Beta-site amyloid precursor protein cleaving enzyme-1 (BACE1) is a target of interest for treating patients with Alzheimer's disease (AD). Inhibition of BACE1 may prevent amyloid-ß (Aß) plaque formation and the development or progression of Alzheimer's disease. Known BACE1 inhibitors were analyzed using computational chemistry and cheminformatics techniques to search for quantitative structure-activity relationships (QSAR). A remarkable relationship was found with only two simple descriptors. The square of the linear correlation coefficient r(2) is 0.75. The main descriptor is the number of hydrophobic contacts in the range 4-5 Å between the atoms of the ligand and active site. The other descriptor is the number of short (<2.8 Å) hydrogen bonds. Our approach uses readily available structural data on protein-inhibitor complexes in the Protein Data Bank (PDB) but would be equally applicable to proprietary structural biology data. The findings can aid structure-based design of improved BACE-1 inhibitors. If an inhibitor has less observed activity than predicted by our correlation, the compound should be retested because the first assay may have underestimated the compound's true activity.

The polymer formed from degradation of third-generation cephalosporin antibacterials is an inhibitor of HIV-1 reverse transcriptase. The polymer has a (Z)-2-(methoxyimino)-N-(2-iminoethyl)acetamide backbone linking thiazolyl rings. We used molecular modeling to investigate the three-dimensional structure of the polymer. Oligomers were constructed by a Scheraga-type buildup procedure. Energy minimization calculations were performed by molecular mechanics using the MMFF force field. Helical conformations are formed by the polymer. The implications of this discovery are discussed in relation to biological activity.

ADVERTISEMENT RETURN TO ISSUEPREVBook ReviewNEXTBook Review of Computational Chemistry Workbook: Learning through ExamplesDonald B. BoydView Author Information Department of Chemistry and Chemical BiologyIndiana University-Purdue University at IndianapolisIndianapolis, Indiana 46202E-mail: [email protected]Cite this: J. Med. Chem. 2010, 53, 17, 6523Publication Date (Web):July 9, 2010Publication History Published online9 July 2010Published inissue 9 September 2010https://doi.org/10.1021/jm1007352Copyright © 2010 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views734Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit Read OnlinePDF (572 KB) Get e-AlertsSUBJECTS:Computational chemistry,Nanospheres,Quantum mechanics,Software,Students Get e-Alerts

This chapter contains sections titled: Introduction and Overview Methodology and Results Proficiencies in Demand Analysis An Aside: Economics 101 Prognosis Acknowledgments References

ADVERTISEMENT RETURN TO ISSUEPREVBook ReviewBook Review of Pathway Analysis for Drug Discovery. Computational Infrastructure and ApplicationsDonald B. BoydView Author Information Department of Chemistry and Chemical BiologyIndiana University—Purdue University at IndianapolisIndianapolis, Indiana 46202Cite this: J. Med. Chem. 2009, 52, 7, 2161–2162Publication Date (Web):March 13, 2009Publication History Published online13 March 2009Published inissue 9 April 2009https://pubs.acs.org/doi/10.1021/jm900139shttps://doi.org/10.1021/jm900139sbook-reviewACS PublicationsCopyright © 2009 American Chemical SocietyRequest reuse permissionsArticle Views427Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Algorithms,Drug discovery,Pharmaceuticals,Software,Toxicity Get e-Alerts

Molecular orbital computer experiments are used to determine the conformational preferences of N-R-3-aminoazetidin-2-ones, where R is methyl or vinyl. These structures, which model both monocyclic and bicyclic antibacterial agents, are found to be most stable when the R substituent is coplanar with the four-membered ring. Only 1–2 kcal/mole and 5–7 kcal/mole are required to twist the model structures into conformations with the CC-NCR dihedral angle around the β-lactam C-N bond equal to that in Δ3-cephalosporins and penicillins, respectively. Twisting is shown to weaken the C-N bond and to make the carbonyl more susceptible to nucleophilic attack. Implications of the results to understanding structure-activity relationships of β-lactam antibiotics are discussed. The MINDO/3 method predicts the inversion barrier of NH3 satisfactorily at about 6 kcal/mole.

Baertschi et al. (Antiviral Chem. Chemother. 1997, 8, 353-362) clarified the nature of a polymeric degradation product formed from the cephalosporin ceftazidime. Interest in the polymeric material arises from its ability to inhibit the RNase H and polymerase activities of HIV-1 reverse transcriptase (RT). To shed light on the structure of the polymeric material like that which forms from degradation of third-generation cephalosporins, we apply molecular modeling and other computational chemistry techniques. Aminothiazole methoxime (2-amino-4-thiazolyl-methoxyimino; ATMO) is the parent structure related to the isolated degradation product of ceftazidime. The MMFF94 force field and Monte Carlo multiple minimum method as implemented in MacroModel are used to generate low-energy conformers. We built up oligomeric models starting from the trimer to the 16-mer and performed distribution analyses on the dihedral angles from the Monte Carlo runs to analyze the three-dimensional shapes of the oligomers. Although the larger oligomers are too long for a complete search of conformational space, the low-energy conformers examined do not show secondary structure or repetitive conformations. Polymeric ATMO material may, therefore, exhibit only random coil conformations. Topological similarity of ATMO structures to other reported RT inhibitors is also examined.

This chapter contains sections titled: Introduction Computational Chemistry: the Beginnings at Lilly Germination: the 1960s Gaining a Foothold: the 1970s Growth: the 1980s Fruition: the 1990s Epilogue Acknowledgments References

Thiazole-4-carboxamide adenine dinucleotide (TAD) analogues T-2′-MeAD (1) and T-3′-MeAD (2) containing, respectively, a methyl group at the ribose 2′-C-, and 3′-C-position of the adenosine moiety, were prepared as potential selective human inosine monophosphate dehydrogenase (IMPDH) type II inhibitors. The synthesis of heterodinucleotides was carried out by CDI-catalyzed coupling reaction of unprotected 2′-C-methyl- or 3′-C-methyl-adenosine 5′-monophosphate with 2′,3′-O-isopropylidene-tiazofurin 5′-monophosphate, and then deisopropylidenation. Biological evaluation of dinucleotides 1 and 2 as inhibitors of recombinant human IMPDH type I and type II resulted in a good activity. Inhibition of both isoenzymes by T-2′-MeAD and T-3′-MeAD was noncompetitive with respect to NAD substrate. Binding of T-3′-MeAD was comparable to that of parent compound TAD, while T-2′-MeAD proved to be a weaker inhibitor. However, no significant difference was found in inhibition of the IMPDH isoenzymes. T-2′-MeAD and T-3′-MeAD were found to inhibit the growth of K562 cells (IC50 30.7 and 65.0μM, respectively).

ChemInformVolume 36, Issue 32 Other Subjects Evaluation of Computational Chemistry Methods: Crystallographic and Cheminformatics Analysis of Aminothiazole Methoximes. Tulay Ercanli, Tulay Ercanli Dep. Chem., Indiana Univ.-Purdue Univ., Indianapolis, IN 46202, USASearch for more papers by this authorDonald B. Boyd, Donald B. Boyd Dep. Chem., Indiana Univ.-Purdue Univ., Indianapolis, IN 46202, USASearch for more papers by this author Tulay Ercanli, Tulay Ercanli Dep. Chem., Indiana Univ.-Purdue Univ., Indianapolis, IN 46202, USASearch for more papers by this authorDonald B. Boyd, Donald B. Boyd Dep. Chem., Indiana Univ.-Purdue Univ., Indianapolis, IN 46202, USASearch for more papers by this author First published: 19 July 2005 https://doi.org/10.1002/chin.200532201AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume36, Issue32August 9, 2005 RelatedInformation

ADVERTISEMENT RETURN TO ISSUEPREVBook ReviewEvolutionary Algorithms in Molecular Design. Methods and Principles in Medicinal Chemistry. Volume 8 Edited by David E. Clark (Aventis Pharma Ltd.; currently at Argenta Discovery Ltd., Dagenham, England). Wiley-VCH: Weinheim and New York. 2000. xii + 276 pp. $145. ISBN 3-527-30155-0Frédérique Barbosa and Donald B. BoydView Author Information Indiana University−Purdue University at Indianapolis Cite this: J. Am. Chem. Soc. 2001, 123, 22, 5384Publication Date (Web):March 27, 2001Publication History Published online27 March 2001Published inissue 1 June 2001https://pubs.acs.org/doi/10.1021/ja0048580https://doi.org/10.1021/ja0048580book-reviewACS PublicationsCopyright © 2001 American Chemical SocietyRequest reuse permissionsArticle Views81Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Algorithms,Combinatorial libraries,Computational chemistry,De novo modeling,Molecular design Get e-Alerts

The natural templates (NT) approach, which is a superimposition-based protocol that has been successfully employed in several studies, is here applied to ligands of the glycine ligand-gated ion channel receptor. Bioactive conformations for glycine and its analogs were obtained using strychnine (a natural and specific competitive antagonist) as template. Experimental evidence was used to guide the superimposition protocol. Three essential regions have been defined in strychnine's structure that serve as a pharmacophore for agonist and antagonist activities. Reasonable alignments of known ligands were found in the majority of the cases. Molecular mechanics (i.e., conformational searches for the relatively flexible ligands) and molecular dynamics (for relatively rigid ligands such as strychnine and 5,6,7,8-tetrahydro-4H-isoxazolo[3,4-d]azepin-3-ol) were used to assess the energetic accessibility of the proposed bioactive conformations.

Comparative molecular field analysis (CoMFA) is applied to antagonists of the 5-HT3 receptor. Analysis is done separately on three published sets of arylpiperazines and on a combination of the three sets. d-Tubocurarine, a conformationally restricted 5-HT3 ligand, is used as a template to assist in selecting the conformation of the antagonists for CoMFA alignment. Two forms of the arylpiperazines (neutral and protonated) and three different kinds of calculated charges (Gasteiger-Hückel, AM1, and AM1 with solvation effect included) are compared. Protonated structures give better statistical results than the neutral species. The way in which charges are calculated does not greatly affect the results. In terms of molecular fields, the behavior in each separate set of compounds cannot be extrapolated to the combined set of 47 compounds. The average value of r2cv from PLS cross-validation on the combined set is 0.70 and varies between 0.56 and 0.80 depending on the orientation of the molecules in the coordinate system. The CoMFA model is tested on four compounds not in the training set: quipazine, N-methylquipazine, 4-phenyl-N-methylquipazine, and KB-6933. Mean agreement of experimental and predicted pKi values of the antagonists is 0.7 log unit. Novel structural modifications are interpreted by the CoMFA model.