In Silico Molecular Modeling and Docking in Drug Development
Unlocking Drug Potential: In Silico Molecular Modeling and Docking in Drug Development
About Program:
The clinical application of drugs in the treatment of diseases has been limited by severe toxic side effects during administration of the drugs. Thus, it has been the objective of numerous studies to prepare better and safe drugs. Molecular modelling and docking could be utilized for generation of effective drug agents (inhibitor) against disease (target) in computer Aided Drug Design (CADD). As a result of this, a large number of candidate drugs have been developed till now. These methods provide the data set for development of computational models, for virtual screening and prediction of biological activity of the new drug to be developed in the near future. These computational models also reduce the number of candidate molecules that needs to synthesized and tested, reducing both cost and time in the process of drug development.
Also, the above said techniques will provide a unique platform to all the researchers working in this field to screen new molecules which are synthesized. The main problem of any drug and other agents, which substantially reduces their therapeutic usefulness, lies in their scant selectivity because these substances affect diseased and normal cells alike and lead to the appearance of adverse side effects. This could be elucidated by exploring the mechanism and mode of action of these agents with the receptor to calculate the probabilities of their existence, leading to deciphering their interaction, mechanism and activity which are very essential for the development of better and safe drugs.
Aim: The aim of the program is to emphasize molecular modelling and drug/receptor interaction to explore biological phenomena at the molecular level. Traditional drug treatments often suffer from severe side effects, driving the pursuit of safer alternatives. Molecular modeling and docking, key to Computer-Aided Drug Design, enable the creation of effective drug agents, leading to a plethora of candidate drugs. These methods streamline drug development by providing essential datasets for computational models, virtual screening, and predicting biological activity, reducing costs and time in the process.
Program Objectives:
- Understanding Molecular Interactions: Provide participants with a comprehensive understanding of the principles of molecular modeling and docking, including the underlying theories of molecular interactions between small molecules and biological targets.
- Mastery of Computational Tools: Familiarize participants with state-of-the-art computational tools and software used in molecular modeling and docking, enabling them to perform virtual screening, ligand-protein docking, molecular dynamics simulations, and structure-based drug design.
- Application to Drug Discovery: Train participants in the application of computational methods to identify potential drug targets, screen compound libraries, optimize lead compounds, and predict their pharmacological properties, with the goal of accelerating the drug discovery process.
- Integration of Experimental and Computational Approaches: Foster an interdisciplinary approach by integrating computational modeling with experimental techniques, allowing participants to validate and refine computational predictions through experimental validation.
- Critical Thinking and Problem-Solving Skills: Develop participants’ critical thinking and problem-solving skills, enabling them to analyze complex biological and chemical data, interpret computational results, and make informed decisions in drug discovery projects.
- Communication and Collaboration: Enhance participants’ ability to communicate effectively and collaborate with interdisciplinary teams of scientists, including medicinal chemists, biologists, pharmacologists, and bioinformaticians, to advance drug discovery projects.
- Ethical and Regulatory Considerations: Raise awareness of ethical issues and regulatory considerations related to computational drug discovery, including data privacy, intellectual property rights, and compliance with regulatory guidelines.
- Professional Development: Provide opportunities for participants to engage in research projects, internships, or industry collaborations to gain practical experience and enhance their professional development in the field of computational drug discovery.
What you will learn?
Day 1: Preparation of Protein Structure
Preparation of compound library
- Generating the 2D structure
- Generating the 3D structure
Energy and molecular properties calculation
- Force field and Energy minimization
- Properties based on Quantum Mechanics
Tools:
- ISIS Draw/ChemSketch
- Argus Lab
- Mopac
Day 2: Protein active site prediction
Overview of Protein and their interaction with ligand
- Overview of protein 3D structure file format
- Overview of small molecule databases
Protein Active site
- Protein Active site Properties
- Protein active site prediction
Tools:
- Protein Data Bank (PDB)/PDBSum
- PubChem/Drug Bank/Zinc Database
- Active site prediction tool
Day 3: Comparative modeling/homology modeling
- Protein 3D structure Prediction based on homology
- Ab initio structure prediction
Tools: Modeller & Online Tools for Ab initio structure prediction
Day 4: Structure Validation
- Protein Structure validation
- Ramachandran plot
Tools: SAVES server
Day 5: Docking and Interaction studies
- Structure Based Drug Design (SBDD)
- Ligand Based Drug Design (LBDD)
Tools: PyRx & ArgusLab
Fee Plan
Intended For :
- Educational Background: Applicants should typically have a bachelor’s degree or higher in a relevant field such as pharmaceutical sciences, chemistry, biochemistry, biotechnology, computational biology, or a related discipline.
- Prerequisites: Some programs may require applicants to have completed specific coursework in chemistry, biochemistry, molecular biology, or related subjects, to ensure they have a strong foundation in the principles underlying molecular modeling and drug development.
- Computer Skills: Proficiency in computer programming languages (e.g., Python, R), molecular modeling software (e.g., Schrödinger Suite, MOE, AutoDock), and data analysis tools is often necessary to effectively participate in the program.
- Research Experience: Applicants with prior research experience in molecular modeling, computational chemistry, or related fields may be given preference, as they are likely to have a deeper understanding of the concepts and techniques covered in the program.
- Letters of Recommendation: Some programs may require letters of recommendation from professors, supervisors, or professionals who can attest to the applicant’s academic abilities, research experience, and suitability for the program.
- Statement of Purpose: Applicants may need to submit a statement of purpose or personal statement outlining their academic background, research interests, career goals, and reasons for applying to the program.
- Language Proficiency: Proficiency in the language of instruction or communication used in the program may be required, especially if the program is conducted in a language other than the applicant’s native language.
- Application Materials: Applicants may need to submit materials such as transcripts, a resume or curriculum vitae (CV), standardized test scores (if applicable), and/or writing samples, depending on the requirements of the program.
Career Supporting Skills
Program Outcomes
- Mastery of Molecular Modeling: Attain a comprehensive understanding of three-dimensional structures and physicochemical properties of drugs and receptors.
- Practical 3D Structure Generation: Acquire skills in generating 3D structures from raw sequence data using tools like ISIS Draw and ChemSketch.
- In-depth Protein Insight: Gain an overview of protein structures, interaction with ligands, and active site prediction using Protein Data Bank (PDB) and relevant tools.
- Advanced Modeling Techniques: Develop proficiency in comparative modeling and homology modeling, utilizing tools such as Modeller and online resources.
- Structure Validation Expertise: Learn to validate protein structures using tools like the SAVES server and interpret results, including Ramachandran plot analysis.
- Hands-On Docking Studies: Explore Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD) through practical exercises using PyRx and ArgusLab.
- Strategic Drug Design: Understand the process of designing drugs based on known and unknown targets, bridging the gap between theory and application.
- Enhanced Research Skills: Develop the ability to critically analyze molecular interactions, contributing to advancements in drug development and virtual screening.
- Efficient Compound Library Preparation: Learn to prepare compound libraries, generate 2D and 3D structures, and calculate energy and molecular properties using various software tools.
- Practical Application: Apply acquired knowledge in a real-world context, empowering participants to contribute to the field of drug development through computational approaches.
