Course Outline
Introduction to AI in Drug Discovery
- Overview of traditional drug discovery processes
- The role of AI in revolutionizing drug discovery
- Case studies: Successful AI-driven drug discovery projects
Machine Learning in Molecular Modeling
- Basics of molecular modeling and simulations
- Applying machine learning to predict molecular properties
- Building predictive models for drug-target interactions
Deep Learning for Virtual Screening
- Introduction to deep learning techniques in drug discovery
- Implementing deep neural networks for virtual screening
- Case studies: AI-driven virtual screening in pharmaceutical companies
AI for Lead Optimization and Drug Design
- Techniques for optimizing lead compounds
- Using AI to predict ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties
- Integrating AI into the drug design pipeline
AI in Clinical Trials
- The role of AI in clinical trial design and management
- Predicting patient responses and adverse effects using AI models
- Case studies: AI applications in clinical trials
Ethical Considerations and Challenges in AI-Driven Drug Discovery
- Ethical issues in AI applications for drug discovery
- Challenges in data privacy, bias, and model interpretability
- Strategies for addressing ethical and regulatory concerns
Summary and Next Steps
Requirements
- An understanding of drug discovery and development processes
- Experience with programming in Python
- Familiarity with machine learning concepts
Audience
- Pharmaceutical scientists
- AI specialists
- Biotech researchers
Testimonials (3)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
The adaptation of exos to our context and the consideration of our request
Amel Guetat - EURO-INFORMATION DEVELOPPEMENTS
Course - Fraud Detection with Python and TensorFlow
Machine Translated
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
