AI in Drug Discovery & Genomics: Accelerating Precision Medicine with Intelligence
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Course Modules:
Module 1: Introduction to Genomics and Drug Discovery
Basics of genomics: DNA, RNA, and sequencing
Overview of the drug discovery pipeline
Where AI fits into modern pharmacogenomics
Module 2: AI Techniques in Bioinformatics
Sequence analysis with ML: classification and clustering
Genomic feature extraction: mutations, SNPs, and expression levels
Deep learning for gene interaction networks and pathway prediction
Module 3: Target Identification and Drug Screening
AI for protein structure prediction and binding site detection
Virtual screening using ML models
QSAR modeling: predicting activity and toxicity of compounds
Module 4: Biomarkers and Personalized Medicine
Using AI to identify genomic biomarkers
Disease subtype classification with gene expression data
Predicting drug response for individualized treatment plans
Module 5: Tools, Datasets, and Platforms
Datasets: TCGA, GTEx, ChEMBL, DrugBank
Tools: DeepChem, BioPython, scikit-bio, AlphaFold
Frameworks: TensorFlow, PyTorch, and MLFlow for healthcare pipelines
Module 6: Capstone Project – AI Pipeline for Drug or Gene Analysis
Choose a case study (e.g., cancer gene classification, COVID-19 drug screening)
Build an AI model to process genomic or molecular data
Submit code, visualizations, and a scientific project report
Tools & Technologies Used:
Python
DeepChem, RDKit, scikit-learn
TensorFlow / PyTorch (for DL models)
BioPython, pandas, NumPy, Matplotlib
Target Audience:
Bioinformatics and data science students
AI developers working in pharmaceutical or genomic research
Biomedical engineers and healthcare innovators
Researchers exploring AI in life sciences
Global Learning Benefits:
Use AI to shorten drug discovery timelines and improve efficacy
Interpret genetic data for diagnosis and treatment prediction
Combine biology and machine learning for impactful innovation
Contribute to breakthroughs in personalized and precision medicine
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