as its activity can be determined and calculated’ and (3) mechanism of efficacy is unknown, felbamate’s a mathematical relationship between the descrip- mechanism of toxicity has likewise not been tors and the compound’s biological activity can be elucidated’-‘. A thorough molecular-level study described”,’ ‘. This knowledge assists in the early of felbamate and other carbamates may shed stages of research as the therapeutic potential of a light on these mechanisms. Quantitative given class of compounds is investigated. The structure-activity relationship (QSAR) studies simplicity of some of the descriptors and the are a technique which aid in identifying the availability of high-powered computers for calcu- structural features which are responsible for lating the large number of equations facilitate biological activity. This process, in turn, has the this in-depth structural analysis. In a competitive potential to uncover possible mechanisms of market, sensitive, yet economical devices are of action for efficacy and toxicity’. Therefore, the great importance. In this study, a set of focused goal of this study is to perform a pattern carbamates is thoroughly described and a recognition QSAR on carbamate anticonvulsants mathematical model is developed which predicts to identify the bioactive pharmacophore, i.e. the the activity or inactivity of individual analogues three-dimensional arrangement of atoms within a against an animal model of epilepsy. 1059-131 l/98/050347 + 08 $12.00/O 0 1998 British Epilepsy Association 348 J. L. Knight & D. F. Weaver MATERIALS AND METHODS molecular fragment (i.e. ‘the bioactive fragment’) which constituted the region of structural com- monality among active anticonvulsants. Background to methods The selection of descriptors capable of com- pletely encoding the significant and structural aspects of carbamate molecular structure was The correlation of biological activity with mole- crucial. The carbamate numbering scheme is cular structure is central to modem neurophar- shown in Fig. 1. The descriptors are listed in macology and enables an enhanced mechanistic Table 1, and are catagorized into four classes: understanding of drug action at the molecular geometric, electronic, topological and physio- level of reality. Accordingly, contemporary chemical (42 descriptors were employed in this approaches to the study and design of bioactive study). Geometric descriptors represent three- molecules are based on the notion that bioactivity dimensional properties and reflect aspects of may be related quantitatively to molecular molecular shape and size. The geometric descrip- structure through the application of QSAR tors employed in this study were derived from techniques’2-‘4. The establishment of QSAR theoretical quantum mechanics and molecular relationships for anticonvulsants has been hin- mechanics calculations. The carbamate analogues dered by the chemical diversity of the moelcules under study were conformationally and geomet- and by the complexity of the physiological and rically optimized using the AM1 Hamiltonian biochemical processes that initiate and propagate after a preliminary scanning of their conforma- seizure activity. tional space using multiple molecular mechanics A vareity of QSAR techniques are available, force field optimizations. Electronic descriptors including free energy models (Hansch such as charge densities, bond moments, orbital equation15”6), mathematical models (Free- energies and frontier electron densities, reflect Wilson equation”), statistical models” (dis- molecular properties arising from variable el- criminant analysis, cluster analysis), quantum ectron distribution through a compound’s str- mechanics and molecular mechanics methods”, uctural framework. The electronic descriptors topological methods2’ and pattern recognition used in this study were obtained from the methods2’-24. In recent years, the pattern recog- Mulliken population analyses of theoretical AM1 nition technique described by Stuper and Jurr?‘, semi-empirical quantum mechanics calculations. Kirschner and Kolalski26 and Wold et al”*24 has Topological descriptors encode aspects of mole- emerged as one of the most all-encompassing cular composition and connectivity, and describe and versatile techniques. patterns of interatomic interconnections that The initial objective of this theoretical pattern determine ultimate molecular architecture. The recognition study was to identify a theoretical topological descriptors used in this study were ‘classification rule’ capable of correctly categoriz- derived from graph theory calculations2635. In ing carbamate compounds into pharmacologically graph theory calculations, a graph G is a active and inactive classes based solely upon an examination of their structural properties. This problem was approached in two stages. First, a set (a) 04 (b) of carbamates of known biological activity was 0 selected and designated as the ‘training set’. A group of theoretical ‘descriptors’ was then gener- ated to describe each compound in this training set. Statistical techniques (regression analyses) were then employed to identify the minimal number of descriptors (i.e. the ‘essential descrip- (c) tor set’) capable of distinguishing active from inactive carbamates. This essential descriptor set -1 AN/H constitutes a classification algorithm or rule. This CH-CH.--0 classification rule was next applied to a second set Cb’ of carbamates, termed the ‘test set’, to evaluate the validity and predictability of the rule. As a Fig. 1: Carbtimate numbering scheme. Representations of corollary to developing a classification rule which the carbamates evaluated in the study. The generalized designated activity from inactivity, it was possible form is represented by A while f3 and C represent specific through complementarity arguments, to identify a analogues Cl and C2, respectively. A structure-activity relationship study of carbamate anticonvulsants 349 Table 1: List of descriptors the Schrodinger partial differential equation: Geometric descriptors (from AM1 semi-empirical W = W, where H is the Hamiltonian calculations) differential operator and Ic, is the wave function. 1-3 Bond lengths Molecular quantum mechanics calculations may NI-C2,C2-03,C2-04. be ab initio or semi-empirical. Semi-empirical 4-6 interatomic distances Nl -R, Nl - Rl, Nl - R2. calculations use a simpler Hamiltonian than the 7-9 Bond angles correct molecular Hamiltonian and incorporate Nl-C2-03,Nl-C2-04,03-C2-04. additional parameters designed to fit experimen- 10 Torsional angle tal data. In contrast, ab initio calculations use the Nl -C2-03-04. full correct Hamiltonian and attempt a solution Electronic descriptors (from AMI semi-empirical calculations) through a rigorous first principles treatment. 1 I-14 Atomic charge densities Classical mechanics force field calculation is Nl, C2, 03. 04. conceptually quite different: it is an empirical 15-16 Molecular dipole moments technique which employs multiple classical equa- 17 Heath of formation tions of motion to provide a priori geometries for 18 E HOMO-LUMO Topological descriptors (from graph theory calculations) varying conformations of large molecules. The 19-20 Zagreb topological indices sum of the individual classical equations constit- M,. M2 utes a multi-dimensional potential
Recommended publications
  • Pharmacophore Modeling and Applications in Drug Discovery

    Pharmacophore Modeling and Applications in Drug Discovery

    REVIEWS Drug Discovery Today Volume 15, Numbers 11/12 June 2010 Reviews Pharmacophore modeling and INFORMATICS applications in drug discovery: challenges and recent advances Sheng-Yong Yang State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan 610041, China Pharmacophore approaches have become one of the major tools in drug discovery after the past century’s development. Various ligand-based and structure-based methods have been developed for improved pharmacophore modeling and have been successfully and extensively applied in virtual screening, de novo design and lead optimization. Despite these successes, pharmacophore approaches have not reached their expected full capacity, particularly in facing the demand for reducing the current expensive overall cost associated with drug discovery and development. Here, the challenges of pharmacophore modeling and applications in drug discovery are discussed and recent advances and latest developments are described, which provide useful clues to the further development and application of pharmacophore approaches. Introduction lead optimization and multitarget drug design (Fig. 1). A variety of The concept of pharmacophore was first introduced in 1909 by automated tools for pharmacophore modeling and applications Ehrlich [1], who defined the pharmacophore as ‘a molecular appeared constantly after the advances in computational chem- framework that carries (phoros) the essential features responsible istry in the past 20 years; these pharmacophore modeling tools, for a drug’s (pharmacon) biological activity’. After a century’s together with their inventor(s) and typical characteristics, are development, the basic pharmacophore concept still remains summarized in Supplementary Table S1. Many successful stories unchanged, but its intentional meaning and application range of pharmacophore approaches in facilitating drug discovery have have been expanded considerably.
  • Database: a Brazilian Repository of Chemical and Biological Information for Drug Design

    Database: a Brazilian Repository of Chemical and Biological Information for Drug Design

    The Brazilian Compound Library (BraCoLi) database: a Brazilian repository of chemical and biological information for drug design Gabriel Corrêa Veríssimo1*, Valtair Severino dos Santos Júnior1*, Ingrid Ariela do Rosário de Almeida1, Marina Sant'Anna Mitraud Ruas1, Lukas Galuppo Coutinho1, Renata Barbosa de Oliveira1, Ricardo José Alves1, Vinícius Gonçalves Maltarollo1‡ 1. Laboratório de Química Farmacêutica, Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. *The authors equally contributed to the manuscript ‡Correspondence author: [email protected] ABSTRACT The Brazilian Compound Library (BraCoLi) is a novel virtual library of manually curated compounds developed by Brazilian research groups to support further computer-aided drug design works. Herein, the first version of the database is described comprising 1,176 compounds. Also, the chemical diversity and drug-like profile of BraCoLi were defined to analyze its chemical space. A significant amount of the compounds fitted Lipinski and Veber’s rules, alongside other drug-likeness properties. Principal component analysis showed that BraCoLi is similar to other databases (FDA-approved drugs and NuBBEDB) regarding structural and physicochemical patterns. Finally, a scaffold analysis showed that BraCoLi presents several privileged chemical skeletons with great diversity. Keywords: drug design, database, chemical library, medicinal chemistry, cheminformatics. INTRODUCTION The application of computational tools as an ally in drug design was an important milestone in medicinal chemistry. This approach is known as computer-aided drug design (CADD) and it is extensively used in several studies to optimize the discovery and design of new drug candidates1,2. Molecular docking, structure-activity relationship 1 (SAR) studies, and virtual screening are a few examples of available computational techniques that are widely employed in drug design3.
  • Swissadme: a Free Web Tool to Evaluate Pharmacokinetics, Drug

    Swissadme: a Free Web Tool to Evaluate Pharmacokinetics, Drug

    www.nature.com/scientificreports OPEN SwissADME: a free web tool to evaluate pharmacokinetics, drug- likeness and medicinal chemistry Received: 05 October 2016 Accepted: 13 January 2017 friendliness of small molecules Published: 03 March 2017 Antoine Daina1, Olivier Michielin1,2,3 & Vincent Zoete1 To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED- Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours. During the time- and resource-consuming processes of drug discovery and development, a large number of molecular structures are evaluated according to very diverse parameters in order to steer the selection of which chemicals to synthetize, test and promote, with the final goal to identify those with the best chance to become an effective medicine for the patients.
  • Ligand Based 3D-QSAR Pharmacophore, Molecular Docking and ADME to Identify Potential Broblast Growth Factor Receptor 1 Inhibitor

    Ligand Based 3D-QSAR Pharmacophore, Molecular Docking and ADME to Identify Potential Broblast Growth Factor Receptor 1 Inhibitor

    Ligand based 3D-QSAR pharmacophore, molecular docking and ADME to identify potential broblast growth factor receptor 1 inhibitors Zizhong Tang Sichuan Agricultural University Lu Huang Sichuan Agricultural University Xiaoli Fu Sichuan Agricultural University Haoxiang Wang Sichuan Agricultural University Biao Tang Sichuan Agricultural University Yirong Xiao Sichuan Agricultural University Hospital Caixia Zhou Sichuan Agricultural University Zhiqiao Zhao Sichuan Agricultural University Yujun Wan Sichuan food feimentation industry research and design institute Hui Chen ( [email protected] ) Sichuan Agricultural University Huipeng Yao Sichuan Agricultural University Zhi Shan Sichuan Agricultural University Tongliang Bu Sichuan Agricultural University Xulong Wu Chengdu Agricultural College Research Page 1/27 Keywords: FGFR1, Inhibitor, molecular docking, pharmacophore, ADME Posted Date: September 1st, 2020 DOI: https://doi.org/10.21203/rs.3.rs-64361/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 2/27 Abstract Background The FGF/FGFR system may affect tumor cells and stromal microenvironment through autocrine and paracrine stimulation, thereby signicantly promoting oncogene transformation and tumor growth. Abnormal expression of FGFR1 in cells is considered to be the main cause of tumorigenesis and a potential target for the treatment of cancer. Methods The known inhibitors were collected to construct 3D-QSAR pharmacophore model, which was veried by cost analysis, test set validation and Fischer test. Virtual screening of zinc database based on pharmacophore was carried out. FGFR1 crystal complex was downloaded from the protein database to dock with the compound. Finally, the absorption, distribution, metabolism and excretion (ADME) characteristics and toxicity of a series of potential inhibitors were studied.
  • Importance of ADME and Bioanalysis in the Drug Discovery

    Importance of ADME and Bioanalysis in the Drug Discovery

    alenc uiv e & eq B io io B a f v o a i l l a Journal of a b Vuppala et al., J Bioequiv Availab 2013, 5:4 n r i l i u t y o DOI: 10.4172/jbb.10000e31 J ISSN: 0975-0851 Bioequivalence & Bioavailability EditorialResearch Article OpenOpen Access Access Importance of ADME and Bioanalysis in the Drug Discovery Pradeep K Vuppala1*, Dileep R Janagam2 and Pavan Balabathula2 1Preclinical Pharmacokinetics Shared Resource, St. Jude Children’s Research Hospital, Memphis, TN, USA 2University of Tennessee Health Sciences Center, Memphis, TN, USA Editorial Bioanalytical support plays a vital role during the lead optimization stages. The major goal of the bioanalysis is to assess the over-all The hunt for new drugs can be divided into two stages: discovery ADME characteristics of the new chemical entities (NCE’s). Arrays and development. Drug discovery includes generating a hypothesis of of bioanalytical methods are required to completely describe the the target receptor for a particular disorder and screening the in vitro pharmacokinetic behavior in laboratory animals as well as in humans and/or in vivo biological activities of the new drug candidates. Drug [7]. Bioanalytical tools can play a significant role for the progress development involves the assessment of efficacy and toxicity of the new in drug discovery and development. Physiologic fluids such as blood, drug candidates. serum, plasma, urine and tissues are analyzed to determine the absorption and disposition of a drug candidate administered to a test To aid in a discovery program, accurate data on pharmacokinetics animal [8].
  • Natural Products As Leads to Potential Drugs: an Old Process Or the New Hope for Drug Discovery?

    Natural Products As Leads to Potential Drugs: an Old Process Or the New Hope for Drug Discovery?

    J. Med. Chem. 2008, 51, 2589–2599 2589 Natural Products as Leads to Potential Drugs: An Old Process or the New Hope for Drug Discovery? David J. Newman† Natural Products Branch, DeVelopmental Therapeutics Program, DCTD, National Cancer InstitutesFrederick, P.O. Box B, Frederick, Maryland 21702 ReceiVed April 5, 2007 I. Introduction From approximately the early 1980s, the “influence of natural products” upon drug discovery in all therapeutic areas apparently has been on the wane because of the advent of combinatorial chemistry technology and the “associated expectation” that these techniques would be the future source of massive numbers of novel skeletons and drug leads/new chemical entities (NCEa) where the intellectual property aspects would be very simple. As a result, natural product work in the pharmaceutical industry, except for less than a handful of large pharmaceutical compa- nies, effectively ceased from the end of the 1980s. Figure 1. Source of small molecule drugs, 1981–2006: major What has now transpired (cf. evidence shown in Newman categories, N ) 983 (in percentages). Codes are as in ref 1. Major and Cragg, 20071 and Figures 1 and 2 below showing the categories are as follows: “N”, natural product; “ND”, derived from a natural product and usually a semisynthetic modification; “S”, totally continued influence of natural products as leads to or sources synthetic drug often found by random screening/modification of an of drugs over the past 26 years (1981–2006)) is that, to date, existing agent; “S*”, made by total synthesis, but the pharmacophore there has only been one de novo combinatorial NCE approved is/was from a natural product.
  • Downloading Only Compounds with the Properties “Drug-Like”, “Purchasable”

    Downloading Only Compounds with the Properties “Drug-Like”, “Purchasable”

    bioRxiv preprint doi: https://doi.org/10.1101/2021.03.02.433618; this version posted June 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. A Pharmacophore Model for SARS-CoV-2 3CLpro Small Molecule Inhibitors and in Vitro Experimental Validation of Computationally Screened Inhibitors Enrico Glaab*†1, Ganesh Babu Manoharan2, Daniel Abankwa*2 1Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg 2Department of Life Sciences and Medicine, University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg *EG and DA are joint senior autHors †Contact: [email protected] Abstract Among the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied intensively. One of the main drug target proteins proposed so far is the SARS- CoV-2 viral protease 3CLpro (also called Mpro), an essential component for viral replication. Ongoing ligand- and receptor-based computational screening efforts would be facilitated by an improved understanding of the electrostatic, hydrophobic and steric features that characterize small molecule inhibitors binding stably to 3CLpro, as well as by an extended collection of known binders. Here, we present combined virtual screening, molecular dynamics simulation, machine learning and in vitro experimental validation analyses which have led to the identification of small molecule inhibitors of 3CLpro with micromolar activity, and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the 3CLpro binding pocket.
  • Quantifying Structure and Performance Diversity for Sets of Small Molecules

    Quantifying Structure and Performance Diversity for Sets of Small Molecules

    Quantifying structure and performance diversity SPECIAL FEATURE for sets of small molecules comprising small-molecule screening collections Paul A. Clemonsa,1, J. Anthony Wilsona, Vlado Dančíka,2, Sandrine Mullera, Hyman A. Carrinskia, Bridget K. Wagnera, Angela N. Koehlera, and Stuart L. Schreibera,b,c aBroad Institute of Harvard and MIT, 7 Cambridge Center, Cambridge, MA 02142; bHoward Hughes Medical Institute, 7 Cambridge Center, Cambridge, MA 02142; and cDepartment of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138 Edited by Jack Halpern, University of Chicago, Chicago, IL, and approved March 21, 2011 (received for review February 28, 2011) Using a diverse collection of small molecules we recently found access property distributions acceptable for certain goals despite that compound sets from different sources (commercial; academic; nonadherence to established rules. natural) have different protein-binding behaviors, and these beha- Comparative analyses of compound sets usually use computed viors correlate with trends in stereochemical complexity for these properties (19, 22, 23) or historical assay results (24, 25). Signifi- compound sets. These results lend insight into structural features cant progress has been made quantifying and visualizing proper- that synthetic chemists might target when synthesizing screening ties of compound sets (26), including methods that relate collections for biological discovery. We report extensive character- structure to intuitive notions of shape (27–29), and similarity ization of structural properties and diversity of biological perfor- fusion methods (30–33) that describe relationships between sets. mance for these compounds and expand comparative analyses to Moreover, chemical similarity and diversity analyses continue to include physicochemical properties and three-dimensional shapes progress (34–37), including studies using Shannon entropy (38) as of predicted conformers.
  • Structure Based Pharmacophore Modeling, Virtual Screening

    Structure Based Pharmacophore Modeling, Virtual Screening

    www.nature.com/scientificreports OPEN Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identifcation of natural anti‑cancer agents targeting XIAP protein Firoz A. Dain Md Opo1,2, Mohammed M. Rahman3*, Foysal Ahammad4, Istiak Ahmed5, Mohiuddin Ahmed Bhuiyan2 & Abdullah M. Asiri3 X‑linked inhibitor of apoptosis protein (XIAP) is a member of inhibitor of apoptosis protein (IAP) family responsible for neutralizing the caspases‑3, caspases‑7, and caspases‑9. Overexpression of the protein decreased the apoptosis process in the cell and resulting development of cancer. Diferent types of XIAP antagonists are generally used to repair the defective apoptosis process that can eliminate carcinoma from living bodies. The chemically synthesis compounds discovered till now as XIAP inhibitors exhibiting side efects, which is making difculties during the treatment of chemotherapy. So, the study has design to identifying new natural compounds that are able to induce apoptosis by freeing up caspases and will be low toxic. To identify natural compound, a structure‑ based pharmacophore model to the protein active site cavity was generated following by virtual screening, molecular docking and molecular dynamics (MD) simulation. Initially, seven hit compounds were retrieved and based on molecular docking approach four compounds has chosen for further evaluation. To confrm stability of the selected drug candidate to the target protein the MD simulation approach were employed, which confrmed stability of the three compounds. Based on the fnding, three newly obtained compounds namely Caucasicoside A (ZINC77257307), Polygalaxanthone III (ZINC247950187), and MCULE‑9896837409 (ZINC107434573) may serve as lead compounds to fght against the treatment of XIAP related cancer, although further evaluation through wet lab is necessary to measure the efcacy of the compounds.
  • A Fully Integrated Pharmacophore Modeling and Structure Optimization Toolkit D Vlachakis, P Fakourelis, V Megalooikonomou, C Makris, S Kossida

    A Fully Integrated Pharmacophore Modeling and Structure Optimization Toolkit D Vlachakis, P Fakourelis, V Megalooikonomou, C Makris, S Kossida

    DrugOn: a fully integrated pharmacophore modeling and structure optimization toolkit D Vlachakis, P Fakourelis, V Megalooikonomou, C Makris, S Kossida To cite this version: D Vlachakis, P Fakourelis, V Megalooikonomou, C Makris, S Kossida. DrugOn: a fully inte- grated pharmacophore modeling and structure optimization toolkit. PeerJ, PeerJ, 2015, 3, pp.e725. 10.7717/peerj.725. hal-01163636 HAL Id: hal-01163636 https://hal.archives-ouvertes.fr/hal-01163636 Submitted on 8 Jun 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License DrugOn: a fully integrated pharmacophore modeling and structure optimization toolkit Dimitrios Vlachakis1,2,4 , Paraskevas Fakourelis1,2,4 , Vasileios Megalooikonomou2, Christos Makris2 and Sophia Kossida1,3 1 Bioinformatics & Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Athens, Greece 2 Computer Engineering and Informatics Department, University of Patras, Patras, Greece 3 IMGT, Laboratoire d’ImmunoGen´ etique´ Moleculaire,´ Institut de Gen´ etique´ Humaine, Montpellier, France 4 These authors contributed equally to this work. ABSTRACT During the past few years, pharmacophore modeling has become one of the key com- ponents in computer-aided drug design and in modern drug discovery.
  • ADME Filters for Virtual Screening

    ADME Filters for Virtual Screening

    Molecules 2002, 7, 51–62 molecules ISSN 1420-3049 http://www.mdpi.org † Virtual Screening in Lead Discovery: A Viewpoint Tudor Ionel Oprea EST Lead Informatics, AstraZeneca R&D Mölndal, S-43183 Mölndal, Sweden. Tel. +46 (0)31-776- 2373, Fax +46 (0)31-776-3792, e-mail: [email protected] † This article does not necessarily reflect the views of AstraZeneca. Received: 22 October 2001; in revised form 9 December 2001 / Accepted: 12 December 2001/ Published: 31 January 2002 Abstract: Virtual screening (VS) methods have emerged as an adaptive response to massive throughput synthesis and screening technologies. Based on the structure-permeability paradigm, the Lipinski rule of five has become a standard property filtering protocol for VS. Three possible VS scenarios with respect to optimising binding affinity and pharmacokinetic properties are discussed. The parsimony principle for selecting candidate leads for further optimisation is advocated. Keywords: ADME filters, combinatorial library design, drug discovery. The Emergence of Virtual Screening Massive throughput in synthesis and screening yields, on the average, hundreds of thousands of novel compounds that are synthesised, then screened for various properties, ranging from biological activity and solubility to metabolic stability and cytotoxicity. The economically-driven pressure to deliver the “first-in-class” drug on the market has forced the pharmaceutical industry to embark in a costly, yet untested, drug discovery paradigm: Hunting for the new gene, the new target, the new lead compound, the new drug candidate, finally hunting for the new drug. Just as computational chemistry (computer-aided drug design, molecular modelling, etc) was being hailed as the newest, safest and fastest method to put new chemical entities on the drug market in the late 1980s, so was combinatorial chemistry (applied molecular evolution, multiple parallel synthesis, etc.), combined with high- throughput screening (HTS), hailed as the newest, safest and fastest method in the mid-1990s.
  • A Survey of Across-Target Bioactivity Results of Small Molecules in Pubchem Lianyi Han, Yanli Wang∗ and Stephen H

    A Survey of Across-Target Bioactivity Results of Small Molecules in Pubchem Lianyi Han, Yanli Wang∗ and Stephen H

    Vol. 25 no. 17 2009, pages 2251–2255 BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btp380 Data and text mining A survey of across-target bioactivity results of small molecules in PubChem Lianyi Han, Yanli Wang∗ and Stephen H. Bryant∗ National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA Received on February 12, 2009; revised on May 20, 2009; accepted on June 16, 2009 Advance Access publication June 23, 2009 Associate Editor: Alfonso Valencia ABSTRACT towards certain targets, in order to exclude them from the lead This work provides an analysis of across-target bioactivity results optimization process. in the screening data deposited in PubChem. Two alternative One straightforward way to verify the target specificity of a approaches for grouping-related targets are used to examine compound is through a data mining process via literature and/or a compound’s across-target bioactivity. This analysis identifies patent searches. However, this is an expensive approach that requires compounds that are selectively active against groups of protein extensive expert knowledge. Another approach is to design a targets that are identical or similar in sequence. This analysis also profiling assay against a panel of targets. This is a powerful approach, identifies compounds that are bioactive across unrelated targets. however it is usually aimed at a limited number of targets and a Statistical distributions of compounds’ across-target selectivity small number of compounds at the decision-making point when provide a survey to evaluate target specificity of compounds by the hit is ready to be progressed to a lead (Azzaoui et al., 2007; deriving and analyzing bioactivity profile across a wide range of Whitebread et al., 2005).