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Career opportunities in this field – Types of positions or designations

1.Principal Investigator : Key Responsibilities include providing a comprehensive and impactful CADD support to drug discovery programs, demonstrating an understanding of full spectrum of project SAR (DMPK, structural and computational , in vitro biology), working in close collaboration with medicinal chemists and presenting plans, progress and results at CADD, chemistry and project team meetings.

2. Drug design research scientist: This position desires to collaborate with interdisciplinary project teams to drive effective decision-making from target identification through candidate nomination by mining and developing predictive models using high-content and time-resolved screening data, including imaging and to keep oneself abreast of scientific literature and interact with internal and external scientists to integrate biological insights into lead characterization and screening efforts.

3. Computer-Aided Drug Discovery or Computational Chemist : Summary of duties include deep understanding of target biology, leading medicinal chemistry design efforts through knowledge of SAR, and application of predictive methods for physical properties, PK/PD, on- and off-target activity  and synthetic feasibility.

4. Molecular Modeler : Responsibilities include conducting support and development activities in areas such as computer aided drug-discovery, drug design, homology modeling, molecular dynamics, statistical analysis and related methods and procedures using open source solutions to support current and future needs in areas relevant to diseases such as cancer and HIV research and clinical studies.

5. CADD investigator : Candidates are expected to perform duties such as driving hypothesis generation to result in higher clinical success rates for programs utilizing small molecules peptides, RNAs, protein degradation, transient covalent inhibitors, molecular glues and kinetic stabilization of drug-target complexes.

6. Drug design Engineers: Job description includes detailing, design engineering, process modelling, device integration, simulation software etc and leading cross-disciplinary mechanistic studies using physics-based modeling and simulation, cellular confirmation and biophysical characterization.

7. Computational Biophysicists : Relevant areas of job duties might include investigation of conformational changes relevant to the function of protein kinases, structural modelling of protein-protein complexes involved in signal transduction or other cellular processes,  direct application of molecular dynamics or other computational methods to drug discovery, ion channels, or other biomolecules, or study of fundamental biophysical problems, such as protein and RNA folding.

8. Bioinformaticist in the health sector : Job description primarily includes developing analysis plans unitedly with members of the research team, conducting analyses in a timely fashion, deciphering results for the team as well as writing analytical strategies for peer-reviewed publications

9. Business Development Executive :  Not only orthodox jobs but one also gets a chance to handle businesses in this field. Most of the Pharma companies look for candidates with 2 – 6 yrs experience, who can identify business opportunities from the local and domestic market to expand their pipeline. Marketing skills are also desired so as to pursue annual sales in order to achieve quarterly and annual targets/budgeted revenues.

10. Research Technician : The personnel is expected to handle standardized molecular biological, microbiological or biochemical tests and laboratory analyses for drug discovery process, and is also responsible for designing and executing advanced experiments and setting strategy, along with developing, modifying and improving standard operating procedures.

11. Lectureship/Professorship: Job description includes teaching assigned courses to undergraduates and graduates, Phd programmes acting as a counsellor, mentor, trainer and educator. Other desirable skills are standard hands-on experience related to molecular modelling and drug designing techniques.

Some of the global pharmaceutical, healthcare and food giants working in this field are Aurobindo Pharma, Glenmark Generics, Cipla , Dr. Reddy’s Laboratories, Foster Corporation,  Abbott,  SeQuent,  Medtronic, Wockhardt, Pfizer, IPCA Laboratories, Calyx, Mother Dairy, Bliss GVS Pharma, Al Rawabi, Almarai, Green Pastures, PepsiCo India, Allergy Therapeutics, CFTRI, Ciron, Sun Pharmaceutical, Novartis, GlaxoSmithKline, Ranbaxy , Biocon Mankind, Beryl Drugs etc

Drug, discovery and development is an intense, lengthy and an interdisciplinary endeavor. The contribution of computational methodologies has revolutionized the process of drug discovery. CADD is a powerful tool in the study of relationship between molecular structure and biological activity and thus essential in the process of rational drug design. Most pharmaceutical and biotechnological companies use computational design tools. This technique will provide a unique platform to all researchers working in this field to screen new drug entities.

This course is for both beginners and experienced those who want to learn about the details of CADD and latest developments in healthcare. Basic understanding of biochemistry and bioinformatics is essential for this course.

Since this subject is a qualitative discipline relying on a working knowledge of many varied subjects, any Biotech / Pharmaceuticals / Bioinformatics / Chemistry and allied programmes and research scientists in biotechnology and pharma industries and clinicians / medical practioners who is EAGER TO LEARN to stay in line and understanding prevailing tools of drug discovery better is eligible to take up this course.

• Students in their B.Sc / B.Tech / M.Sc / M.Tech/ PhD in any life science field, B.pharm/M.pharm/MBBS who are interested in taking up drug discovery as a career considering the present scenario
• Aspiring researchers to want to contribute towards public health by designing, generating and evaluating effective drug agents (inhibitor) against disease (target) through computational techniques
• Science enthusiasts who like to stay updated about the recent trends and updates regarding one of the most talked topics worldwide

Everyone has only one question these days: Is there any drug to treat COVID-19?

And if yes, when is a drug entering into the market to treat COVID-19?

According to various reports, approximately 90 drugs and vaccines are in clinical trials now. And the number is increasing day by day!

Drug, discovery and development are an extremely intense, expensive, lengthy and an interdisciplinary endeavour. Several drug candidates that enter clinical trials fail because of varied reasons. The contribution of Artificial Intelligence and computational methodologies has revolutionized the process of drug discovery. This has become a powerful tool in the study of the relationship between molecular structure and biological activity and thus essential in the process of rational drug design.

Computer assisted drug design or CADD speeds up this process, reducing surprises and predicting the properties, thereby reducing the cost of R&D. These days, major world’s pharmaceutical and biotechnological companies use computational design tools.

Figure : An illustration showing the different stages involved in developing a drug

The first stage in the drug development process is drug discovery. Some drugs have been discovered by accident, in the past for example, penicillin.

Now a days, more systematic approaches are used, such as:

• High-throughput screening by which scientists are able to test many potential targets with thousands of diverse chemical compounds to identify a new drug-target combination.
• Rational drug design by which designing and synthesising compounds based on the known structure of a specific target molecule can be done.

Although hundreds of potential lead components can be identified by high-throughput screening, many will be eliminated at the first round of testing. During these rounds, compounds are tested in cultured cells or animals to find out how effective they are and if they have any toxic effects. Compared to high-throughput screening, rational drug design develops fewer compounds. These compounds are however, very specific to the target and use computer-based modelling to achieve this specificity

Computational approaches are beneficial tools to guide and interpret various experiments to accelerate the drug design process.

The two general types of computer-aided drug design (CADD) approaches in existence

• Structure based drug design (SBDD)
• ligand based drug design (LBDD).

SBDD method identifies key sites and interactions that are important for their respective biological functions, by analyzing the macromolecular target 3D structural information, typically of RNA or proteins. This information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s).

LBDD method focuses on an approach called as the structure-activity relationship (SAR) where the known ligands for a target are used to establish a relationship between their physiochemical properties and antibiotic activities. This information can be used for optimization of known drugs or guide the design of new drugs with improved activity.

Figure : Traditional workflow of structure-based drug design (SBDD) and ligand-based drug design (LBDD)

The availability of the experimentally determined 3D structures of target proteins determines the choice of CADD approaches to be employed. The knowledge of the target protein structure to calculate interaction energies is used in structure based CADD, whereas in ligand-based CADD, chemical similarity searches or construction of predictive, quantitative structure-activity relationship (QSAR) models exploits the knowledge of known active and inactive molecules. Structure based CADD is a combination of information from various fields such as X-ray crystallography and/or NMR, synthetic organic chemistry, molecular modelling, QSAR, and biological evaluation. Structure based CADD aims at designing compounds having strong binding affinity with the target, thus exhibiting properties like improved DMPK/ADMET properties, reduced free energy and target specification i.e., reduced off-target. One of the most common applications of CADD is the virtual high-throughput screening (vHTS) also known as screening of virtual compound libraries.

CADD assists scientists in minimizing the synthetic and biological testing efforts by focussing only on the most promising compounds. It also predicts possible derivatives that would improve activity, besides explaining the molecular basis of therapeutic activity. CADD entails:

• To streamline the drug discovery and development processes by the use of computing power.
• Using combination of chemical and biological information about targets and/or ligands to identify and optimize new drugs.
• Elimination of undesirable compounds with properties like poor activity and/or poor absorption, distribution, metabolism, excretion and toxicity, ADMET through In silico designing of filters ; which facilitate selection of the most promising candidates.

Promising advantages of drug discovery through CADD include:

• Selecting smaller set of compounds from large compound libraries through experimental testing.
• Optimization of lead compounds by increasing drug metabolism and pharmacokinetics (DMPK) properties like ADMET –  absorption, distribution, metabolism, excretion and the potential for toxicity
• Newer compound designing can be achieved by “growing” starting molecules(one functional group at a time) or even by piecing together fragments into novel chemotypes
• Any form of traditional experimentation requiring animal and human models can be replaced by CADD, saving both time and cost
• CADD minimizes the chances of drug resistance and thus leads to production of lead compounds which would target the causative factor.
• CADD aims at constructing high quality libraries and datasets which can be optimized for higher molecular diversity or similarity

In the past few years, the success stories of CADD in drug discovery demonstrated the utility in the process of drug development. CADD provides beneficial information about target molecules, lead compounds, screening and optimization. Latest advancements such as combinatorial chemistry, QSAR, different databases and available new software tools provide a basis for designing of ligands and inhibitors that require specificity. The backbone of the CADD process include various approaches like pharmacophore modelling, docking homology modelling. Understanding the three-dimensional aspects of drug – receptor interaction on the molecular basis and accessing the medicinal chemistry in designing of new therapeutic agents is also one of the utilities of computational chemistry.

CADD helps in providing information about the chemistry of the chemical entities which is quite inaccessible through laboratory experiments, thus reducing cost and labour. In near future, CADD will certainly improve quality of research and facilitate the development of numerous drugs.

Some of the most essential skills for becoming a successful computational medicinal chemist in today’s world are to master different kinds of CADD approaches and their software and utilizing all computational resources that are valuable for drug design. Pharmacophore modelling and docking are also very likely to become routine in drug-discovery projects if they are not considered to have already done so. The use of more accurate methods like molecular dynamics and Quantum Mechanics, continue to grow.

In summary, CADD is extremely helpful for pharmaceutical development in areas such as prediction of design of compounds, prediction of druggability, 3D structures and in silico ADMET prediction. This also requires that computational predictions must be integrated with experimental approaches for successful drug discovery and development.

This particular CADD course covers major computational techniques which are used in drug discovery, supplying a basic level of knowledge of this research field.

These techniques can provide a unique platform to all researchers working in this field to screen new drug entities. Owing to its rapid development rate, there is a vast array of jobs in this sector of Designing & Molecular Modeling, no wonder!

Happy Learning!