AI for Drug Discovery and Design Fundamentals Training by Tonex
This two-day intensive training offers a deep dive into the transformative applications of Artificial Intelligence (AI) and Machine Learning (ML) in modern drug discovery and design. Participants will explore state-of-the-art techniques for molecular property prediction, lead identification, and de novo drug development using AI-powered tools. The course bridges data science and pharmaceutical R&D, empowering professionals to enhance drug design pipelines with predictive analytics and algorithmic innovations. Importantly, the intersection of AI and cybersecurity is highlighted, focusing on the protection of sensitive biomedical data and intellectual property. As cyber threats evolve, integrating secure AI workflows is crucial to safeguard proprietary drug discovery models and datasets.
Audience:
- Drug discovery scientists
- Bioinformaticians
- Cheminformaticians
- AI and ML engineers
- Data scientists in life sciences
- Cybersecurity professionals
Learning Objectives:
- Understand AI/ML applications in the drug discovery pipeline
- Distinguish between ligand-based and structure-based design using AI
- Apply predictive modeling techniques such as QSAR
- Utilize AI tools for molecular generation and screening
- Integrate AI with traditional docking and molecular dynamics
- Address cybersecurity implications in AI-driven drug research
Course Modules:
Module 1: Introduction to AI in Drug Design
- Overview of AI in pharmaceutical R&D
- AI’s role in accelerating discovery timelines
- Historical and emerging AI applications
- Challenges in data availability and quality
- Regulatory and ethical considerations
- Cybersecurity threats in AI pipelines
Module 2: Ligand vs. Structure-Based Approaches
- Fundamentals of ligand-based methods
- Structure-based design workflows
- Comparative strengths and limitations
- AI-enhanced pharmacophore modeling
- Deep learning in structure prediction
- Protecting model architectures from intrusion
Module 3: Predictive Modeling and QSAR
- Basics of QSAR (Quantitative Structure-Activity Relationship)
- Feature extraction for chemical descriptors
- Regression and classification models
- Model validation and performance metrics
- Best practices for QSAR deployment
- Data integrity and adversarial input risks
Module 4: Deep Learning for Molecular Generation
- SMILES-based generative models
- Graph neural networks for molecules
- Reinforcement learning in drug design
- De novo molecular design algorithms
- Evaluation of generative outputs
- Cybersecurity in model training environments
Module 5: Machine Learning in Virtual Screening
- ML for hit identification and prioritization
- Ensemble learning and screening libraries
- Feature-based screening versus end-to-end
- Integration with docking pipelines
- Avoiding false positives with ML tuning
- Access control and secure screening workflows
Module 6: Case Studies and Best Practices
- Insilico Medicine’s AI platform overview
- Atomwise’s structure-based AI strategy
- BenevolentAI’s biomedical knowledge graphs
- Lessons learned from AI implementation
- Building secure collaborative research systems
- Strategic planning for scalable AI adoption
Join Tonex’s AI for Drug Discovery and Design Fundamentals Training to enhance your expertise in AI applications across the drug development lifecycle while mastering the cybersecurity challenges inherent in handling high-value biomedical data. Secure your seat now to stay ahead in this fast-evolving domain!