Mini-Reviews in Medicinal Chemistry, Volume 3, No. 8, 2003
Contents
Molecular
Descriptors in Medicinal Chemistry: From Design to QSAR via Internet-Based
Resources
Executive
Editor: Giulia Caron
Physicochemical Effects in the Representation
of Molecular Structures for Drug Designing Pp.789-796
Johann
Gasteiger
Fragmental Methods in the Analysis of
Biological Activities of Diverse Compound Sets Pp.797-808
P.
Japertas, R. Didziapetris and A.
Petrauskas
The WWW as a Tool to Obtain Molecular
Parameters Pp.809-820
Igor
V. Tetko
A Comparison of Calculated and Experimental
Parameters as Sources of Structural
Information: The Case of Lipophilicity-Related Descriptors Pp.821-830
Giulia
Caron and Giuseppe Ermondi
Applied Introduction to Multivariate Methods
Used in Drug Discovery
Pp.831-843
Eugenia
Migliavacca
The Use of Molecular Descriptors in the
Design of Gadolinium (III) Chelates as MRI Contrast Agents Pp.845-859
Alessandro Maiocchi
In Silico ADME Prediction: Data, Models,
Facts and Myths Pp.861-875
Franco Lombardo, Eric Gifford and Marina Y. Shalaeva
Abstracts
[Back to top] Physicochemical Effects in the Representation
of Molecular Structures for Drug Designing
Johann
Gasteiger
After the identification
of a biological target, drug design is to analyze the relationships between the
structure of potential ligands and their biological activity. A hierarchy of
structure representation is presented here considering either the constitution
of a molecule, its 3D structure, or the molecular surface. At each level, a
variety of physicochemical effects can be accounted for. Furthermore, the
special requirements of learning algorithm, such as neural networks, are taken
into consideration. Application to problems from combinatorial chemistry, lead
identification, high-throughput screening, and prediction of ADME-Tox
properties are given.
[Back to top] Fragmental Methods in the Analysis of
Biological Activities of Diverse Compound Sets
P.
Japertas, R. Didziapetris and A.
Petrauskas
The current
mini-review explains how fragmental methods (FMs) can be used in the analysis
and prediction of physicochemical properties and biological activities. The
considered properties include log P, solubility, pKa, intestinal
permeability, P-gp substrate specificity and toxicity. The focus will be a
description of a “mechanistic” approach, which implies a gradual reduction of
alternative explanations for any property or activity. This means a flexible
construction of fragmental parameters using large amounts of experimental data.
Since biological activities involve multiple (unknown) target macromolecules
with multiple binding modes, a stepwise classification (C-SAR) analysis is most
useful. It involves the following procedures: (i) construction of
physicochemical profiles using parameters that can be reliably predicted, (ii)
identification of reactive functional groups and the largest active skeletons,
(iii) generalization of these groups and skeletons in terms of “site-specific
physicochemical profiling”. This entails a dynamic construction of 2D
pharmacophores that can be converted into 3D models.
[Back to top] The WWW as a Tool to Obtain Molecular
Parameters
Igor
V. Tetko
This article
analyses molecular property calculation resources available on the Internet.
The first section summarizes the on-line database resources that could be
useful to search molecular and biological properties of chemicals, and indicates
some principal databases with physicochemical, thermochemical, toxicity, cancer
and HIV data. The second section overviews popular standalone programs for
calculation of molecular descriptors. Some of these programs can be downloaded
for free and used as standalone applications for calculation of molecular
descriptors. The third section describes on-line tools for the prediction of
molecular properties, activities and calculation of molecular descriptors.
Analysis of emerging tools that can be useful to developing new on-line servers
for the prediction of molecular parameters and properties is also given.
[Back to top] A Comparison of Calculated and Experimental
Parameters as Sources of Structural
Information: The Case of Lipophilicity-Related Descriptors
Giulia Caron and Giuseppe Ermondi
This review is
organized in three parts: firstly there is a general overview of recent
developments in lipophilicity written to induce medicinal chemists to question
what they want to obtain from this kind of study; secondly, the
state-of-the-art of experimental and computational determination of log P is
briefly reviewed; finally, some applications are discussed to illustrate how
much information can be extracted from lipophilicity, and to highlight the
difficulty of obtaining a reliable, general method to work with.
[Back to top] Applied Introduction to Multivariate Methods
Used in Drug Discovery
Eugenia
Migliavacca
The number of
articles concerning optimization and applications of multivariate techniques in
drug discovery testifies the growing importance attributed to these methods.
This mini review focuses on some of the basic and most employed multivariate
techniques in drug discovery research. Examples from the literature were
selected to illustrate a number of potential applications.
[Back to top] The Use of Molecular Descriptors in the
Design of Gadolinium (III) Chelates as MRI Contrast Agents
Alessandro
Maiocchi
Nuclear Magnetic
Resonance Imaging (MRI) is a very useful tool in modern medical diagnostics,
especially when gadolinium(III)-based contrast agents are administered to the
patient with the aim of increasing the image contrast between normal and
diseased tissues. The main purpose of this review is to show that a new
generation of these contrast agents could be developed by making greater use of
soft modelling techniques such as QSAR/QSPR after a suitable description of
their molecular structure.
[Back to top] In Silico ADME Prediction: Data, Models,
Facts and Myths
Franco
Lombardo, Eric Gifford and Marina Y.
Shalaeva
A critical review
of a very recent work in the field of in silico ADME prediction is presented
with emphasis on the work published during the period 2000-2002, and several
other review articles are mentioned in order to offer a broader view of the
field. We find that not much progress has been made in developing robust and
predictive models, and that the lack of accurate data, together with the use of
questionable modeling end-points, has greatly hindered the real progress in
defining generally applicable models.
Due to the largely
empirical nature of QSAR/QSPR approaches, general and truly predictive models for
complex phenomena, such as absorption and clearance, may still be chimeric. The
development of local models for use within focused chemical series may be the
most appropriate way of utilizing in silico ADME predictions, once experience
and data have been gained on a given project and/or structural class.