Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Data Analytics and AI in Drug Development shows how machine learning is compressing one of science’s longest and costliest pipelines. You follow the discovery workflow and see where AI adds leverage: mining biological data for drug targets, virtual screening of compound libraries, predicting ADMET properties and toxicity, and analysing clinical and omics data. The course balances the computational methods with the realities of pharmaceutical R&D — data quality, validation and the gap between a promising prediction and a viable drug. You finish able to reason about where and how AI genuinely helps drug discovery. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies data analytics and AI to drug development — target identification, virtual screening, ADMET prediction and analysis across the discovery pipeline.
📋 Course Objectives
1. Mine biological data to identify drug targets.
2. Apply virtual screening to compound libraries.
3. Predict ADMET properties and toxicity.
4. Analyse clinical and omics data in discovery.
5. Judge where AI adds real value in the pipeline.
👥 Who Should Enroll?
• Pharma and biotech R&D scientists
• Cheminformatics and bioinformatics professionals
• Data scientists entering drug discovery
• Students specialising in computational pharma
🚀 Key Learning Outcomes
• An understanding of AI across drug discovery.
• The ability to reason about pharma data workflows.
• A foundation for computational drug development.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Data Analytics Foundations
Apply linear algebra and calculus concepts to solve complex data analytics problems • Develop probabilistic models to analyze and interpret large datasets • Design and implement algorithms for data preprocessing and feature engineering
Data Engineering, Preprocessing, and Feature Pipelines
Configure data pipelines using Apache Beam and Apache Spark for efficient data processing • Analyze and preprocess large datasets using techniques such as data normalization and feature scaling • Implement data quality control measures to ensure data integrity and accuracy
Model Architecture, Algorithm Design, and Data Analytics Methods
Design and implement deep learning models using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) • Evaluate the performance of machine learning models using metrics such as accuracy, precision, and recall • Develop and apply transfer learning techniques to adapt pre-trained models to new datasets
Training, Hyperparameter Optimization, and Evaluation
Train and optimize machine learning models using techniques such as grid search and random search • Analyze and interpret the results of hyperparameter tuning experiments • Implement early stopping and learning rate scheduling to prevent overfitting
Deployment, MLOps, and Production Workflows
Deploy machine learning models using containerization techniques such as Docker • Configure and manage model serving pipelines using TensorFlow Serving and AWS SageMaker • Develop and implement monitoring and logging systems to track model performance
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and identify potential biases in machine learning models and datasets • Develop and implement strategies for mitigating bias and ensuring fairness in AI systems • Evaluate the ethical implications of AI systems and develop guidelines for responsible AI development
Industry Integration, Business Applications, and Case Studies
Apply data analytics and AI techniques to real-world business problems and case studies • Develop and present business cases for AI adoption and implementation • Evaluate the return on investment (ROI) and potential benefits of AI solutions
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | scikit-learn |
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