Subject: Summary of QSAR review responses.

From: "Roberta Susnow" <[email-masked]>
Date: Thu, 3 Feb 2000 11:55:43 -0500

Hi,

    Below is a compilation of all the responses I received regarding the QSAR
review. It's pretty long! Thanks for all your input.

Roberta Susnow
Physiome Sciences

1)If you are interested in physical property and environmental
fate (e.g., biodegradability, atmospheric oxidation) QSARs, a good review is

P.H. Howard and W.M. Meylan.
Prediction of Physical Properties, Transport, and Degradation for
Environmental Fate and Exposure Assessments. pp 185-205. In: Quantitative
Structure-Activity Relationships in Environmental Sciences - VII,
Proc.QSAR96. Ed. F. Chen and G. Schuurmann. SETAC Press, Pensacola, FL.
1997.

Phil

Philip H. Howard, Ph.D. Phone: 315-452-8417
Director Fax: 315-452-8440
Environmental Science Center Email: [email-masked]
Syracuse Research Corporation Website http://esc.syrres.com/
6225 Running Ridge Rd.
North Syracuse, NY 13212

2)In addition to the reviews I have written a book which is a practical
guide to  the methods (including set design) so that might be some help.
The book  is:

"Data Analysis for Chemists: Application to QSAR and Chemical Product
Design"  Oxford University Press, 1995, ISBN 0 19 855728 0.

and some book chapters are:

"Quantitative Structure Activity Relationships" in "Similarity Models in
Chemistry,  Biochemistry and Related Fields", T.M. Krygowski, J. Shorter
and R. Zalewski  (Eds), pp 557-627, Elsevier, Amsterdam, 1991.

"Pattern Recognition Methods for use in Rational Drug Design" in
"Molecular Design  and Modeling: Concepts and Applications", J.J.
Langone (Ed.), Vol. 203 of Methods  in Enzymology, Academic Press, 1991
pp 613-638.

"Neural Networks - A Tool for Drug Design" with D.T. Manallack in
"Advanced  Computer-Assisted techniques in Drug Discovery", volume 3 of
"Methods and  Principles in Medicinal Chemistry", H. van de Waterbeemd
(Ed.), VCH, Weinheim,  New York, 1994 pp 293-318.

"Neural Networks in the Search for Similarity and Structure-Activity"
with D.W. Salt  in "Molecular Similarity Tools for Drug Design", P.M.
Dean (Ed.), Blackie Academic  and Professional, London, Glasgow, 1995 pp
187-214.

"Electronic Structure Calculations in Quantitative Structure Property
Relationships"  with M.R. Saunders in Volume 1 of "Advances in
Quantitative Structure Property  Relationships", M. Charton (Ed.), JAI
Press, Greenwich, London, 1996, pp 53-79.

"Structure-Property Correlations in Molecular Design" in
"Structure-Property  Correlations in Drug Research", H. van de
Waterbeemd (Ed.), R.G. Landes, Austin,  USA, 1996, pp 81-110.

A couple of very recent reviews are:

"Neural Networks in Drug Discovery; Have they Lived up to Their Promise
?" D.T.  Manallack and D.J. Livingstone, Eur. J. Med. Chem., 34, 195-208
(1999).

"The Characterization of Chemical Structures Using Molecular Properties.
A Survey"  D.J. Livingstone, J. Chem. Inf. Comput. Sci., in press.

I hope this helps, happy reading !

David L.
------------------------------------------------------------------ D.J.
Livingstone                ChemQuest                        Delamere
House, 1 Royal Crescent,                        Sandown. Isle of Wight
UK PO36 8LZ 
Phone & Fax: +44 (0)1983 401793  e-mail [email-masked]
http://www.chmqst.demon.co.uk
------------------------------------------------------------------

3)It may be hard to get anything very current as the field is undergoing
rapid evolution.  My work with Frank Burden is exploring all these issues
with a view to developing a very robust QSAR method suitable for use by
non-experts, which will give good, reliable models without the pitfalls of
chance correlations, overfitting, overtraining, poor variable selection
etc.  We have developed Bayseian neural nets to provide robust SAR mapping,
ARD (automatic relevance detection) for variable selection, Gaussian
processes for mapping, and a number of new molecular descriptors such as
molecular eigenvalues, molecular multipole moments, and new topological
indices.

I will try to find some current useful textx/reviews for you but also
suggest you look at very recent papers by our group, and Yvonne Martin.
The Journal of Chemical Information and Computer Science (ACS) is the best
place to look for contemporary work in this area.

Cheers,

Dave

Dr. David A. Winkler                    Email: [email-masked]
Senior Principal Research Scientist     Voice: 61-3-9545-2477
CSIRO Molecular Science Fax:   61-3-9545-2446
Private Bag 10,Clayton South MDC 3169   http://www.csiro.au
Australia         http://www.molsci.csiro.au

4)my comp chem web page has links to qsar stuff:

http://www.dakotacom.net/~bear/compchem.html

Soaring Bear Ph.D. Research Pharmacologist, Informatics,
Chemistry & Biochemistry, Herbalist & Healthy Lifestyles
Herbal Science:  http://www.amfoundation.org/herbmed.htm
My web tools:      http://www.dakotacom.net/~bear
email: [email-masked],  [email-masked]
fax: 559-663-5833, 520-795-9350

5)The following chapters in "The Encyclopedia of Computational Chemistry"
(Paul Schleyer et al. Eds. John Wiley & Sons: Chichester, 1998, ISBN:
0-471-96588-X)
covers QSAR:

Environmental Chemistry: QSAR
Qnatitative Structure-Activity Relationships in Drug Design
Qnatitative Structure-Property Relationships (QSPR)
Structure-Activity Relationships in Modeling Nucleic Acid Ligand Interactions

-Lingran
**********************************************************
Lingran Chen, Ph.D.
Senior Scientific Programmer
MDL Information Systems, Inc.

Phone: (510) 895-1313, Ext. 1305
FAX:   (510) 614-3616
Email: [email-masked]

6)> You will find the following chapters from Reviews in Computational
Chemistry helpful:
E. L. Plummer, in Reviews in Computational Chemistry, K. B. Lipkowitz
and D. B. Boyd, Eds., VCH Publishers, New York, 1990, Vol. 1, pp. 119-168.

L. H. Hall and L. B. Kier, RCC, 1991, Vol. 2, pp. 367-422.

I. B. Bersuker and A. S. Dimoglo, RCC, 1991, Vol. 2, pp. 423-460.

L. M. Balbes, S. W. Mascarella, and D. B. Boyd, RCC, 1994, Vol. 5, pp.
337-379.

G. A. Arteca, RCC, 1996, Vol. 9, pp. 191-253.

E. J. Martin, D. C. Spellmeyer, R. E. Critchlow Jr., and J. M. Blaney, 
RCC, 1997, Vol. 10, pp. 75-100.

T. I. Oprea and C. L. Waller, RCC, Wiley, 1997, Vol. 11, pp. 127-182.

P.-A. Carrupt, B. Testa, and P. Gaillard, RCC, Wiley, 1997, Vol. 11, pp.
241-315.

Good luck, Don

Donald B. Boyd, Ph.D.
Editor, Reviews in Computational Chemistry
http://chem.iupui.edu/rcc/rcc.html
Editor, Journal of Molecular Graphics and Modelling
(publication of the ACS COMP division and MGMS)
http://chem.iupui.edu/~boyd/jmgm.html
Department of Chemistry
Indiana University-Purdue University at Indianapolis
402 North Blackford Street
Indianapolis, Indiana 46202-3274, U.S.A.
Telephone 317-274-6891, FAX 317-274-4701
E-mail [email-masked]

7):  The following few references (and the references cited therein) will provide
you with detail descriptions on QSAR (both methods and applications).
1.    "Exploring QSAR" Vol 1 and 2, Hansch, C.  et al., 1995. ACS publication.
2.    "Comprehensive Medicinal Chemistry", Vol 4 (Rational Drug Design),
Hansch et. (Des)., 1990, Pergamon Press.
3.   "Classical and Three-Dimensional QSAR in Agrochemistry", Hansch, C. and
Fujita, T. (Des), 1995, ACS Symposium Series.
4.    "Medicinal Chemistry Vol. 19 (Quantitative Structure-Activity Relationships
of Drugs)", Topliss, J.G. (ed), 1983, Academic Press.
5.    "Quantitative Drug Design. A critical introduction", Martin, Y.C., 1978,
Marcel Dekker.
6.    "Strategy of Drug Design. A Molecular Guide to Biological Activity" Purcell,
W. P., et al. , 1973, Wiley.
7.    "3D QSAR in Drug Design, Theory, Methods and Application." Kubinyi, H.
(Ed)., 1993, ESCOM Science Publishers.
8.    "Perspectives in Drug Discovery and Design, Vols. 9/10/11 (3D QSAR in Drug
Design: Ligand-Protein Interactions and Molecular Similarity", Kubinyi, H. et al.
(Des), 1998, Kluwer/ESCOM.
9.    "Perspectives in Drug Discovery and Design, Vols. 12/13/14 (3D QSAR in Drug
Design: Recent Advances", Kubinyi, H. et al. (Des), 1998, Kluwer/ ESCOM.

    I just wrote a chapter on QSAR in a book  and if you are interested I will
send you a reprint when the book comes out later this year.
    Hope this helps.

Asim Debnath

8)The following chapters in "The Encyclopedia of Computational Chemistry"
(Paul Schleyer et al. Eds. John Wiley & Sons: Chichester, 1998, ISBN:
0-471-96588-X)
covers QSAR:

Environmental Chemistry: QSAR
Qnatitative Structure-Activity Relationships in Drug Design
Qnatitative Structure-Property Relationships (QSPR)
Structure-Activity Relationships in Modeling Nucleic Acid Ligand Interactions

-Lingran
**********************************************************
Lingran Chen, Ph.D.
Senior Scientific Programmer
MDL Information Systems, Inc.

Phone: (510) 895-1313, Ext. 1305
FAX:   (510) 614-3616
Email: [email-masked]
**********************************************************

9)Allen Richon's article "An Introduction to QSAR Methodology" on
the web http://www.netsci.org/Science/Compchem/feature19.html"
gives a simple, elegant and meaning of QSAR for starters.
It also talks about PLS and crossvalidation too..

Best,
Sree
***********************************************************
             Dr. Sreedhara V Rao
Fulmer Synthesis Rm 468             1401 NE Merman Drive,
Department of Chemistry             Terrace Apt #902
Washington State University         Pullman, WA-99163
Pullman, WA 99164-4630.

Ph: 509-335-5754/4643             509-334-5980(res)
Fx: 509-335-8867 [email-masked]
[email-masked]     http://www.wsu.edu/~voleti

10)A source of good information concerning QSAR can be found on the "QSAR &
Modelling Society" web-page. They offer lists of books, articles, datasets and
much more.  Check it out at -->   http://www.pharma.ethz.ch/qsar/

regards,
Greg
+-----------------------------------------------------------+
| Gregory L. Durst                  computational chemist   |
| Lilly Research Laboratories       tele: 317/433-3386      |
| Lilly Corporate Center            email: [email-masked] |
| Indianapolis, IN 46285   USA                              |
+-----------------------------------------------------------+

11)You might want to consider the following references.  They probably will
not be the complete answer for you but they should help:

1.  Ajay, Murcko, M. A. 1995. Computational Methods to Predict Binding Free
Energy in Ligand-Receptor Complexes. J. Med. Chem. 38:4953-67

2.  Kansy, M., Ulmschneider, M., van de Waterbeemd, H. 1995. 3D Structural
Databases in the Olfactophore Generation of Musk Odor. In QSAR and
Molecular Modelling: Concepts, Computational Tools and Biological
Applications, ed. :633-38. : Prous Science Publishers

3.  Livingstone, D. 1995. Data Analysis for Chemists. ISBN Number 0 19
855728 0. Oxford: Oxford University Press

4.  Pearlman, R. S., Smith, K. M. 1999. Metric Validation and the
Receptor-Relevant Subspace Concept. J. Chem. Inf. Comput. Sci. 39:28-353

5.  Rogers, D., Hopfinger, A. J. 1994. Application of Genetic Function
Approximation to Quantitative Structure-Activity Relationships and
Quantitative Structure-Property Relationships. J. Chem. Inf. Comput. Sci.
34:854-66

--
Jeffrey L. Nauss, PhD Phone: (858) 799-5555
Senior Scientist, Training Fax: (858) 458-0136
Molecular Simulations Inc. E-mail: [email-masked]
9685 Scranton Road http://www.msi.com/about/events/training
San Diego, CA 92121

12)Recently I was also looking for a review on QSAR and here is what
I got by searching Current Contents from 1993 on.  If you find something
else, please, let me know.

Sincerely,
Darko Babic
Institute "Rudjer Boskovic"
HR-10001 Zagreb, P.O.B. 1016
  Croatia
-------------------------------------------------------------------------
<1>
Authors
  Kubinyi H.
Title
  QSAR AND 3D QSAR IN DRUG DESIGN .2.
  APPLICATIONS AND PROBLEMS [Review]
Source
  Drug Discovery Today.  2(12):538-546, 1997 Dec.
Local Messages
  IRB      
Abstract
  QSAR already plays an important role in lead structure
  optimization and it can be predicted that QSAR methods will
  become essential for handling the huge amount of data associated with
  combinatorial chemistry. 3D QSAR has already been
  successfully applied to many data sets of enzyme and receptor ligands. The
  theory and methodology of these approaches were outlined in Part 1 of this
  article, published in the November issue. In the second part of this
two-part
  review, the author explains the applications of these methods and addresses
  the associated problems. [References: 43]

<2>
Authors
  Kubinyi H.
Title
  QSAR AND 3D QSAR IN DRUG DESIGN .1.
  METHODOLOGY [Review]
Source
  Drug Discovery Today.  2(11):457-467, 1997 Nov.
Local Messages
  IRB      
Abstract
  Classical QSAR methods describe structure-activity
  relationships in terms of physicochemical parameters and steric properties
  (Hansch analysis, extrathermodynamic approach), or certain structural
  features (Free Wilson analysis). 3D QSAR methods, especially
  comparative molecular field analysis, consider the three-dimensional
  structures and the binding modes of protein ligands. Quantitative
  similarity-activity relationships derive correlations between the
  similarities of individual compounds and their biological activities. Theory
  and methodology of these approaches are described here, together with the
  proper use of regression and partial least squares analyses for deriving
  quantitative structure-activity relationships. Part 2, to be published in
the
  December issue, will address applications and problems. [References: 45]

<3>
Authors
  Fujita T.
Title
  RECENT SUCCESS STORIES LEADING TO COMMERCIALIZABLE BIOACTIVE COMPOUNDS WITH
  THE AID OF TRADITIONAL QSAR PROCEDURES [Review]
Source
  Quantitative Structure-Activity Relationships.  16(2):107-112, 1997 Apr.
Local Messages
  IRB      
Abstract
  New successful applications of the Hansch-type traditional
  QSAR to key steps in designing novel bioactive compounds are
  reviewed. Studies for a benzhydrylbenzylpiperazine antimigraine agent
  (lomerizine), azole-type agricultural fungicides (metconazole and
  ipconazole), and a biphenylyloxobutanoic acid antiinflammatory agent
  (flobufen) were taken as the examples. Structural optimizations were nicely
  made by using the QSAR information sometimes along with
  findings obtained from 3D molecular modelling studies and/or hypotheses
  proposed for metabolic fates. The two azole fungicides were launched in
1994.
  The antimigraine and antiinflammatory agents are expected to be
  commercialized soon. [References: 27]

<4>
Authors
  Maddalena DJ.
Title
  APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS TO QUANTITATIVE
STRUCTURE-ACTIVITY
  RELATIONSHIPS [Review]
Source
  Expert Opinion on Therapeutic Patents.  6(3):239-251, 1996 Mar.
Local Messages
  IRB      
Abstract
  This review article examines the role of supervised and unsupervised
  artificial neural networks (ANNs) in the field of quantitative
  structure-activity relationships (QSAR). They were found to
  be useful in both classical QSAR and structure-property
  correlation (SPC) studies where the data sets contain significant non-linear
  relationships. New applications of ANNs, including methods of descriptor
  optimisation, simultaneous prediction of multiple descriptors, the
  development of flexible pharmacophore models and new methods for the
  multidimensional reduction and display of data sets suggest that ANNs will
  have a useful role in the field of QSAR in the future.
  [References: 61]

<5>
Authors
  Karelson M.  Lobanov VS.  Katritzky AR.
Title
  QUANTUM-CHEMICAL DESCRIPTORS IN QSAR/QSPR STUDIES [Review]
Source
  Chemical Reviews.  96(3):1027-1043, 1996 May.
Local Messages
  IRB      

<6>
Authors
  Hansch C.  Hoekman D.  Gao H.
Title
  COMPARATIVE QSAR - TOWARD A DEEPER UNDERSTANDING OF
  CHEMICOBIOLOGICAL INTERACTIONS [Review]
Source
  Chemical Reviews.  96(3):1045-1075, 1996 May.
Local Messages
  IRB      

<7>
Authors
  Kaminski JJ.
Title
  COMPUTER-ASSISTED DRUG DESIGN AND SELECTION [Review]
Source
  Advanced Drug Delivery Reviews.  14(2-3):331-337, 1994 Jun-Jul.
Local Messages
  IRB      
Abstract
  Comparative molecular field analysis (CoMFA), a promising new statistical
and
  graphic three-dimensional quantitative structure-activity relationship
  (3D-QSAR) technique, and its potential in the drug discovery
  and design process are reviewed. CoMFA, a computer-assisted
  3D-QSAR analysis tool, attempts to correlate molecular shape
  descriptors defined for a set of substrate (drug) molecules with their
  observed biological activities. The molecular shape descriptors for each
  substrate (drug) molecule are determined by analysis of the steric and
  electrostatic fields that surround a molecule using a test probe atom. CoMFA
  can also be used to predict the biological activity of unknown substrates in
  a series of compounds. [References: 20]

<8>
Authors
  Stone M.  Jonathan P.
Title
  STATISTICAL THINKING AND TECHNIQUE FOR QSAR AND RELATED
  STUDIES .2. SPECIFIC METHODS [Review]
Source
  Journal of Chemometrics.  8(1):1-20, 1994 Jan-Feb.
Local Messages
  IRB      
Abstract
  Twenty-two contrasting statistical methods are reviewed for their
  applicability to QSAR studies and similar
  prediction-oriented fields. Each method is concisely specified prior to
  explanatory or critical comment. [References: 40]

<9>
Authors
  Stone M.  Jonathan P.
Title
  STATISTICAL THINKING AND TECHNIQUE FOR QSAR AND RELATED
  STUDIES .1. GENERAL THEORY [Review]
Source
  Journal of Chemometrics.  7(6):455-475, 1993 Nov-Dec.
Local Messages
  IRB      
Abstract
  The two parts of this paper form a critique of a variety of statistical
  techniques of actual or potential use in quantitative structure-activity
  relationship (QSAR) studies and related fields. Part I
  explores the statistical thinking that is needed to underpin those
  techniques. Emphasis is placed on (a) the role of 'exchangeability' as an
  alternative to unrealistic statistical modelling and (b) the use of
  cross-validation to limit self-deception in the use of any particular
  technique. The problem of the almost unlimited range of molecular
descriptors
  is seriously addressed. (Part II provides a concise critical review of
  methods - some well-established and some new.) [References: 71]

<10>
Authors
  Domine D.  Devillers J.  Chastrette M.  Karcher W.
Title
  NON-LINEAR MAPPING FOR STRUCTURE ACTIVITY AND STRUCTURE PROPERTY MODELLING
  [Review]
Source
  Journal of Chemometrics.  7(4):227-242, 1993 Jul-Aug.
Local Messages
  IRB      
Abstract
  From a review of the theoretical aspects of non-linear mapping and the
  different algorithms available in the literature, the methodological and
  practical problems linked to the use of this multivariate method in
  structure-activity and structure-property relationship studies are
  underlined. Useful tools for the graphical display of the outputs and the
  interpretation of the obtained clusters are presented. Statistical
parameters
  estimating the quality of the graphical representation of each individual
are
  also introduced. An example of application on a data matrix of 37 acaricides
  described by four physicochemical descriptors (pi, F, R, MR) is presented.
  [References: 81]

Roberta G. Susnow
Physiome Sciences
Scientist
TEL:609-987-1199 x243
FAX:609-987-9393
307 College Road East
Princeton New Jersey 08540
[email-masked]