Cheminformatics combines chemistry, computer science, and information science to study large amounts of chemical information, mostly with computer assistance. It encompasses the design, creation, organization, storage, retrieval, analysis, and use of chemical data. Cheminformatics has various applications including drug discovery. It uses tools like databases, machine learning, molecular properties predictions, and information analysis to help identify new drug leads. Future trends include increased data integration, computer-assisted synthesis design, and expanded use of cheminformatics methods in theoretical chemistry and protein studies. Cheminformatics plays an important role in modern drug development.

1. SUBMITTED BY:
PALLAVI GUPTA
BIOINFORMATICS

2. CHEMOINFORMATICS
AND ITS APPLICATION IN DRUG DEVELOPMENT

3. DRUG DISCOVERY AND DEVELOPMENT
PROCESS OF DRUG DEVELOPMENT
TOOLS USED FOR DRUG DEVELOPMENT
APPLICATIONS OF DRUG DISCOVERY IN
MODERN ERA
CONCLUSION
HISTORY OF CHEMOINFORMATICS
WHAT IS CHEMOINFORMATICS?
MAJOR ASPECTS OF CHEMOINFORMATICS
COMMON CHEMINFORMATICS PATTERN
APPLICATIONS OF CHEMOINFORMATICS
CHALLENGES TO CHEMOINFORMATICS
CHEMINFORMATICS AND OTHER DISCIPLINES
FUTURE TRENDS OF CHEMINFOMATICS
Contents

4. HISTORY OF
CHEMOINFORMATICS
Cheminformatics is the mixing of those
information resources to transform data into
information and information into knowledge for
the intended purpose of making better
decisions faster in the area of drug lead
identification and optimization.
The term cheminformatics was defined in its
application to drug discover, for instance, by
F.K. Brown in 1998
Cheminformatics combines the scientific
working fields of chemistry, computer science,
and information science—for example in the
areas of topology, chemical graph
theory, information retrieval and data mining in
the chemical space. Cheminformatics can also
be applied to data analysis for various
industries like paper and pulp, dyes and such
allied industries.

5. WHAT IS CHEMOINFORMATICS ?
.
WHY DO WE NEED
CHEMOINFORMATICS
? To get funding
To handle large amount of information
Data information knowledge
To move chemistry into the computer age
Cheminformatics (also known
as cheminformatics and chemic
al informatics) is the study
of large amounts of chemical
information. It is done mostly
with the help of computers.
These tools are used
by pharmaceutical companies
to discovery new drugs.
Cheminformatics
encompasses the design ,
creation , organization
,management , retrieval ,
analysis , dissemination ,
visualization , and use of
chemical information.
To move from data to knowledge
Measurements/Calculations

6. MAJOR ASPECTS OF CHEMOINFORMATICS
Databases: Development of databases
for storage and retrieval of small
molecule structures and their properties.
04
Machine learning: Training of decision trees,
neural networks, self organizing maps, etc.
on molecular data.
05
Predictions: Molecular properties relevant to drugs,
virtual screening of chemical libraries, system
chemical biology networks.
06
Information Acquisition is a process of generating
and collecting data empirically or from theory
(molecular simulation)
01
Information management deals with
storage and retrieval of information.
02
Information use which includes Data Analysis,
correlation, and application to problems in the
chemical and biochemical sciences.
03

7. Markush
structure or
generic
structure
This is a topological pattern
used by chemists for many
years. It is determined by
experience. It is an efficient
way to represent an
unlimited number of
compounds with the same
scaffold. Additional
restrictions can be applied to
make the pattern more
specific. It is suitable for lead
optimization and hit-to-lead
efforts.
Fingerprint This is the topological pattern
systematically generated
from an algorithm. This
pattern has no human bias,
but can be meaningless to
chemistry. It is used in HTS
data mining.
Three-
dimensional
pharmacophore
This pattern is derived,
manually or computationally,
from a three-dimensional
molecular model. The
pattern is based upon a
physical model and binding
mechanism. It is sensitive to
conformation changes.
Better results are obtained
when supported by crystal or
NMR structural data. It is
suitable for lead
optimization.
COMMON CHEMINFORMATICS PATTERNS

8. Regression Regression methods are the most
traditional approaches for pattern
recognition. These methods assume
the variables are continuous and
the curve shapes are pre-defined.
For multidimensional data, curve
patterns are not known and trying
all possible curves is very time
consuming. In these cases, genetic
algorithms may be applied to
partially solve the problem of
identifying curve patterns.
Decision tree
classification
This approach is applied when there
are a great number of descriptors
and, the descriptors have various
value types and ranges.
Hierarchical
clustering
This approach assumes the objects
have hierarchical characters. The
methods require similarity or
distance matrices. The approach
may produce multiple answers for
users to explain or with which to
experiment.
Non-
hierarchical
clustering
The approach assumes the objects
have non-hierarchical characters,
and the number of clusters is
known prior the computation. The
method requires similarity or
distance matrices. The approach
may produce multiple answers for
users to explain or with which to
experiment.

9. APPLICATIONS OF CHEMOINFORMATICS
QUANTITATIVE STRUCTURE
ACTIVITY RELATIONSHIP :
predict activities of compounds
from their structure.
VIRTUAL LIBRARIES :
chemical data can pertain to
real or virtual molecules.
VIRTUAL SCREENING :
identification of novel active
molecules in large compound
databases.
STORAGE AND RETRIEVAL :
helps in storage , indexing &
search of information.
FILE FORMATS : uses
formats like SDF:2D &
3D, Mol:2D,
Mol2:3D,Xml

10. C H A L L E N G E S
TO
CHEMOINFORMATICS
Three Fundamental Questions
of A Chemist:
The fundamental and
lasting objective of synthesis is
not production of new
compounds but production of
properties.
If this is accepted, chemists
have to face three fundamental
questions.
a. Which compound will
have the desired property?
b. How can I make this
compound?
c. Did I make this
compound (what is the product
of my reaction)?
Toxicity Prediction and Risk
Assessment:
Society has become increasingly
interested and concerned about the
impact of chemicals on the
environment and on human health.
Therefore, chemicals should be
introduced into the market or used
only if they have been proven to be
safe.
Furthermore, the impact of
chemicals on the environment is of
much concern in society. Models for
persistence, bioaccumulation and
toxicity of chemicals in all kinds of
organisms are asked for.
Modeling Biological Systems:
The next step is then a focus on unraveling the events
in living organisms. It should be realized that life is
maintained by (bio)chemical reactions; modeling and
understanding them is essential for getting deeper
insights into the events that keep living species alive.
This could provide a basis for curing diseases. New
research fields have been conceived of, with names
such as systems chemistry, systems biology and
systems chemical biology. Whatever the names, the
aim is to further an understanding of biological
systems even to a point that they can be altered. A
collaboration between cheminformatics and
bioinformatics is essential for this purpose.
The newest developments aim at modeling entire
human organs. Large-scale government-supported
projects in the U.S. and in Germany have been
initiated to develop models for the human liver, the
virtual liver v-Liver at EPA [ and the German virtual
liver project networks

11. CHEMOINFORMATICS AND OTHER DISCIPLINES
Finally, one can use the relationships
between different properties issued
from physicochemical theory. (For
example, the Arrhenius law could be
particularly useful upon the modelling the
rate constants). These relationships
could be integrated into
cheminformatics workflow as an external
knowledge.
The second important distinction comes from the
fact that the chemical data result from an explorative
process in a huge chemical space rather than from
specially organized sampling. Hence, they cannot
be considered as representative, independent
and identically distributed sampling from a well
defined distribution. Thus, special approaches are
Cheminformatics as a Theoretical Chemistry
Discipline needed to treat this problem: various
strategies to explore chemical space, the
“applicability domain” concept, the active learning
approach, etc.
Cheminformatics and Bioinformatics
Unlike cheminformatics dealing with “chemical size”
molecules, bioinformatics uses computational tools to study
the structure and function of biomolecules (proteins, nucleic
acids). This is a broad field mostly involving 3D (force field
and quantum mechanics calculations) and 1D (sequence
alignment) modeling. In the latter, a biomolecule is
represented as a string of characters (building blocks).
Graph and fixed size vector models used in cheminformatics
are very rarely used in bioinformatics. In this sense, chemo
and bioinformatics are “complementary”.
Cheminformatics and Machine learning although
machine learning is widely used for structure property
modelling, cheminformatics can be considered as a very
specific area of its application. The specificity of
cheminformatics results from (i) the nature of chemical
objects, (ii) the complexity of the chemical universe and
(iii) a possibility to take into account an extra-knowledge.
The basic chemical object is a graph (or hyper graph),
rather than simple fixed-sized vector of numbers as in
the typical applications in mathematical statistics and
machine learning. This dictates the need to apply graph
theory, to develop novel descriptors and structured
graph kernels, and to apply machine learning methods
capable of dealing with structured discrete data.

12. FUTURE TRENDS OF CHEMINFORMATICS
5. Text and image mining, automatic extraction of
useful information from publications and patents.
6. Integration with bioinformatics, with focus on
ligand protein interactions and pharmacophores
7. Disappearing border between cheminformatics
and computational chemistry
8. In technology area –modularization, web services
9.Open source collaborative software development
10. Using all the advanced cheminformatics system,
it enhances the drug discovery rapidly and with low
cost and helps to eminent scientists to synthesize
the chemical molecules which lead to helps the
society.
1. Global databases, integration of multiple data
sources, public (Wikipedia-like) curation.
2. Use of Computer Assisted Structure
Elucidation (CASE) process and Computer
Assisted Synthesis Design (CASD) would be
integrated into the daily work process of bench
chemists.
3. Cheminformatics methods will be extended to
theoretical chemistry, stimulation of reaction;
study of proteins will be the future areas of thrust
for cheminformatics.
4. Use of large chemo genomics databases
(WOMBAT, GVK …)

13. DRUG
DEVELOPMENT

14. DRUG DISCOVERY AND DEVELOPMENT
Drug is an active chemical substance used for diagnosis, mitigation,
treatment and prevention(DMTP) of diseases of humans and animals.
Also includes chemical substances, diagnostic, abortive and
contraceptive substances.
Drug discovery is the process of identifying new medicines.
Drug discovery take years to decade for discovering a new drug
and very costly.
It involves a wide range of biological, chemical and
pharmacological disciplines.

15. PROCESS OF DRUG DISCOVERY

16. TOOLS USED FOR
DRUG DEVELOPMENT
The development of software and tools for computer assisted
organic synthesis are under vast development. This has
resulted in many tools and representations for chemical
structures. Some of the tools are listed below :
Chemdraw
Chemwindow
Chemreader
Chemsketch
logchem
wendi
chemmine
pubchem
open babel
Some other tools such as, CAS Draw, DIVA (Diverse
Information, Visualization and Analysis), Structure
Checker Accord, DS Accord Chemistry Cartridge,
MarvinSketch PowerMV, TINKER, APBS, ArgusLab,
Babel, ioSolveIT, ChemTK, Chimera, CLIFF, Dragon,
gOpenMol, Grace, JOELib, Jmol, IA_LOGP, Lammps,
MIPSIM, Mol2Mol, AMSOL, MOLCAS, Molexel, ICM-
Pro, ORTEP, Packmol, Polar, XLOGP,PREMIER
Biosoft, Q-chem, ALOGPS, Qmol, SageMD, ChemTK
Lite, Transient, CLOGP,TURBOMOLE, UNIVIS, VMD,
WHATIF, GCluto, COSMOlogic, KOWWIN are also
used.

17. APPLICATIONS OF DRUG DISCOVERY IN MODERN ERA
1 2 3 4
Drugs function by
interacting with
multiple protein
targets to create a
molecular interaction
signature that can be
exploited for rapid
therapeutic
repurposing and
discovery.
Developers designed
and synthesized a
series of nine
enmein-type ent-
kaurane diterpenoid
and furoxan-based
nitric oxide (NO)
donor hybrids from
commercially
available oridonin.
Feng Xu and coauthors
employed the 3D culture of
MCF-7 and SMMC-7721
cells based on the hanging
drop method and evaluated
the anti-proliferative activity
and cellular uptake of two
promising anti-tumor drug
candidates, evodiamine
(EVO) and rutaecarpine
(RUT), in 3D multicellular
spheroids and compared the
results with those obtained
from 2D monolayers
Rizk E. Khidre and
colleagues designed
and synthesized a
novel series of
quinoline compounds
and screened for
their antimalarial
activities, with the
hope that these
compounds could
lead to the
availability of better
drugs to treat
malaria.
5
Jun-Ru Wang and coauthors studied
a new series of ester derivatives of
10-hydroxycanthin-6-one using a
simple and effective synthetic
route as part of their continuing
research on canthin-6-one
antimicrobial agents. They
characterized the structure and
antimicrobial activity of each
compound, investigated the
structure-activity relationship, and
identified the promising lead
compound that had significant
antimicrobial activity against all the
fungi and bacterial strains tested for
the development of novel canthine-
6-one antimicrobial agents.

18. Cheminformatics can hence be described as the application of
informatics methods to solve chemical problems. It has developed
over the last 40 years to a mature discipline that has applications in
many areas of chemistry. It is an important scientific discipline that
stands on the interface between chemistry, biology and Information
Technology. Cheminformatics spans a very broad range of
problems and approaches which are often inter-related and
sometimes difficult to categorize. As high throughput technologies
and combinatorial chemistry continue to advance, informatics
techniques will become indispensable in managing and analyzing
the exploding volumes of data. By organizing, the data,
Cheminformatics will further introduce advancements in chemistry
and open new possibilities in the field of drug discovery. There are
still many problems that await a solution and therefore many new
developments in cheminformatics are foreseen. We believe that
this review will be the defining theme and might help to provide
much new advancement in the field of cheminformatics in coming
years. Hopefully, the availability of information related to
cheminformatics will catalyze further advancements and would
open new advancements in this field.
CONCLUSION

19. Thank You