Current
Computer-Aided Drug Design
ISSN: 1573-4099
Current Computer-Aided
Drug Design
Volume 1, Number 2, April 2005
Contents
Novel Computational Approaches in QSAR and Molecular Design Based
on GA, Multi-Way PLS and NN Pp. 129-145
Kiyoshi Hasegawa, Masamoto Arakawa and Kimito Funatsu
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Post-Genomic Design of Bioactive Molecules Pp. 147-162
Martin G. Grigorov
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Variable Subset Selection in the Presence of Flagged Observations
and Multicollinear Descriptors in QSAR Pp. 163-177
Peter P. Mager and Luis Sanchez
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Predicting ADMET Properties by Projecting onto Chemical Space-Benefits
and Pitfalls Pp. 179-193
Hongmao Sun
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Assessing QSAR Limitations – A Regulatory Perspective
Pp. 195-205
Weida Tong, Huixiao Hong, Qian Xie, Leming Shi, Hong Fang
and Roger Perkins
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Computer Design of Vaccines: Approaches, Software Tools and
Informational Resources Pp. 207-222
Boris N. Sobolev, Ludmila V. Olenina, Ekaterina F. Kolesanova,
Vladimir V. Poroikov and Alexander I. Archakov
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Abstracts
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Novel Computational Approaches in QSAR and Molecular Design Based
on GA, Multi-Way PLS and NN
Kiyoshi Hasegawa, Masamoto Arakawa and Kimito Funatsu
QSAR and subsequent molecular design are very important steps in
drug discovery. Through QSAR, one derives a model that relates a
set of molecular descriptors to a biological activity. The resulting
model can be used to predict the activity values of new compounds
in molecular design. QSAR models range from simple, parametric equations
to complex, non-linear models. These models have each specific advantage
and shortcoming derived from their own algorithms. We have developed
hybrid approaches combining GA, multiway PLS and NN to utilize specific
advantage and to cover specific shortcoming of each method. We have
picked up five topics and outlined with the representative examples
in this review article.
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Post-Genomic Design of Bioactive Molecules
Martin G. Grigorov
This article is presenting the most recent trends in the ways that
the bioactive molecules of the future will be designed. They rely
on important recent discoveries for our understanding of the global
organization of the cell, giving evidence that biological networks
form small-world and scale-free structures. These networks are composed
of well-defined modules, with nodes connected by relatively short
paths that allow for fast signaling. The few very connected nodes,
that are unlikely to be affected by random alterations, support
the proper functioning of the whole system. The fact that in cells
everything is connected to everything explains why monogenic diseases,
associated to the alteration of individual genes, were found to
be an exception rather than a rule. The newly developed chemogenomic
technology is offering an alternative to the traditional animal-centric
pharmacological approach in the need to evaluate bioactive molecules
efficacy on intact biological systems, where the multiple targets
and pathways reside in their natural environment. The existence
of regulatory and interaction ‘neural centres’ or hubs
in these networks is setting new perspectives to target identification
and validation. With these new technologies at hand, we are entering
an exciting new era where the pharmacological targets will shift
from single proteins, to functional protein complexes, to whole
networks determining precise cellular states, and where the new
cures and foods will be no more based on single active ingredients
but will represent molecular cocktails or multiple ligands with
components targeting the neural centers of whole disease-associated
molecular networks.
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Variable Subset Selection in the Presence of Flagged Observations
and Multicollinear Descriptors in QSAR
Peter P. Mager and Luis Sanchez
A major problem in traditional quantitative structure-activity
relationships (QSARs) analysis is to select suitable chemical descriptors
from a large pool of variables. Decisions against or in favor of
a particular descriptor depends entirely on the result of statistically
based hypothesis testing. Uncertain results may be produced in presence
of multicollinear descriptors and flagged observations (high-leverage
points, outliers, influential data). To satisfy the assumptions
for hypothesis testing, diagnostic statistics and subsequent design
repair are employed. Here we show an example with nonnucleoside
HIV-1 reverse transcriptase inhibitors.
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Predicting ADMET Properties by Projecting onto Chemical Space-Benefits
and Pitfalls
Hongmao Sun
The mechanisms behind ADME (absorption, distribution, metabolism,
and excretion) related properties and toxicity endpoints are usually
complex, and many are not fully understood. As a result, most ADMET
predictive models are not based on theoretical principles, but are
derived from experimental data. ADMET properties are best analyzed
by projecting them onto the compounds of the training set. There
are multiple advantages to projecting the ADMET properties from
the problem domain to the chemical domain. Projection simplifies
the problem, and avoids the entanglement of needing to invoke specific
mechanisms. Projection focuses on the most important, and most tractable,
aspect of the problem -- the related properties of the compounds
themselves. In this review article, the general requirements of
the chemical space to be projected are discussed, including the
size and diversity of the training set and the accuracy of the biological
measurements, and the process is illustrated using an analogue of
a real projection. Also, the successes and pitfalls of the projection
method in recent ADMET predictions are reviewed.
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Assessing QSAR Limitations – A Regulatory Perspective
Weida Tong, Huixiao Hong, Qian Xie, Leming Shi, Hong Fang
and Roger Perkins
Wider acceptance of QSARs would result in a constellation
of benefits and savings to both private and public sectors.
For this to occur, particularly in regulatory applications,
a model’s limitations need to be identified. We define
a model’s limitations as encompassing assessment of overall
prediction accuracy, applicability domain and chance correlation.
A general guideline is presented in this review for assessing
a model’s limitations with emphasis on and examples of
application with consensus modeling methods. More specifically,
we discuss the commonalities and differences between external
validation and cross-validation for assessing a model’s
limitations. We illustrate two common ways of assessing overall
prediction accuracy, depending on whether or not the intended
application domain is predefined. Since even a high quality
model will have different confidence in accuracy for predicting
different chemicals, we further demonstrate using the novel
Decision Forest consensus modeling method a means to determine
prediction confidence (i.e., certainty for an individual chemical’s
prediction) and domain extrapolation (i.e., the prediction
accuracy for a chemical that is outside the chemistry space
defined by the training chemicals). We show that prediction
confidence and domain extrapolation are related measures that
together determine the applicability domain of a model, and
that prediction confidence is the more important measure.
Lastly, the importance of assessing chance correlation is
emphasized, and illustrated with several examples of models
having a high degree of chance correlations despite cross-validation
indicating high prediction accuracy. Generally, a dataset
with a skewed distribution, small data size and/or low signal/noise
ratio tends to produce a model with high chance correlation.
We conclude that it is imperative to assess all three aspects
(i.e., overall accuracy, applicability domain and chance correlation)
of a model for the regulatory acceptance of QSARs.
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Computer Design of Vaccines: Approaches, Software Tools and Informational
Resources
Boris N. Sobolev, Ludmila V. Olenina, Ekaterina F. Kolesanova,
Vladimir V. Poroikov and Alexander I. Archakov
Development of computer methods in molecular biology and
fast growth of microbial genomics data enabled new approach
based on selecting in silico antigenic components
to design vaccine constructs. It is expected that application
of this technology will eliminate side effects of new vaccines
and reduce the time consumption and financial expenses. The
bioinformatics methods of sequence analysis are used to reveal
the most prospective proteins or protein fragments of infectious
agents as candidates for vaccine design. In these studies
the specialized molecular immunology databases are widely
used. The new approach ("Reverse vaccinology") could
help in designing vaccines against diseases where traditional
methods are not successful, e.g. when the viral genome reveals
the extreme variability and permanent changes of antigenic
properties that make difficulties for selection of molecular
targets for medicines and candidate vaccines. A number of
informational resources are already designed to collect and
provide genomic data on certain microbes or viruses. The peculiarity
of such resources is presentation of data, characterizing
the different genomic variants of the same infectious agents.
These structural data coupled with information on functional/immune
features and software tools have to compose basis for constructing
a new generation of vaccines against "common" and
new infections such as AIDS, Hepatitis C, and SARS. The approaches
published in literature, as well as the authors’ original
results are discussed.
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