Module #1 Introduction to AI in Drug Discovery and Development Overview of the role of AI in the pharmaceutical industry and its applications in drug discovery and development
Module #2 Basics of Machine Learning Fundamentals of machine learning, including supervised and unsupervised learning, regression, and classification
Module #3 Deep Learning for Drug Discovery Introduction to deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and their applications in drug discovery
Module #4 Data Sources for AI in Drug Discovery Overview of available data sources for AI applications in drug discovery, including public databases, high-throughput screening data, and electronic health records (EHRs)
Module #5 Data Preprocessing and Feature Engineering Techniques for preprocessing and feature engineering in AI applications for drug discovery, including data cleaning, normalization, and feature selection
Module #6 Cheminformatics and Molecular Representation Introduction to cheminformatics and molecular representation, including molecular descriptors and fingerprints
Module #7 Predicting Drug-Likeness and ADMET Properties Using AI to predict drug-likeness and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of small molecules
Module #8 Target Identification and Validation AI applications for identifying and validating potential drug targets, including protein-protein interaction networks and gene expression analysis
Module #9 Virtual Screening and Hit Identification Using AI for virtual screening and hit identification, including docking and pharmacophore-based approaches
Module #10 Lead Optimization and SAR Analysis AI applications for lead optimization and structure-activity relationship (SAR) analysis, including QSAR modeling and machine learning-based approaches
Module #11 AI for Pharmacokinetics and Pharmacodynamics Modeling Using AI for pharmacokinetics and pharmacodynamics modeling, including physiologically-based pharmacokinetic (PBPK) modeling and systems pharmacology
Module #12 Image Analysis for Drug Discovery AI applications for image analysis in drug discovery, including high-content screening and image-based phenotypic profiling
Module #13 Natural Language Processing for Drug Discovery Using AI for natural language processing in drug discovery, including text mining and information extraction from scientific literature
Module #14 Collaborative Filtering for Drug-Target Interaction Prediction AI applications for collaborative filtering in drug-target interaction prediction, including matrix factorization and neighborhood-based models
Module #15 Graph Neural Networks for Molecular Design Using AI for graph neural networks in molecular design, including molecular graph generation and optimization
Module #16 AI for Clinical Trial Design and Optimization AI applications for clinical trial design and optimization, including predictive modeling and simulation-based approaches
Module #17 Personalized Medicine and Precision Therapeutics Using AI for personalized medicine and precision therapeutics, including genomics-based approaches and patient stratification
Module #18 Regulatory Considerations and Ethics in AI for Drug Discovery Regulatory considerations and ethical implications of using AI in drug discovery, including data privacy and bias in AI models
Module #19 Real-World Applications and Case Studies Real-world applications and case studies of AI in drug discovery, including success stories and challenges encountered
Module #20 Future Directions and Emerging Trends Emerging trends and future directions in AI for drug discovery, including explainability, transparency, and accountability of AI models
Module #21 Practical Exercises and Project Development Hands-on practical exercises and project development in AI for drug discovery, including a final project presentation
Module #22 Python Programming for AI in Drug Discovery Introduction to Python programming for AI applications in drug discovery, including popular libraries and frameworks
Module #23 R Programming for AI in Drug Discovery Introduction to R programming for AI applications in drug discovery, including popular libraries and frameworks
Module #24 Cloud Computing and High-Performance Computing for AI Overview of cloud computing and high-performance computing for AI applications in drug discovery, including Amazon Web Services (AWS) and Google Cloud Platform (GCP)
Module #25 Course Wrap-Up & Conclusion Planning next steps in AI for Drug Discovery and Development career
