AI-Driven Drug Discovery and Development

• 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

AI-driven drug discovery is transforming the pharmaceutical industry by accelerating the identification and development of new drugs. TensorFlow is a powerful machine learning framework widely used in drug discovery. Python is the programming language of choice for implementing AI models in this field.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to leverage AI techniques to revolutionize drug discovery and development processes.

By the end of this training, participants will be able to:

• Understand the role of AI in drug discovery and development.
• Apply machine learning techniques to predict molecular properties and interactions.
• Use deep learning models for virtual screening and lead optimization.
• Integrate AI-driven approaches into the clinical trial process.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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