Current
Computer-Aided Drug Design
ISSN: 1573-4099
Current Computer-Aided
Drug Design
Volume 1, Number 1, January 2005
Contents
Editorial
Pp.1-2
A New Group Contribution Approach to the Calculation
of LogP Pp. 3-9
Hao Zhu, Aleksander Sedykh, Suman K. Chakravarti
and Gilles Klopman
[Abstract] [Full
text article]
Understanding Skin Penetration: Computer Aided
Modeling and Data Interpretation Pp. 11-19
I. Tuncer Degim
[Abstract] [Full
text article]
‘Inductive’ Descriptors: 10 Successful
Years in QSAR Pp. 21-42
A. Cherkasov
[Abstract] [Full
text article]
Proteomics in Computer-Aided Drug Design Pp.
43-52
Ying Wang, Jen-Fu Chiu and Qing-Yu He
[Abstract] [Full
text article]
Structural Basis for Interaction of Inhibitors
with Cyclin-Dependent Kinase 2 Pp. 53-64
Fernanda Canduri and Walter Filgueira de Azevedo
Jr.
[Abstract] [Full
text article]
The Potential Performance of Artificial Neural
Networks in QSTRs for Predicting Ecotoxicity of Environmental
Pollutants Pp. 65-72
Ryo Shoji
[Abstract] [Full
text article]
Kohonen Artificial Neural Network and Counter
Propagation Neural Network in Molecular Structure-Toxicity
Studies Pp. 73-78
Marjan Vracko
[Abstract] [Full
text article]
Three-Dimensional Structural Analysis of the Binding
Site of Two Inhibitors, Nervonic Acid and Lithocholic Acid,
of DNA Polymerase β and DNA Topoisomerase II
Pp. 79-91
Yoshiyuki Mizushina, Nobuyuki Kasai, Fumio Sugawara,
Hiromi Yoshida and Kengo Sakaguchi
[Abstract] [Full
text article]
Recent Advances in Docking and Scoring Pp.
93-102
M. Krovat, T. Steindl and T. Langer
[Abstract] [Full
text article]
The Design and Docking of Virtual Compound Libraries
to Structures of Drug Targets Pp. 103-127
Amy C. Anderson and Dennis L. Wright
[Abstract] [Full
text article]
Abstracts
[Back to top]
A New Group Contribution Approach to the Calculation of LogP
Hao Zhu, Aleksander Sedykh, Suman K. Chakravarti
and Gilles Klopman
[Full text article]
A new improved group contribution model that predicts the
n-octanol/water partition coefficient (logP) is described.
A combined parameter set that contains 153 basic parameters,
41 extended parameter and 14 molecular surface/property descriptors
was generated from a training database of 8320 chemicals.
The model achieved significant improvement after modifying
the traditional group contribution equation by using a three
dimensional steric hindrance modulator. The predictive ability
of this model was accessed by calculating the logP values
of a test set of 1667 ordinary organic chemicals and a set
of 137 drug-like chemicals that were not included in the training
database.
[Back to top]
Understanding Skin Penetration: Computer Aided Modeling and
Data Interpretation
I. Tuncer Degim
[Full text article]
There has been considerable development in our knowledge
about the mechanism of skin permeation. This has largely been
brought about by the development of experimental techniques
and increased computer technology, hardware and available
software. The advanced computer technology and software have
provided indications, relationships, at a molecular level,
about routes of penetration and how the formulations can be
formulated considering the effects of excipients and drugs
on the barrier properties of skin layers. Available computer
programs for molecular modeling have been used to calculate
some molecular properties of the drug molecules such as surface
area, partial charges etc. This publication reviews some of
the mathematical models and techniques used some molecular
descriptors and properties that have been constructed to predict
and understand percutaneous penetration and transdermal delivery.
The models are also useful for various enhancement strategies
that can be used in dermal penetration and formulation development
studies. If the appropriate biophysical techniques combined
with the mathematical modeling and statistical analysis using
computer, it can provide useful information for identifying
the possible penetration processes when different classes
of enhancers or excipients used in the formulation. Models
are also useful for understanding which factors affect the
penetration of molecules through skin and these factors/parameters
can be used for the control of the penetration rate when effective
transdermal delivery or therapy is required or targeted.
[Back to top]
‘Inductive’ Descriptors: 10 Successful Years in
QSAR
A. Cherkasov
[Full text article]
The paper overviews the developments of ‘A New Model
of Inductive Effect’ - an approach introduced 10 years
ago for calculation of Taft’s substituent constants.
The original model enabled accurate quantification of inductive
parameters σ* and allowed approaching numerous important
theoretical problems associated with inductive and steric
interactions.
A number of methods derived from the original approach have
been reviewed and discussed including those for ‘inductive’
electronegativity, ‘inductive’ hardness-softness
and ‘inductive’ partial charges. The practical
use of ‘inductive’ reactivity indices as a novel
and effective class of QSAR (quantitative structure-activity
relationships) descriptors has been illustrated in the context
of QSAR studies of antibacterial activity of organic chemicals
and cationic peptides.
The further developments and prospective applications of
‘inductive’ 3D QSAR descriptors in the area of
computer-aided drug design have also been discussed.
[Back to top]
Proteomics in Computer-Aided Drug Design
Ying Wang, Jen-Fu Chiu and Qing-Yu He
[Full text article]
Proteins are functional molecules in cells and are the major
targets for drug action. To design a rational drug, we must
firstly find out which proteins can be the drug targets in
pathogenesis. Proteomics has great promise in identification
of protein targets and biochemical pathways involved in disease
processes. Proteomics as a whole increasingly plays an important
role in the multi-step drug-development process. The process
includes target identification and validation, lead selection,
small-molecular screening and optimization, and toxicity testing.
Furthermore, sub-disciplines such as computational proteomics,
chemical proteomics, structural proteomics and topological
proteomics offer significant contributions especially in computer-aided
drug design. This review will summarize the recent progress
in pharmaco-proteomics and the discipline's potential application
in computer-assisted drug design.
[Back to top]
Structural Basis for Interaction of Inhibitors with
Cyclin-Dependent Kinase 2
Fernanda Canduri and Walter Filgueira
de Azevedo Jr.
[Full text article]
Cell cycle progression is tightly controlled by the activity
of cyclin-dependent kinases (CDKs). CDKs are inactive as monomers,
and activation requires binding to cyclins, a diverse family
of proteins whose levels oscillate during the cell cycle,
and phosphorylation by CDK-activating kinase (CAK) on a specific
threonine residue. The central role of CDKs in cell cycle
regulation makes them a promising target for studying inhibitory
molecules that can modify the degree of cell proliferation,
the discovery of specific inhibitors of CDKs such as polyhydroxylated
flavones has opened the way to investigation and design of
antimitotic compounds. A chlorinated form, flavopiridol, is
currently in phase II clinical trials as a drug against breast
tumors. The aromatic portion of the inhibitor binds to the
adenine-binding pocket of CDK2, and the position of the phenyl
group of the inhibitor enables the inhibitor to make contacts
with the enzyme not observed in the ATP complex structure,
the analysis of the position of this phenyl ring not only
explains the great differences of kinase inhibition among
the flavonoid inhibitors but also explains the specificity
of roscovitine and olomoucine to inhibit CDK2. There is strong
interest in CDKs inhibitors that could play an important role
in the discovery of a new family of antitumor agents. The
crystallographic analysis together with bioinformatics studies
of CDKs are generating new information about the structural
basis for inhibition of CDKs. The relevant structural features
that may guide the structure based-design of a new generation
of CDK inhibitors are discussed in this review.
[Back to top]
The Potential Performance of Artificial Neural Networks
in QSTRs for Predicting Ecotoxicity of Environmental Pollutants
Ryo Shoji
[Full text article]
This review surveys the applications of neural network methodologies
to the field of Quantitative Structure-Toxicity Relationships
(QSTRs) in environment, and more specifically ecotoxicity.
QSTR is one of the methods for predicting hazards of various
chemicals and utilizes a computer-based technology such as
artificial neural network to predict the toxicity of a chemical
solely from its molecular attributes. Many artificial neural
network methodologies have been applied to ecotoxicological
data for fish, bacteria, protozoa and so on. The results demonstrate
the ability of the artificial neural network methodologies
to apply nonlinear structure-toxicity relationships for the
prediction of the corresponding toxicity values for chemicals,
which are not part of the training sets. In order to employ
an artificial neural network for QSTR, although users must
pay attention to over-parameterization, data distribution,
the structure and training cycle of neural network, and chance
correlation, fine tuned neural network has high performance
to predict ecotoxicity of chemicals. In the most of the QSTR
studies, the results by artificial neural network modeling
gave clearly better prediction of toxicity values compared
to the results by multiple linear regression analysis or other
commercial QSTR programs.
[Back to top]
Kohonen Artificial Neural Network and Counter Propagation
Neural Network in Molecular Structure-Toxicity Studies
Marjan Vracko
[Full text article]
We present self-organizing map or Kohonen network and counter
propagation neural network as powerful tools in quantitative
structure property/activity relationship modeling. Two areas
of applications are discussed: estimation of toxic properties
in environmental research and applications in drug research.
[Back to top]
Three-Dimensional Structural Analysis of the Binding Site
of Two Inhibitors, Nervonic Acid and Lithocholic Acid, of
DNA Polymerase β and DNA Topoisomerase II
Yoshiyuki Mizushina, Nobuyuki Kasai, Fumio Sugawara,
Hiromi Yoshida and Kengo Sakaguchi
[Full text article]
We found that nervonic acid (NA, 15cis-tetracosenoic
acid) which is a cis-configurated unsaturated long-chain
fatty acid and lithocholic acid (LCA, hydroxy-5β-cholan-24-oic
acid) which is a bile acid are selective inhibitors of mammalian
DNA polymerase β (pol β) and DNA topoisomerase II
(topo II). Here, we report the molecular interaction of NA
and LCA with pol b or topo II. On 1H - 15N
HMQC NMR analysis of pol β with NA or LCA, the 8 kDa
domain of pol β bound to NA or LCA as a 1 : 1 complex
with a dissociation constant (KD) of 2.64 or 1.56 µM,
respectively. The NA-binding region was comprised mainly of
four amino acid residues (Leu11, Lys35, His51 and Thr79) of
pol β on the NA-interaction interface. Similarly, the
LCA-binding region consisted of three amino acid residues
(Lys60, Leu77 and Thr79). Based on a three-dimensional structural
analysis and comparison with the spatial positioning of specific
amino acids binding to NA and LCA on pol β, we obtained
supplementary information allowing us to build a structural
model of topo II using geometrical and evolutionary trace
methods. The four amino acid residues were Thr596, His735,
Leu741 and Lys983 for topo II, corresponding to Thr79, His51,
Leu11 and Lys35 for pol β and the three amino acid residues
were Lys720, Leu760 and Thr791 for topo II, corresponding
to Lys60, Leu77 and Thr79 for pol β. These results suggested
that the NA and LCA-binding domains of pol β and topo
II are three-dimensionally very similar.
[Back to top]
Recent Advances in Docking and Scoring
E. M. Krovat, T. Steindl and T. Langer
[Full text article]
This review is focused on recent advances and new aspects
in the field of molecular docking and scoring, and it covers
multiple applications and case studies. Basic requirements
and different algorithms for docking are briefly discussed.
Moreover, parameters that influence docking results, combination
of different docking algorithms and scoring functions, performance
of scoring functions, docking using homology models, and ligand
and protein flexibility are examined to give an overview of
the state-of-the-art methods and a survey of innovative approaches
in molecular docking and scoring. Regarding the enormous amount
of literature in this field we restrict ourselves on an overview
of several important advances in docking and scoring techniques
published within the last two years, i.e. we considered publications
ranging from 2002 to 2004.
[Back to top]
The Design and Docking of Virtual Compound Libraries to Structures
of Drug Targets
Amy C. Anderson and Dennis L. Wright
[Full text article]
This review provides a detailed analysis of the use of virtual
library screening (VLS) in the drug discovery process. The
first part is intended as a larger overview of the integrated
VLS process. Small molecule and target macromolecule considerations
will be described separately and will be subsequently integrated
in a discussion of docking, scoring and evaluation. The second
half of the review will focus on recent case studies that
use VLS as part of an integrated drug discovery program. The
case studies will illustrate the range of possible targets
in VLS, provide an account of inclusive methodology and reveal
the expectations for realistic goals. Recent efforts provide
compelling evidence that VLS is successful when practiced
in an integrated fashion involving synthetic, structural and
computational expertise.
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