AI for Drug Discovery and Development
( 25 Modules )

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

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AI for Drug Discovery and Development ( 25 Modules )

AI for Drug Discovery and Development
( 25 Modules )