Classical and Three-Dimensional QSAR in Agrochemistry PDF
Preview Classical and Three-Dimensional QSAR in Agrochemistry
ACS SYMPOSIUM SERIES 606 Classical and Three-Dimensional QSAR in Agrochemistry g or1 acs.w00 bs.6.f u0 p6 p://5-0 htt99 2012 | 1/bk-1 Corwin Hansch, EDITOR 14, 102 Pomona College n October 5 | doi: 10. Toshio Fujita, EDITOR o9 3.34.136 May 5, 19 Fujitsu Kansai Systems Laboratory by 89.16n Date: nloaded ublicatio Developed from a symposium sponsored wP o D by the Division of Agrochemicals at the 208th National Meeting of the American Chemical Society, Washington, DC, August 21-25, 1994 American Chemical Society, Washington, DC 1995 In Classical and Three-Dimensional QSAR in Agrochemistry; Hansch, C., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1995. S 583.2 .C58 1995 Copy 1 Classical and three-dimensional QSAR in agrochemistry Library of Congress Cataloging-in-Publication Data Classical and three-dimensional QSAR in agrochemistry / Corwin Hansch, editor, Toshio Fujita, editor. p. cm.—(ACS symposium series; 606) "Developed from a symposium sponsored by the Division of Agro- chemicals at the 208th National Meeting of the American Chemical Society, Washington, DC, August 21-25, 1994." g Includes bibliographical references and index. or1 acs.w00 ISBN 0-8412-3321-7 bs.6.f u0 1. Agricultural chemicals—Structure-activity relationships— p6 p://5-0 Congresses. 2012 | htt1/bk-199 SCohIce.ime tHyica.a nlMs Schoece, itCeintoygr.w (2iDn0i.8v tihIsIi:.o nF1 9uo9jf4it :aA ,g WTrooacsshhheiiomn,g i1cto9anl2s9,. D- ICV) .. AVIIm.I .Se reAircmiaesne.r Cichaenm ical 4, 02 n October 15 | doi: 10.1 S63518.38.—2.Cd5c28 0 1995 95-3702C9IP o9 3.34.136 May 5, 19 This book is printed on acid-free, recycled paper. by 89.16n Date: ACmopeyrriicgahnt C©h e1m99i5ca l Society nloaded ublicatio Achlla pRteigrh itns tRhiess evrovleudm. eT inhdei caaptepse atrhaen cceo poyfr igthhet ocwodneer 'as tc othnese nbot ttthoamt roefp rtohger afpirhsitc pcoapgiee so fo fe athche wP chapter may be made for personal or internal use or for the personal or internal use of Do specific clients. This consent is given on the condition, however, that the copier pay the stated per-copy fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, for copying beyond that permitted by Sections 107 or 108 of the U.S. Copyright Law. This consent does not extend to copying or transmission by any means—graphic or electronic—for any other purpose, such as for general distribution, for advertising or promotional purposes, for creating a new collective work, for resale, or for information storage and retrieval systems. The copying fee for each chapter is indicated in the code at the bottom of the first page of the chapter. The citation of trade names and/or names of manufacturers in this publication is not to be construed as an endorsement or as approval by ACS of the commercial products or services referenced herein; nor should the mere reference herein to any drawing, specification, chemical process, or other data be regarded as a license or as a conveyance of any right or permission to the holder, reader, or any other person or corporation, to manufacture, reproduce, use, or sell any patented invention or copyrighted work that may in any way be related thereto. Registered names, trademarks, etc., used in this publication, even without specific indication thereof, are not to be considered unprotected by law. PRINTED IN THE UNITED STATES OF AMERICA In Classical and Three-Dimensional QSAR in Agrochemistry; Hansch, C., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1995. 1995 Advisory Board ACS Symposium Series Robert J. Alaimo Cynthia A. Maryanoff Procter & Gamble Pharmaceuticals R. W. Johnson Pharmaceutical Research Institute Mark Arnold University of Iowa Roger A. Minear University of Illinois g David Baker at Urbana-Champaign acs.orw001 University of Tennessee Omkaram Nalamasu ubs.06.f Arindam Bose AT&T Bell Laboratories p6 http://995-0 Pfizer Central Research Vincent Pecoraro 2012 | 1/bk-1 RNaovbael rRt eFse.a Brcrha dLya,b oJrra. tory University of Michigan 4, 02 George W. Roberts on October 195 | doi: 10.1 MMChaaerrmyg EaEdr.ei tt C CAaos.m tCeplaalvnioaynn augh NJUoonhrivtnhe r RsCit.a yrS oohlfina Iapll liSentyoai tse University 3.34.136 May 5, 19 ANarttihonura l BS.c iEenllcies Foundation Doautg Ularsb aAn.a -SCmhaitmh paign nloaded by 89.16ublication Date: UGUnnuiinvveedrrass iittIyy. ooGff KeWoairnsgcsoa ns sin at Madison CLD.ou nSPcoounmrtr eanstu Tnedcahrnaomlog ies Corporation ow P Madeleine M. Joullie Michael D. Taylor D University of Pennsylvania Parke-Davis Pharmaceutical Research Lawrence P. Klemann William C. Walker Nabisco Foods Group DuPont Douglas R. Lloyd Peter Willett The University of Texas at Austin University of Sheffield (England) In Classical and Three-Dimensional QSAR in Agrochemistry; Hansch, C., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1995. Foreword IHE ACS SYMPOSIUM SERIES was first published in 1974 to provide a mechanism for publishing symposia quickly in book form. The purpose of this series is to publish comprehensive books developed from symposia, which are usually "snapshots in time" of the current research being done on a topic, plus g some review material on the topic. For this reason, it is neces or1 acs.w00 sary that the papers be published as quickly as possible. bs.6.f Before a symposium-based book is put under contract, the pu60 proposed table of contents is reviewed for appropriateness to p://5-0 the topic and for comprehensiveness of the collection. Some htt99 2012 | 1/bk-1 rpoaupnerds o uart et heex cslcuodpeed o fa tt hteh ivso lupmoine.t , Iann add doitthioenrs, aa drrea fatd odfe eda ctho 4, 02 paper is peer-reviewed prior to final acceptance or rejection. 11 n October 5 | doi: 10. TTerhh^eis ) aaounft ohtnhoyrems soythumespn or seriveuivemiwse, wpthhroeoic reb sepsc aopimse ress u tphaecerc voeirdsdeitidon rgb( syt)o t ohtfhe t eho err egbcaoonomizk . o9 3.34.136 May 5, 19 wmcahemnoed rcaaht-eirocenkasd thyoa cft o apblyol ,tn hae ncetdhss esau rbyrem vriieetvw itsheiroesn fsia nhnaadlv peta hbpeee ernes d mtiotoa rdthse,.e perdeiptoarrse, by 89.16n Date: view Apsa pae rrsu aler,e o innlcylu odreidgi nina lt hrees evaorlcuhm epsa.p eVrse rabnatdim o rriegpinraodl urec nloaded ublicatio tions of previously published papers are not accepted. wP o D In Classical and Three-Dimensional QSAR in Agrochemistry; Hansch, C., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1995. Preface iHIS VOLUME STEMS FROM THE SYMPOSIUM honoring Toshio Fujita for his work in agricultural chemistry. The variety of the papers represent a few of the many areas of research in physical organic chemis try, toxicology, traditional and 3-D QSAR to which he has so effectively contributed. It is not only the quality but also the great diversity of his contributions which makes Toshio Fujita a leader in agricultural chemis g or1 try and many other aspects of QSAR. ubs.acs.06.pr00 comAmse rwciea lp souincct eossuets inin Cthhea pdteersi g1n, QofS AheRr bhicaids easc hainedve idn sietcst imcidoesst . noLtiatbtllee p6 p://5-0 comparable to this is known from the area of medicinal chemistry. It is htt99 my belief that the exceptional success QSAR has achieved in agrochemis 12 | bk-1 try is in large part due to the stimulation agricultural scientists received er 14, 2010.1021/ fmroanmy tshtued Kenytso.t oH Uins iavlemrsoistty 3la0b0o praatpoerrys oafn aFlyuzjiet am, ahnisy caosplleeacgtsu eosf, cahnemd ihcios- n Octob5 | doi: btriyo.l ogical interactions beyond those associated with agricultural chemis 3.34.136 oMay 5, 199 aou rc oIwm dhmoo lenero catia pmlp peraoroandc thuoc t tiom. tpIhnleyd etdheedas,itg tinhn e ot hfo enb liyloa asmctt ei3va0seu yrceoeam orpsf osQuunSccdAesRs sa nhfoadrs l eQcshSsaA ntoRgxe iidcs by 89.16n Date: ccohrermeliacatilnsg. Nchoe mloincgael rs tirsu ictt uar ger eaantd a bchioielovegmicaenl te ftfoic afcoyr.m uElaxtceit eamn eenqtu antoiown nloaded ublicatio cinosmigTehhste ,f ranonemdx tct hopmeh pqaausreai lsiiotnyn otwhf ieft ihtQ ,o tSthhAeeR rr aQnadgSveAe Ronft. u srtreu wctiulrl ebs ec otvoe rbeedt,t emr ecohrgaannisitziec wP Do what we have learned so that past experience can be brought to bear on current research. This can be viewed as a complex extension of organic reactions. Once a new organic reaction is discovered, many researchers begin to delineate its scope and its side reactions (somewhat comparable to side effects and metabolism of bioactive compounds). Gradually these reactions are then related to the still growing body of organic chemistry. QSAR appears to be evolving in a similar fashion. We are seeking out the similarities and differences in the way chemicals affect plants, animals, insects, cells, enzymes, etc. A compound affecting the biochem istry of one organism is likely to affect that of others. Fujita's EMIL pro ject is an important innovative means for uncovering such generalizations. QSAR must go hand in hand with advances in comparative biochemistry and toxicology. To uncover the possibilities for selectively active ix In Classical and Three-Dimensional QSAR in Agrochemistry; Hansch, C., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1995. chemicals, QSAR will be increasingly important in forming the myriad connections between organic chemistry and biology. It is my sincere hope that this volume will be helpful not only to agri cultural scientists, but also to scientists working in related fields such as biochemistry, pharmaceuticals, and cosmetics. CORWIN HANSCH Department of Chemistry Pomona College 645 North College Avenue Claremont, CA 91711-6338 June 28, 1995 g or1 cs.00 bs.a6.pr u0 p6 p://5-0 htt99 2 | k-1 1b er 14, 2010.1021/ n Octob5 | doi: o9 3.34.136 May 5, 19 by 89.16n Date: nloaded ublicatio wP o D x In Classical and Three-Dimensional QSAR in Agrochemistry; Hansch, C., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1995. Chapter 1 Status of QSAR at the End of the Twentieth Century Corwin Hansch1 and Toshio Fujita2 1Department of Chemistry, Pomona College, 645 North College Avenue, Claremont, CA 91711-6338 2EMIL Project, Fujitsu Kansai Systems Laboratory, 2-2-6 Shiromi, g Chuo-ku, Osaka 540, Japan or1 cs.00 ah ubs.06.c Despite the fact that the various approaches to QSAR have, during the http://p995-06 pinatsetr atchtiroene odf eocragdaensi,c ccoommppoleutneldys wchitahn gtheed vtahrei ouwsa yfo rmwes osft uldifye, twhee 4, 2012 | 021/bk-1 hsahraeov rceto cano slmoidniengrg ewsd,a .a ys two egllo aisn tdheev aeldovpainntga gae ssc, ioefn coue ro fc uQrrSeAnRt .m Setohmodeo olof gthiees 11 n October 5 | doi: 10. Wpaer aadrieg mal.l cHauogwhetv uepr, wine t hnee etdas tko ocfo tnrsyiidnegr tforo imm ptirmovee t od itfifmeree ntht ea swpeeacktsn eosfs thaes wQeSllA Ras o9 the advantages of the so-called classical QSAR, and of course the same applies to the 3.34.136 May 5, 19 costufr tutsicuntbgus-retaidlt ugceehn adtsne gveweslho ipwcmhh eiccnhats n.a rlWee aendao ktt one eascssoi llilyni ncpelaaarsrasimitcyea plt erQroiSbzeAledRm osar,r isbceoustv efcrroa mnpa rapalomsooer trseeers leusclptt iaoicnne by 89.16n Date: pethfofeoe rcSltyAs..R CIatlsna dssst rilecanatelg rtQahlS ivAsa Rtlhi dtaeatn tiidto spn rt aoom vbioedn eugsn tdQheeSr -AppoRasr.as imbTiehltieety rHi zfaeomdr ,mm eeestcpth-eTacniaaifsltl tyipc ai nrina tmteerermptersre sto aftw isothenirc iohcf wnloaded Publicatio basorieolu loutigsouincasal.ll yUQ unSstAield Rw iewn cictahlna s tgshirocosauelp QfQoSrS AArReRa, ciatniro etne rsem sopsfe ocoifra mgllayenc ivhcaa lncuioasmmblpseo wuinne d tahsre e i ncn oohmto dpmeavoreigsleoonpnoi uonsfg Do a science of QSAR. Classical QSAR A little over three decades have passed since the discovery of a general approach for the formulation of biological quantitative structure activity relationships (QSAR) (1,2) The discovery fits in naturally with the subject of this symposium in that it grew out of the studies of plant growth regulators, especially the phenoxyacetic acids. Many have mistakenly thought that QSAR stems from medicinal chemistry. In fact, the first QSAR symposium, called "Biological Correlations - The Hansch Approach," was sponsored by the ACS Division of Pesticide Chemistry (now Agrochemicals) at the 161st national meeting in Los Angeles in 1970. 0097-6156/95/0606-0001$12.00/0 © 1995 American Chemical Society In Classical and Three-Dimensional QSAR in Agrochemistry; Hansch, C., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1995. 2 CLASSICAL AND THREE-DIMENSIONAL QSAR IN AGROCHEMISTRY Classical QSAR and Agrochemistry That the early start in QSAR occured in agrochemicals may account, in part, for more successes in the form of commercial pesticides than in the case of new drugs evolving from QSAR. QSAR played an important role in the design of the following commercial products: herbicides, Metamitrone (Bayer) (3-5), Bromobutide (Sumitomo) (6), the insecticide Bifenthrin (FMC) (7), and the fungicides Metconazole and Ipconazole (Kureka) (8). The present volume provides an opportunity for reflection on where the subject stands today. The QSAR database being constructed at Pomona contains 6000 QSAR evenly divided between those from physical organic chemistry and those from biology (9,10). Among the biological QSAR, 109 are for insects, 67 for plants and 145 are for fungi (which are not limited to the agrochemical class). However, these figures understate the situation. For example, there are 88 enzymic QSAR from cholinesterase and 24 for P-450 enzymes used to develop insecticides and synergists. org 1 In addition there are 40 on chloroplasts which have been used to devise photosystem cs.00 II inhibitors and 16 QSAR for algae. Many other QSAR on enzymes or organelles ubs.a06.ch omf ayag arloscoh beem oicf ahle lQp SinA Rth ei sd elseisgsn t ohaf nb iothaactti vfoe rc opmhapromuancdesu. tiOcaf lcso, ubrsuet tohnee t osthaol unludm nboetr p6 http://995-0 fsotrrugcettu, raals cthlaes sEesM bILet wpreoejne ccto hmaps osuhnodws ne f(f1ec1ti,v1e2 a)s, pthhaatr mthaecreeu itsic naolst oar c pleeastni cbidoeusn.d ary in 2012 | 1/bk-1 (bactIenr itah,e tucumrroern, te dtca.t)a bfoalsleo wtheed labryg etshto nseu mfobre re nozfy QmSeAs R( 7(7838)5.) iTs hfeo rm sotusdt ieins teorne scteinllgs 4, 02 examples, of which many more are needed, are those where the same or similar n October 15 | doi: 10.1 coPorogmsatpnuoilsaumtnesds.s o fa rCel atsessicteadl QonS AiRso lated enzymes and also on intact cells or whole o9 3.34.136 May 5, 19 Trsayhtsietoe inmnai lt(iiza2le , p1vo3asr)tiu:al taietoelnses,c twirnoh naici chs,e shtt yioldlf r hocooplnhdgo, ebanricee, r tsah napdtr otshdteeurrcieci n.a rgeI t a tw hsrateasen tdmhaear djho orrp efesap ctothonarstse i nfi eHne daame tdem steto t t- wnloaded by 89.16Publication Date: TtTmeSwlaThuoofeEclt sa iRoederf alI ecrMtteecohf tOuerlrilsoaLtdecne. tribtiachvel Irlip tyetp ay eiart s ahr(m amoesmausuesjseroteapetrnenrr rstidfiis azasaicenl nlotdgyodf r tbQslmho,y Sago ttAThPl aeRt arohnf rit fvs 'oisπ ot r sl mEhuiamsymlim gdp(ehroel)otess p tpob shereee oc vtdp bieaiooirmlcysfl ys eppii nmabfrrsoliaaenirogm ctniiionnepa ttallder ebaripslssme ac h rceotaaoyrlsmneup c leebdsu tte oleeaeafrrcnr sic cc ho esuefuuesfmnfecefdthcei c tcfsoatto)so-s,r. Do biological interaction. Although more attention is now being given to Η-bonding, it has not been a problem of overriding importance. Quality of QSAR We can now begin to obtain some general feeling for the quality of the results for classical QSAR. Table I has been formulated by searching the biological QSAR database with the command >. That is, η > 7 finds the number of sets based on 8 or more data points (1984). Thus about one third of the examples are based on 7 or fewer points. Table I. Distribution of QSAR Based on Set Size Number of Congeners/Set Number of QSAR Sets with Outliers n>7 1984 1272 n> 12 1144 856 n>20 568 450 n>50 129 99 n>100 31 25 In Classical and Three-Dimensional QSAR in Agrochemistry; Hansch, C., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1995. 1. HANSCH & FUJITA QSAR at the End of the 20th Century 3 While small sets cannot characterize much of receptor space, they can provide clues about electronic or hydrophobic effects which may be supported by lateral validation with other QSAR (9,10,14,15). Even a set of 100 congeners acting in an animal provides information for only a superficial characterization of the SAR. It is for this reason that lateral validation is so important. We must make the maximum use of all QSAR information, enzymes, P-450, organelles, membranes, etc., as well as that from physical organic chemistry in the design of new agrochemicals and drugs. Compounds in the set marked as outliers in a QSAR are not included in the correlation. The situation is not as bad as pictured in Table I. Based on 8 or more congeners (1984 sets) we find that 1272 contain one or more outliers. However, in many instances congeners lacking suitable parameters are held in the set, but are not used to formulate the equation. In another view of quality we can consider the sets where η > 12 (1144 examples) shown in Table Π: org 1 Table II. Number of QSAR out of 1144 at Three Levels of Quality cs.00 Level of Correlation Number of QSAR Sets with Outliers (%) ah bs.6.c r>.90, r2>.81 832 576 (69) pu60 r>.95, r2>.90 305 176 (58) p://5-0 r>.975, r2>.95 107 56 (52) htt99 2012 | 1/bk-1 The better correlations tend to have fewer outliers. Clearly a large number of 14, 102 equations leave 20% or more of the variance "unexplained." In many instances, on October 95 | doi: 10. etQQhsSSeprAAeecRR ii asvfl rlpiayol emTinn att ybhth leoee f pΠ erhoaΙy.ro slmyic awflo oorr rikgm,a ppnraicor vtd eoamfta etbhnaitss b ecy aw nbe e btcteae nrc hpcaoarrmgaempdae rteteo rt ihezexastpieeo rnwi.m itheTn utthraenl inbnoigoi nsloeog,w ibc utaolt 3.34.136 May 5, 19 Table III. Distribution of Physical Organic QSAR Based on Set Size by 89.16n Date: Nnn>>u7m1 2b er of Congeners/Set 5N174u27m 9 ber of QSAR 73S01e46t (s(54 w58))i th Outliers (%) wnloaded Publicatio nnη >>> 251000 0 211 673 78118(5 ((045)14 )) o D The data set size for physical organic chemistry is smaller at every level. Here too outliers in a set may be ortho substituents for which often no attempt was made at parameterization. Table IV based on η > 12 (572 examples) for physical organic reactions can be compared with Table Π. Table IV. Number of Physical Organic QSAR at Three Levels Level of Correlation Number of QSAR Sets With Outliers (%) r>.90, r2>.81 538 288 (54) r>.95, r2>.90 420 316(75) r>.975, r2>0.95 272 121 (44) The correlations are much better; 94% of the total number of examples have r greater than 0.90. Only 73% of the biological QSAR meet this standard. One can not expect In Classical and Three-Dimensional QSAR in Agrochemistry; Hansch, C., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1995.
Description:The list of books you might like
