544
D. Fourches
Finally, more and more cheminformatics software tools will become fully
available and functional on tablets, smartphones and other mobile devices such as
augmented reality glasses ( e.g., Google Glass Project). This new step in technology
portability could realize one of the initial promises of cheminformatics, which was
(and still is) to assist experimentalists for chemical-relevant decision making as close
as possible to the lab bench. For instance, the following tablet applications are notable: PyMol (edited by Schrodinger; itunes.apple.com/us/app/pymol/) allows users to
browse and visualize protein structures and protein-ligand complexes; Chemspider
(edited by Molecular Materials Informatics; itunes.apple.com/us/app/chemspider/)
enables structural and text queries on the Chemspider database; Chemical Engineering AppSuite (edited by John McLemore; itunes.apple.com/us/app/chemical-engineering-appsuite/) integrates a collection of chemistry related tools and databases;
and Elemental (edited by Dotmatics Limited; itunes.apple.com/us/app/elemental/)
and Molprime (edited by Molecular Materials Informatics; itunes.apple.com/us/
app/molprime/) allows users to draw and export compound structures. These applications are the first representatives of an entire new generation of “cheminformatics apps” with user-friendly, tablet-ready graphical interfaces that will offer a
direct and intuitive access to diverse chemogenomics data. It is very likely that the
features offered by these applications will progressively integrate complex QSARbased predictors coupled with sophisticated modules that will be capable of rapidly
accessing and cross-searching chemical biological databases, visualizing [40] and
analyzing HTS results, launching modeling and screening computations on remotely
controlled workstations, and sharing chemical information in the cloud.
In summary, chemical biological data streams produced by modern drug discovery platforms are reaching unprecedented levels of size, diversity, and complexity
that require the development of novel cheminformatics methods and tools to process and analyze them. This situation offers some unique perspectives for inventing a new generation of expert systems and decision making technologies. As the
profusion of experimental data expands in the coming years to fully reach the level
and requirements of so-called Big Data, joint efforts and collaborations between
experimentalists and modelers will not only continue to strengthen but will become
the key for successful drug discovery. Modelers will have to guarantee fast and
accurate accesses to the correct and integrated chemical information and generate
reliable experimentally-testable hypotheses ( e.g, compound X is active toward both
targets Y and Z to induce phenotype P). In return, experimentalists will have to
keep on guiding modelers for better understanding their practical needs so that the
computational technologies can be adapted accordingly. This transitioning period is
extremely important for the whole cheminformatics research community: from the
students starting to learn the basics and recognizing the need to know all molecular
modeling approaches [41], to well-established researchers already facing the new
scales of data complexity and associated opportunities. The era of Big Chemical
Data is finally here and therefore it seems that modelers have never been so close
to achieving the rational, computer-aided design of novel molecules with controlled
polypharmacology and safety profiles.
D. Fourches
Finally, more and more cheminformatics software tools will become fully
available and functional on tablets, smartphones and other mobile devices such as
augmented reality glasses ( e.g., Google Glass Project). This new step in technology
portability could realize one of the initial promises of cheminformatics, which was
(and still is) to assist experimentalists for chemical-relevant decision making as close
as possible to the lab bench. For instance, the following tablet applications are notable: PyMol (edited by Schrodinger; itunes.apple.com/us/app/pymol/) allows users to
browse and visualize protein structures and protein-ligand complexes; Chemspider
(edited by Molecular Materials Informatics; itunes.apple.com/us/app/chemspider/)
enables structural and text queries on the Chemspider database; Chemical Engineering AppSuite (edited by John McLemore; itunes.apple.com/us/app/chemical-engineering-appsuite/) integrates a collection of chemistry related tools and databases;
and Elemental (edited by Dotmatics Limited; itunes.apple.com/us/app/elemental/)
and Molprime (edited by Molecular Materials Informatics; itunes.apple.com/us/
app/molprime/) allows users to draw and export compound structures. These applications are the first representatives of an entire new generation of “cheminformatics apps” with user-friendly, tablet-ready graphical interfaces that will offer a
direct and intuitive access to diverse chemogenomics data. It is very likely that the
features offered by these applications will progressively integrate complex QSARbased predictors coupled with sophisticated modules that will be capable of rapidly
accessing and cross-searching chemical biological databases, visualizing [40] and
analyzing HTS results, launching modeling and screening computations on remotely
controlled workstations, and sharing chemical information in the cloud.
In summary, chemical biological data streams produced by modern drug discovery platforms are reaching unprecedented levels of size, diversity, and complexity
that require the development of novel cheminformatics methods and tools to process and analyze them. This situation offers some unique perspectives for inventing a new generation of expert systems and decision making technologies. As the
profusion of experimental data expands in the coming years to fully reach the level
and requirements of so-called Big Data, joint efforts and collaborations between
experimentalists and modelers will not only continue to strengthen but will become
the key for successful drug discovery. Modelers will have to guarantee fast and
accurate accesses to the correct and integrated chemical information and generate
reliable experimentally-testable hypotheses ( e.g, compound X is active toward both
targets Y and Z to induce phenotype P). In return, experimentalists will have to
keep on guiding modelers for better understanding their practical needs so that the
computational technologies can be adapted accordingly. This transitioning period is
extremely important for the whole cheminformatics research community: from the
students starting to learn the basics and recognizing the need to know all molecular
modeling approaches [41], to well-established researchers already facing the new
scales of data complexity and associated opportunities. The era of Big Chemical
Data is finally here and therefore it seems that modelers have never been so close
to achieving the rational, computer-aided design of novel molecules with controlled
polypharmacology and safety profiles.