Course

Unit 1

UNIT I- Computational Methods for Target Prediction 7 Hours

• Introduction to Computer Aided Drug History and Role of Bioinformatics and Chemo-informatics in drug discovery (1H)
• Classification of protein structures – Primary, Secondary, Super-secondary, Tertiary, and Quaternary Binding sites – Active Sites, Allosteric Sites, and Cryptic pockets. Sequence, Domains, Fold, and Motif of protein structure. (2H)
• Structural databases – UniProt, PDB, OPM, PubChem, ChEMBL (1H)
• Introduction to Network Application of network pharmacology, construction of the network, clustering, and topological analysis of network. Overview of KEEG and Gene Ontology (3H)

Unit 2

UNIT II-Quantitative Structure-Activity Relationships and Pharmacophore modelling: Applications 10 Hours

• Hansch analysis, Free Wilson analysis, and the relationship between them, Advantages and disadvantages. Deriving 2D-QSAR equations (2H)

• Physicochemical parameters and experimental methods to calculate physicochemical parameters: Hammett equation and electronic parameters (sigma) Lipophilicity effects and (log P, pi-substituent constant) Steric effects (Taft steric and MR parameters (2H)
• 3D-QSAR approaches and contour map analysis Statistical methods used in QSAR and pharmacophore modelling and importance of statistical parameters (3H)
• Concept of pharmacophore, pharmacophore mapping, identification of Pharmacophore features, and Pharmacophore modelling (2H)
• Conformational search used in pharmacophore mapping (1H)

Unit 3

UNIT III-Molecular Modelling and Docking 10 Hours

• Importance of 3D structure in drug Preparation of protein and ligand structures. (1H)
• In silico Structure Prediction

.1. Homology Modelling, Threading, Fold Recognition. Ab initio modeling. (2H)

.2. Sequence Alignment: DNA/protein sequences analysis, Alignment, pairwise and global alignment, Multiple alignment, structure-based alignment, Sequence alignment tools – BLAST, FASTA, CLUSTAL. (1H)

.3. Model refinement and validation (1H)

• Molecular docking theory and different methods of docking:

.1. Rigid and flexible docking, covalent docking, Induced fit docking. (2H)

.2. Analysis of protein-ligand interaction (1H)

• In silico approaches to ADME/T models, pharmaceutical issues, software tools in ADME/T prediction, limitations of in silico approaches (2H)

Unit 4

UNIT IV-Molecular Mechanics and Molecular Dynamics in Drug Design 10 Hours

• Introduction to Molecular mechanics and force General features of molecular potential energy surface;

.1. Local and global energy minima, saddle point, applications. (1H)

.2. First order and second order Energy Minimization methods (2H)

• Molecular Dynamics Types and uses of molecular dynamics simulation (3H)
• Binding free energy calculation using MM/GBSA and MM/PBSA (2H)
• Conformation search using molecular mechanics and molecular Comparison between global minimum conformation and bioactive conformation (1H)
• Introduction to quantum mechanics in drug design (1H)

Unit 5

UNIT V- Virtual Screening and Lead Optimization 8 Hours

• Virtual screening: Lead compound selection and lead optimization using virtual screening. Drug likeness and filtering methods. Rapid QSAR methods for virtual screening, Rapid molecular docking methods for virtual screening, structure-based in-silico virtual screening protocols (3H)
• Library design: Enumeration and Core hopping for lead optimization (1H)
• Importance of water molecules in drug design (1H)
• De novo drug design:

1. Receptor/enzyme interaction and its analysis, Receptor/enzyme cavity size prediction, predicting the functional components of cavities (1H)
2. Fragment based drug design (2H).

Computer-Aided Drug Design (CADD) is an interdisciplinary course that merges principles of chemistry, biology, and computer science to understand and apply computational tools in drug discovery and development. Through a combination of theoretical knowledge, practical experience, and exposure to the latest industry advancements, students will be well-prepared for advanced roles in pharmaceutical research and development. This course aims to equip students with the knowledge and skills necessary to utilize computational techniques for the design and optimization of new therapeutic compounds. Students will gain foundational knowledge of molecular modeling, including energy minimization, molecular dynamics, and homology modeling. The course delves into both structure-based and ligand-based drug design methodologies, covering key areas such as protein-ligand docking, virtual screening, SAR, and QSAR. Emphasis is placed on drug-target interaction analysis, lead optimization, and predicting ADMET properties using in silico tools.

Through hands-on tutorials, case studies, and expert lectures, students will develop the skills to utilize state-of-the-art software for the design and optimization of therapeutic compounds, preparing them for advanced roles in pharmaceutical research and development. The course also aims to foster a mindset that is inquisitive and open to exploring new methodologies and technologies in drug design.

Upon successful completion of the course, the student shall be able to;

KNOWLEDGE

K1: Recall the fundamental principles of molecular modeling, including energy minimization and molecular dynamics.

K2: Use computational tools to perform protein-ligand docking and virtual screening.

K3: Explore how ADME prediction influences the advancement of lead molecule development.

K4: Assess the effectiveness of various computational techniques in predicting drug-target interactions.

K5: Evaluate the use of artificial intelligence and machine learning in modern drug design.

K6: Design a workflow by integrating multiple computational tools and techniques.

SKILL

S1: Identify appropriate software tools for different stages of the drug design process.

S2: Integrate data from multiple sources to perform a holistic analysis of drug candidates.

S3: Operate molecular docking software to screen potential drug candidates.

S4: Perform comprehensive molecular dynamics simulations and analyze the results

S5: Develop a new class of molecules by the use of molecular docking and dynamics methods

S6: Prepare computational models for molecular dynamics simulations.

ATTITUDE

A1: Demonstrate a proactive approach to problem-solving and innovation in drug design projects.

A2: Participate in group discussions to plan the development of new class of enzyme inhibitors A3: Show an understanding of the importance of accuracy and attention to detail in computational work.

A4: Engage with peers and instructors to seek feedback and improve computational techniques.

A5: Foster a mindset that is inquisitive and open to exploring new methodologies

A6: Display a commitment to ethical practices in computational drug design, including data integrity and patient safety

REFERENCE BOOKS:

1. Andrew R Leach and Valerie J Gillet, An Introduction to Chemoinformatics, Revised Edition, Springer, 2007
2. Alan Hinchliffe, Molecular Modelling for Beginners, Second Edition, Wiley, 2008
3. Andrew R Leach, Molecular Modelling Principles and Applications, Second Edition, Pearson Education Ltd, 2009
4. Graham Patrick, An Introduction to Medicinal Chemistry, Sixth Edition, Oxford University Press, 2017.
5. Robert M Stroud and F Moore, Computational and structural approaches to drug design, RSC Publishing, 2008

JOURNALS

1. Journal of Chemical Information and Modelling [ACS]
2. Journal of Bimolecular Structure and Dynamics [ Taylor and Francis]
3. Journal of Computer-Aided Molecular Design [Wiley]
4. Journal of Molecular Modelling [Springer]
5. Chemical Physics [Elsevier]