Module #1 Introduction to Machine Learning in Drug Discovery Overview of machine learning, its applications, and importance in drug discovery
Module #2 Basics of Drug Discovery Introduction to drug discovery process, types of drugs, and challenges faced by the industry
Module #3 Machine Learning Terminology and Notations Fundamentals of machine learning, including supervised and unsupervised learning, types of data, and performance metrics
Module #4 Drug Discovery Data Types and Sources Overview of different data types used in drug discovery, including molecular structures, bioactivity data, and clinical trial data
Module #5 Overview of Machine Learning Models for Drug Discovery Introduction to various machine learning models used in drug discovery, including linear regression, decision trees, random forests, and neural networks
Module #6 Molecular Representation and Descriptor Calculation Introduction to molecular representation methods, including fingerprints, descriptors, and graph neural networks
Module #7 Machine Learning Models for Chemical Property Prediction Application of machine learning models to predict chemical properties, such as solubility, logP, and toxicity
Module #8 QSAR Modeling for Bioactivity Prediction Introduction to QSAR modeling, including linear and non-linear regression methods, and their applications in bioactivity prediction
Module #9 Machine Learning for Chemical Reaction Prediction Application of machine learning models to predict chemical reactions, including reaction outcomes and mechanisms
Module #10 Virtual Screening for Hit Identification Introduction to virtual screening, including ligand-based and structure-based approaches, and their applications in hit identification
Module #11 Introduction to Bioinformatics and Genomics Overview of bioinformatics, genomics, and transcriptomics, including high-throughput sequencing technologies
Module #12 Machine Learning for Gene Expression Analysis Application of machine learning models to analyze gene expression data, including clustering, classification, and regression methods
Module #13 Machine Learning for Protein Structure Prediction Introduction to protein structure prediction, including sequence-based and structure-based methods
Module #14 Machine Learning for Biological Network Analysis Application of machine learning models to analyze biological networks, including protein-protein interaction networks and gene regulatory networks
Module #15 Machine Learning for Disease Biomarker Identification Introduction to disease biomarker identification, including machine learning approaches to identify biomarkers from genomic and transcriptomic data
Module #16 Deep Learning for Drug Discovery Introduction to deep learning models, including convolutional neural networks and recurrent neural networks, and their applications in drug discovery
Module #17 Transfer Learning and Domain Adaptation Introduction to transfer learning and domain adaptation, including their applications in drug discovery
Module #18 Explainable AI for Drug Discovery Introduction to explainable AI, including model interpretability and feature importance methods
Module #19 Multi-Task Learning and Multi-Modal Learning Introduction to multi-task learning and multi-modal learning, including their applications in drug discovery
Module #20 Adversarial Attacks and Defense in Drug Discovery Introduction to adversarial attacks and defense, including their applications in drug discovery
Module #21 Machine Learning for Lead Optimization Case study on using machine learning for lead optimization, including examples of successful applications
Module #22 Machine Learning for Target Identification Case study on using machine learning for target identification, including examples of successful applications
Module #23 Machine Learning for Clinical Trial Outcome Prediction Case study on using machine learning to predict clinical trial outcomes, including examples of successful applications
Module #24 Machine Learning for Adverse Event Prediction Case study on using machine learning to predict adverse events, including examples of successful applications
Module #25 Real-World Applications of Machine Learning in Drug Discovery Industry case studies and success stories on using machine learning in drug discovery
Module #26 Future Directions in Machine Learning for Drug Discovery Overview of emerging trends and future directions in machine learning for drug discovery
Module #27 Challenges and Limitations of Machine Learning in Drug Discovery Discussion of challenges and limitations of using machine learning in drug discovery
Module #28 Ethical Considerations in Machine Learning for Drug Discovery Discussion of ethical considerations in using machine learning for drug discovery
Module #29 Regulatory and IP Considerations in Machine Learning for Drug Discovery Discussion of regulatory and intellectual property considerations in using machine learning for drug discovery
Module #30 Course Wrap-Up & Conclusion Planning next steps in Machine Learning Models for Drug Discovery career
