BT25403 Artificial Intelligence in Biotechnology – Semester IV – Biotech – R-2025

Subject Code & Name: BT25403 – Artificial Intelligence in Biotechnology

Regulation: R-2025

Semester: IV (Fourth Semester)

Branch: B.Tech. Biotechnology (Biotech)

Credits / L-T-P: 3 Credits | L-T-P: 3-0-0

Course Objectives

• To introduce students to the basics of Artificial Intelligence and its applications in modern biological research and healthcare.

Full Unit-wise Syllabus

Unit I – Basics of Artificial Intelligence

Introduction to Artificial Intelligence (AI), Difference between AI, Machine Learning, and Deep Learning, Importance of AI in biological sciences, Types of biological data, Overview of AI applications in biotechnology

Activities: Collection and presentation of recent AI applications in biotechnology from scientific articles, news, or online sources.

Unit II – Gene and Genome Informatics Using AI

Introduction to AI-assisted bioinformatics tools -comparison and identification of DNA and protein sequences, Identification of genes and their functions in a genome; detection of promoters and important gene control regions using AI, designing primers for PCR and cloning using AI-based software

Activities: Identification of gene regions and protein sequences using online bioinformatics tools.

Unit III – Applications of AI In Drug Discovery And Healthcare

AI in drug discovery and development - Identification of new drug targets using AI; AI in disease diagnosis - Detection of diseases from medical images and patient data; Applications of AI in medical biotechnology - AI-based analysis of biological and clinical data for healthcare applications.

Activities: Group discussion on AI applications in drug discovery and disease diagnosis

Unit IV – AI in Industrial and Agricultural Biotechnology

AI in fermentation and bioprocess optimization - Monitoring and control of fermentation conditions using AI; AI applications in food and agricultural biotechnology - AI-based prediction of crop diseases and yield improvement; Smart biosensors and diagnostics - Use of AI in rapid disease detection through biosensors

Activities: Group-based quiz competition on applications of AI in biotechnology.

Unit V – Future Trends and Ethical Aspects Of AI

Ethical issues in AI and biotechnology- Data privacy and security in handling biological and medical information; Advantages and limitations of AI - Faster data analysis and automation in research, challenges related to data quality and limited human-like reasoning; future scope of AI in biotechnology

Activities: Preparation of a chart/poster showing AI applications in healthcare, or biotechnology research.

Course Outcomes (COs)

• CO1: Understand the fundamental concepts, principles, and applications of Artificial Intelligence in biological sciences and biotechnology.
• CO2: Apply AI-based bioinformatics tools for the interpretation and analysis of gene, genome, and biological data.
• CO3: Analyze the role of AI approaches in identifying drug targets and solving biomedical problems.
• CO4: Evaluate AI-driven solutions for industrial biotechnology, precision agriculture, and healthcare applications.
• CO5: Design AI-based models and innovative solutions to address emerging challenges in biotechnology, while considering ethical and societal implications.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 40% | End Semester Examinations 60%
• Internal methodology: Quiz (20%), Assignments (30%) and Internal Examinations (50%)

Source: Official Anna University – B.Tech. Biotechnology R-2025 Syllabus
Last Updated: October 2026