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AKTU AI in Drug Discovery (HTCB801) Syllabus PDF – B.Tech Honors Degree in Computational Biology and Bioinformatics
AI in Drug Discovery HTCB801
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Official AKTU syllabus of AI in Drug Discovery (HTCB801) for B.Tech Honors Degree in Computational Biology and Bioinformatics – 4 credits, L-T-P 3-1-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — AI in Drug Discovery
Download PDFHTCB801 · 4 credits · L-T-P 3-1-0
Course Outcomes
| Course Outcome | Description At the end of the course, the student will be able to |
| CO1 | Understand AI Fundamentals in Drug Discovery |
| CO2 | Analyze Biological Data for Drug Target Identification |
| CO3 | Implement Predictive Models for Drug Activity |
| CO4 | Integrate Data Sources for Comprehensive Drug Discovery |
| CO5 | Explain Ethical and Regulatory Considerations in AI for Drug Discovery |
| Detailed Syllabus (Theory) | |
| Unit No. | Topics CO No. No. of proposed lectures |
| I | Overview of Drug Discovery: Drug development process, Importance of target identification and validation; Fundamentals of Artificial Intelligence: Introduction to AI and Machine Learning, Supervised and Unsupervised Learning, Deep Learning and Neural Networks; Applications of AI in Drug Discovery: Target identification using AI, Predictive modelling in drug discovery, High-throughput screening and virtual screening. 1 8 |
| II | Basics of Molecular Biology: DNA, RNA, and protein structures, Cellular processes and pathways; Bioinformatics Tools in Drug Discovery: Sequence analysis, Structure prediction, Data mining for drug targets. 2 8 |
| III | Computational Chemistry Techniques: Molecular dynamics simulations, Quantum mechanics in drug design; Chemo-informatics Approaches: Chemical databases and their use, Ligand-based and structure-based drug design. 3 8 |
| IV | Feature Engineering and Data Preprocessing: Handling biological and chemical data, Feature selection and extraction; Predictive Modelling: Regression and classification models, Model evaluation and validation; Deep Learning in Drug Discovery: Convolutional Neural Networks (CNNs) for molecular structures, Recurrent Neural Networks (RNNs) for biological sequences. 4 8 |
| V | Successful Case Studies: Examples of drugs developed using AI, Impact on pharmaceutical industry; Ethical and Regulatory Considerations: Ethical issues in AI-based drug discovery, Regulatory challenges and guidelines; Future Trends and Emerging Technologies: Role of AI in personalized medicine, Integration of multi-omics data in drug discovery. 5 8 |
Text Books
- Artificial Intelligence in Drug Discovery by Martin F. O'Donnell.
- Computational Drug Discovery and Design by Tudor I. Oprea.
- Artificial Intelligence for Drug Discovery by Mark Johnson.
- Machine Learning in Drug Discovery and Development by Richard S. Segall.
Reference Books
1.Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution, Databases and Analytical Tools by SupratimChoudhuri. 2.Artificial Intelligence in Drug Discovery by Patrick Walters. 3.Computational Drug Discovery and Design" by D. C. Young.
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