Course Outline
Intro to AI in Drug Discovery
- Review of conventional drug discovery workflows
- How AI is transforming the drug discovery landscape
- Practical examples: Effective AI-led drug discovery initiatives
Machine Learning in Molecular Modeling
- Fundamentals of molecular modeling and simulation
- Applying ML to forecast molecular features
- Developing predictive models for drug-target binding
Deep Learning for Virtual Screening
- Overview of deep learning methods in drug discovery
- Building deep neural networks for virtual screening purposes
- Examples: AI-based virtual screening within pharmaceutical firms
AI for Lead Optimization and Drug Design
- Methods for refining lead compounds
- Predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles using AI
- Embedding AI into the drug design workflow
AI in Clinical Trials
- The contribution of AI to clinical trial design and oversight
- Forecasting patient outcomes and side effects via AI models
- Real-world applications: AI use cases in clinical research
Ethical Perspectives and Challenges in AI-Driven Drug Discovery
- Moral implications of AI in drug development
- Issues related to data privacy, algorithmic bias, and model transparency
- Approaches to resolving ethical and regulatory hurdles
Recap and Future Directions
Requirements
- Foundational knowledge of drug discovery and development lifecycle
- Proficiency in Python programming
- Working knowledge of machine learning principles
Target Audience
- Scientists in the pharmaceutical industry
- AI specialists and engineers
- Researchers in biotechnology
Testimonials (2)
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
Magdalena - Samsung Electronics Polska Sp. z o.o.
Course - Deep Learning with TensorFlow 2
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
