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Course Outline
Introduction to AI in Drug Discovery
- Overview of traditional drug discovery workflows.
- The transformative role of AI in drug discovery.
- Case studies: Successful AI-driven drug discovery projects.
Machine Learning in Molecular Modeling
- Fundamentals of molecular modeling and simulations.
- Applying machine learning to predict molecular properties.
- Developing 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
- A solid understanding of drug discovery and development workflows.
- Practical experience in Python programming.
- Familiarity with core machine learning concepts.
Audience
- Pharmaceutical scientists.
- AI specialists.
- Biotech researchers.
21 Hours
