Role of bioinformatics in different steps of drug discovery and drug designing. Various tools and software used in Computer-aided drug designing.

1. W Roseybala Devi
19L10921
MSc Bioinformatics, Year-II
JSS Academy of Higher Education and Research, Mysore

2. 1. Understanding Bioinformatics
2. Drug discovery
3. Bioinformatics in Drug design
4. Softwares & Databases used in drug discovery
5. Conclusion
6. References

3. • The term was first introduced in the 1990s.
• Originally, Bioinformatics: the management &
analysis of data pertaining to DNA, RNA & protein sequences.
• In present, Bioinformatics: creation & advancement of Databases, algorithms,
computational and statistical theories to solve formal & practical problems arising
from the management and analysis of Biological data.
• In the past deacade, devt. in genomics, molecular research technologies & devt in
information technologies have combined together to produce a tremendous amount of
information related to molecular biology.
• Hence, Bioinformatics is the name given to the mathematical and computational
approaches used to increase the understanding of biological processes.

4. Mapping & analyzing DNA &
protein sequences
Aligning DNA & protein
sequences
Creating & viewing 3-D models
of protein structures

5. 1. Sequence mapping of biomolecules (DNA, RNA, proteins).
2. Primer designing
3. Prediction of functional gene products.
4. Trace evolutionary trees of genes.
5. Prediction of protein structure.
6. Molecular Modeling of Biomolecules
7. Development of models for the functioning of cells, tissues & organs.
8. Discovery & designing of drugs for medical treatment.

6. • a substance which has a physiological effect when ingested or
otherwise introduced into the body.
• all medicines are drugs, whereas not all drugs are medicines.
• a medicine imparts a positive medical effect on a patient.
• A drug, in contrast to a medicine, can have a positive or negative
effect on a patient.
• Desirable drug
• Bioinformatics facilitate the discovery of such desirable drugs.

7. • it is the process through which potential
new medicines are identified.
• In the past, most drugs have been discovered
by identifying the active ingredients feom traditional remedies or by serendipitous
discoveries.
• new approaches helps in understanding how diseases & infections are controlled at the
molecular and physiological level and to target specific entities based on this knowledge.
• the process of drug discovery involves the identification of candidates, synthesis,
characterization, screening and arrays for therapeutic efficacy.
• Drug discovery starts with diagnosos of a disease with well characterized symptoms that
reduce the quality of life.

8. • The discovery of new pharmaceutical drugs is one of the preeminent tasks—
scientifically, economically, and socially—in biomedical research.
• Advances in informatics and computational biology have increased
productivity at many stages of the drug discovery pipeline.
• Drug discovery has slowed, largely due to the reliance on small molecules as
the primary source of novel hypotheses.
• Bioinformatics facilitate the discovery of desirable drugs.

9. Omics Function Databases
Genomics Understanding pathogenesis, Identification of disease genes,
Discovery of putative drug targets, Patient-centered efficacy and
toxicity assessment of drugs/targets.
GWAS Catalog
GWAS central
dbGaP
PharmGKB.
Transcriptomics Disease mechanisms, Mode of action of compounds, Moving from
disease genes to drug targets, Identification/evaluation of drug
target candidates, Early prediction of adverse drug target effects.
DrugMatrix
TG-GATE
LINCS 1000
Expression Atlas
GEO repository
ArrayExpress
Proteomics Post-translational process, Protein–protein network interaction,
Drug target efficacy and safety evaluation at protein level, Protein
toxicology.
PRIDE Archive
Peptide Atlas
ProteomicsDB
Human Proteome Map
Human Proteome Atlas
Metabolomics Novel DTD, Drug target efficacy and safety evaluation at
metabolomic level, Metabolic toxicity.
Human Metabolome
Madison Metabolomics
Golm Metabolome Databas
MassBank
MetaboLights
MetabolomeExpress
OmicsdataandtheirusesinDrugDiscovery

10. • Lead optimization aims at enhancing the most promising
compounds to improve effectiveness, diminish toxicity, or increase
absorption.
Drug optimization
• A study to test a drug, a procedure, or another medical treatment
in animals.The aim of a preclinical study is to collect data in support
of the safety of the new treatment.
Preclinical trials
• A clinical trial is only done when there is good reason to believe that a new
test or treatment may improve the care of patients. Clinical trials are
research studies that involve people.
Clinical trials
• Target identification is the process of identifying the direct molecular
target.It aimed at finding the efficacy target of a drug/pharmaceutical or
other xenobiotic.Target identification
• The process involves the application of a range of techniques that aim to
demonstrate that drug effects on the target can provide a therapeutic
benefit with an acceptable safety window.
Target validation
• Compound Screening is defined as the identification of compounds that
could be promising candidates for drug development, before it advances to
the more-costly stages of preclinical and clinical trials.
Compound screening

11. • Bioinformatic analysis accelerate drug target identification and drug candidate
screening and refinement,and facilitate characterization of side effects and
predict drug resistance.
• uses high-throughput molecular data in comparison between patients, animal
disease models, cancer cell lines & normal controls.
• Key objective:
i. to connect disease symptoms to genetic mutation, epigenetic modification
& other environmental factors modulation of gene expression.
ii. to identify drug targets that can either restore cellular function or eliminate
malfunctioning cells.
iii. predict or refine drug candidate to achieve the designed therapeutic results
and minimize side effects.
iv. access the impact on environmental health & the potential of drug
resistance.

12. Role of Bioinformatics in drug discovery
Steps in Drug discovery Role of Bioinformatics
Target identification bioinformatics plays a key role in the exploitation of genomic, transcriptomic, and
proteomic data to gain insights into the molecular mechanisms that underlie disease
and to identify potential drug targets.
Target validation rational and efficient mining of the information that integrates knowledge about
genes and proteins is necessary for linking targets to biological function. Structural
information from raw sequence data, helps in identification or design of target-
specific ligands. (Gene logic, Immusol, Aptamers}
Compound Screening rational index of drug desirability (Idd) in phenotypic screening.
virtual high-throughput screening:protein targets are screened against databases of
small-molecule compounds to see which molecules bind strongly to the target. ZINC
is a good example of a vHTS compound library
Drug optimization screening of compound libraries.chemical structure of a confirmed hit is extensively
optimized to produce a preclinical drug candidate. (Comprehensive medicinal
chemistry, Drug bankPharmaGKB)
Pre-clinical and trials Big Data Management (BDM)solve the current data storagevarious clinical
Data.management tools and software available in the market are clinical conductor
CTMS (by Bio-Optronics), Clindex, Ascend (by Biopharm).

13. • The drug discovery process ends when one lead compound is found for a
drug candidate, and the process of drug development starts.
• Drug design is the inventive process of finding new medications based
on the knowledge of a biological target.

14. Bioinformatics and drug designing approaches
Approaches Role of Bioinformatics
Ligand-based approach 3D quantitative structure activity relationships (3D QSAR) and pharmacophore
modeling are the most important and widely used tools in ligand based drug
design. They can provide predictive models suitable for lead identification and
optimization
Target-based approaches • essential genes (well annotated genome). Essential genes are often highly
conserved and can be revealed by genomic comparisons between pathogens
and their phylogenetic relatives.
• to check if such essential genes have homologues in the host. If they do, then
inhibiting such essential genes in the pathogen
• to minimize the chance of pathogen developing drug resistance. Finding
pathogenecity is and bioinformaticians have created database facilitate the
identification pathogenicity islands as drug targets.
De novo approaches UCSF, DOCK, FLOG, GOLD, LEGEND18, LUDI19, SPROUT20, GRID
Structure Based Drug designing Homology modeling. MODELLERSwissModel RAMPCOMPOSER

15. • A compilation of software, databases & web services dirctly related to drug discovery are
found at http://click2drug.org/ maintained by Swiss Institute.
click2drug.org

18. • many software packages are powerful and free and supported by well known
institutions.
ChEMBL & SwissSidechain Databases
UCSF Chimera software tool 3D visualization
SwissSimilarity virtual screening
SwissBioisostere for ligand design
SwissTargetPrediction webserver to accurately predict the targets
of bioactive molecules based on a
combination of 2D and 3D similarity
SwissSideChain Database for studying, modeling or viualizing
non-natural amino acids
SwissDock for docking drug candidates (small molecules)
on proteins

19.  Novel approaches in Bioinformatics and development of software packages and
databases have expanded our views to the biological system, just as the
microscopes and telescopes have extended our views of patterns we have never
seen before.
 Drug discovery, drug development and commercialization are complicated
processes. Bioinformatics tools availability have made positive effect on overall
process and they can accelerate various steps of drug designing, and reduce the cost
and over all time.

20. 1. Xia X. (2017). Bioinformatics and Drug Discovery. Current topics in medicinal chemistry, 17(15), 1709–1726.
https://doi.org/10.2174/1568026617666161116143440
2. Parul Kumar. Bioinformatics: History, Coverage, Components and Applications. BiologyDicsussions.
https://www.biologydiscussion.com/biodiversity/bioinformatics/bioinformatics-history-coverage-components-and-applications/11294
3. Romano JD, Tatonetti NP. Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives. Front
Genet. 2019;10:368. Published 2019 Apr 30. doi:10.3389/fgene.2019.00368
4. Ségalat, Laurent. (2007). Loss-of-function genetic diseases and the concept of pharmaceutical targets. Orphanet journal of rare diseases. 2.
30. 10.1186/1750-1172-2-30.
5. Paananen J, Fortino V. An omics perspective on drug target discovery platforms [published online ahead of print, 2019 Nov 27]. Brief
Bioinform. 2019;bbz122. doi:10.1093/bib/bbz122
6. Gill, S. K., Christopher, A. F., Gupta, V., & Bansal, P. (2016). Emerging role of bioinformatics tools and software in evolution of clinical
research. Perspectives in clinical research, 7(3), 115–122. https://doi.org/10.4103/2229-3485.184782
7. Acharya, C., Coop, A., Polli, J. E., & Mackerell, A. D., Jr (2011). Recent advances in ligand-based drug design: relevance and utility of the
conformationally sampled pharmacophore approach. Current computer-aided drug design, 7(1), 10–22.
https://doi.org/10.2174/157340911793743547