B Pharmacy 8th Semester Computer-Aided Drug Design Notes

B.Pharmacy 8th Semester Computer-Aided Drug Design (CADD) notes covering drug discovery process, lead discovery, QSAR, molecular modelling, docking, virtual screening, bioinformatics, ADME databases, and energy minimization. Exam-oriented, clear, and ideal for university exam preparation.

Bachelor of Pharmacy
Semester		8th Semester		Subject		Computer-Aided Drug Design
Syllabus
Unit 1st	Introduction to Drug Discovery and Development Stages of drug discovery and development Lead discovery and analogue-based drug design Rational approaches to lead discovery based on traditional medicine, Random screening, Non-random screening, serendipitous drug discovery, lead discovery based on drug metabolism, lead discovery based on clinical observation. Analog Based Drug Design: Bioisosterism, Classification, Bioisosteric replacement. Any three case studies
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Unit 2nd	Quantitative Structure Activity Relationship (QSAR) SAR versus QSAR, History and development of QSAR, Types of physicochemical parameters, experimental and theoretical approaches for the determination of physicochemical parameters such as Partition coefficient, Hammet’s substituent constant and Tafts steric constant. Hansch analysis, Free Wilson analysis, 3D-QSAR approaches like COMFA and COMSIA
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Unit 3rd	Molecular Modeling and virtual screening techniques Virtual Screening techniques: Drug likeness screening, Concept of pharmacophore mapping and pharmacophore based Screening, Molecular docking: Rigid docking, flexible docking, manual docking, Docking based screening. De novo drug design
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Unit 4th	Informatics & Methods in drug design Introduction to Bioinformatics, chemoinformatics. ADME databases, chemical, biochemical and pharmaceutical databases
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Unit 5th	Molecular Modeling: Introduction to molecular mechanics and quantum mechanics.Energy Minimization methods and Conformational Analysis, global conformational minima determination
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Computer-Aided Drug Design (CADD) is a key subject in B.Pharmacy 8th semester that introduces modern computational approaches used in drug discovery and development. The syllabus covers stages of drug discovery, lead identification, analog-based drug design, and bioisosterism with case studies.

It explains QSAR concepts, physicochemical parameters, Hansch and Free-Wilson analysis, and 3D-QSAR methods like CoMFA and CoMSIA. The subject also includes molecular modeling, virtual screening, pharmacophore mapping, molecular docking, de novo drug design, bioinformatics, chemoinformatics, ADME databases, and energy minimization techniques, making it essential for understanding rational drug design.

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