WISDOM: A Grid-Enabled Drug Discovery Initiative against Malaria - Grid Computing: Infrastructure, Service, and Applications - page 350

Lead optimization addresses the development from the most promising
lead compounds to a safe and effective drug. Instead of expensive and
longer in vitro and in vivo tests, evaluation of basic chemical properties can
be achieved by virtual screening and quantitative structure activity rela-
tionship (QSAR). QSAR is a quantitative correlation process of chemical
structure with well-dei ned methods, such as optimization for pharma-
ceutical properties [absorption, distribution, metabolism, excretion and
toxicity (ADMET)] or efi cacy against the target organism.
In silico drug discovery contributes to increasing biological system knowl-
edge, to managing data in a collaboration space, to speeding up analysis,
and consequently to increasing the low success rate of the traditional “wet”
approach. The efi ciency gains of such an integrated knowledge system
could correspond to save 35% costs, or about US$300 million, and 15%
time, or two years of development time per drug.
Nevertheless, in spite of increasing levels of investment in in silico tech-
niques, there is a steady decline in the number of new molecules that enter
clinical development and reach the market. Many factors have changed
over the past ten years, particularly the domination of the target-based
drug discovery paradigm, favoring screening and rational drug discovery
programs. A new approach aims to integrate rational drug discovery with
a strong physiology and disease focus.

14.2.1.1 Requirements

Reducing the research time and cost in the discovery stage and enhancing
information about the leads are key priorities for pharmaceutical compa-
nies worldwide [1]. To achieve this goal, in silico drug discovery must meet
the following requirements:

• in silico drug discovery process includes the management of a

large variety and quantity of scientii c data; for example, images,
sequences, models, and databases. Data integration is thus a chal-
lenge to increase knowledge discovery but also to ease the com-
plex workl ow. This implies data format standardization, datal ow
dei nition in a distributed system, infrastructure and software
providers for data storage, services for data and metadata regis-
tration, data manipulation, and database updates.
The

The

• in silico drug discovery process also includes the manage-

ment of a large variety and quantity of software. Software inte-
gration is another challenge to build efi cient and complex
workl ows and to ease data management and data mining.
Software can be provided in a distributed environment such as a
Web server on the Internet. Different experts are absolutely neces-
sary to maintain and update software and workl ows to propose
new methods or pipelines, to use remote services, exploit outputs,
and i nally to propose compounds for assay. A software workl ow