In Silico drug designing and role of bioinformatics

  • “In Silico” is an expression used to mean “performed on computer or via computer simulation.” 
  • In Silico drug designing is thus the identification of the drug target molecule by employing bioinformatics tools.

drug designing

  • The inventive process of finding new medications based on the knowledge of a biological target is called as drug designing. It can be accomplished in two ways:

Ligand based drug design

  • Relies on knowledge of other molecules that bind to the biological target of interest.

Structure-based drug design

  • Relies on knowledge of the three-dimensional structure of the biological target obtained through methods such as homology modeling, NMR spectroscopy, X-ray crystallography etc.
  • Drug discovery process is a critical issue in the pharmaceutical industry since it is a very costly and time-consuming process to produce new drug potentials and enlarge the scope of diseases incurred.
  • In both methods of designing drugs, computers and various bioinformatics tool come handy. Thus, in silico drug designing today is very crucial means to allay the arduous task of manual and experimental designing of drugs.
  • In silico technology alone, however, cannot guarantee the identification of new, safe and effective lead compound but more realistically future success depends on the proper integration of new promising technologies with the experience and strategies of classical medicinal chemistry.

The Process of Drug Designing

The drug discovery process involves the identification of the lead structure followed by the synthesis of its analogs, their screening to get candidate molecules for drug development.

In the traditional drug discovery process, the steps include:

  1. Identification of the suitable drug target which are biomolecules mainly including DNA, RNA and proteins (such as receptors, transporters, enzymes and ion channels).
  2. Validation of such targets is necessary to exhibit a sufficient level of ‘confidence’ and to know their pharmacological relevance to the disease under investigation. This can be performed from very basic levels such as cellular, molecular levels to the whole animal level.
  3. Identification of effective compounds such as inhibitors, modulators or antagonists for such target is called lead identification where the design and development of a suitable assay is done to monitor the effect on the target under study.
  4. Compounds showing dose-dependent target modulation in terms of a certain degree of confidence are processed further as lead compounds.
  5. Subsequently, the experiments are performed on the animal models in the laboratories and the positive results are then optimized in terms of potency and selectivity.
  6. Assesing of the physicochemical properties, pharmacokinetic and safety features are also assessed before they become candidates for drug development.

Even though most of the processes depend on experimental tasks, in silico approaches are playing important roles in every stage of this drug discovery pipeline which are described below:

In silico Methods in Drug Discovery and the role of Bioinformatics

  • In silico drug design represents computational methods and resources that are used to facilitate the opportunities for future drug lead discovery.
  • The explosion of bioinformatics, cheminformatics, genomics, proteomics, and structural information has provided hundreds of new targets as well as new ligands.

The Role of Bioinformatics

  • Bioinformatic techniques hold a lot of prospective in target identification (generally proteins/enzymes), target validation, understanding the protein, evolution and phylogeny and protein modeling.
  • Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance.
  • One of the major thrusts of current bioinformatics approaches is the prediction and identification of biologically active candidates, and mining and storage of related information.
  • It also provides strategies and algorithm to predict new drug targets and to store and manage available drug target information.
  • In molecular docking:
    • Docking is an automated computer algorithm that attempts to find the best matching between two molecules which is a computational determination of binding affinity between molecules.
    • This includes determining the orientation of the compound, its conformational geometry, and the scoring. The scoring may be a binding energy, free energy, or a qualitative numerical measure.
    • In some way, every docking algorithm automatically tries to put the compound in many different orientations and conformations in the active site, and then computes a score for each.
    • Some bioinformatics programs store the data for all of the tested orientations, but most only keep a number of those with the best scores.
    • Docking can be done using bioinformatics tools which are able to search a database containing molecular structures and retrieve the molecules that can interact with the query structure.
  • It also aids in the building up chemical and biological information databases about ligands and targets/proteins to identify and optimize novel drugs.
  • It is involved in devising in silico filters to calculate drug likeness or pharmacokinetic properties for the chemical compounds prior to screening to enable early detection of the compounds which are more likely to fail in clinical stages and further to enhance detection of promising entities.
  • Bioinformatics tools help in the identification of homologs of functional proteins such as motif, protein families or domains.
  • It helps in the identification of targets by cross species examination by the use of pairwise or multiple alignments.
  • The tools help in the visualization of molecular models.
  • It allows identifying drug candidates from a large collection of compound libraries by means of virtual high-throughput screening (VHTS).
  • Homology modeling is extensively used for active site prediction of candidate drugs.


  1. Arthur M Lesk (2014). Introduction to bioinformatics. Oxford University Press. Oxford, United Kingdom
  3. Rao and K. Srinivas. Modern drug discovery process: An in silico approach. Journal of Bioinformatics and Sequence Analysis, 2, 2011, 89-94.

About Author

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Sagar Aryal

Sagar Aryal is a microbiologist and a scientific blogger. He attended St. Xavier’s College, Maitighar, Kathmandu, Nepal, to complete his Master of Science in Microbiology. He worked as a Lecturer at St. Xavier’s College, Maitighar, Kathmandu, Nepal, from Feb 2015 to June 2019. After teaching microbiology for more than four years, he joined the Central Department of Microbiology, Tribhuvan University, to pursue his Ph.D. in collaboration with Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Saarbrucken, Germany. He is interested in research on actinobacteria, myxobacteria, and natural products. He has published more than 15 research articles and book chapters in international journals and well-renowned publishers.

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