Certified AI in Drug Discovery & Development (CAID3) Certification Program by Tonex
AI is transforming the pharma value chain—from target identification to market access. This program gives you an end-to-end, practical blueprint for using AI securely and compliantly across discovery, development, manufacturing, and supply. Learn how to design molecular pipelines, build robust ADMET predictors, and accelerate clinical decisions with trustworthy evidence. See how AI streamlines CMC, forecasting, and release processes while meeting GxP expectations.
We emphasize governance, risk, and assurance so teams can deploy AI that is auditable and reproducible. Cybersecurity is integral: protect IP, patient and omics data, and safeguard models against drift and adversarial threats. Map controls to 21 CFR Part 11, Annex 11, and emerging FDA/EMA guidance. By the end, you will know how to align science, data, and regulation to shorten timelines, reduce cost, and scale AI safely—without compromising quality or ethics.
Learning Objectives:
- Translate business goals into AI use cases across the pharma lifecycle.
- Build data pipelines and features fit for regulated environments.
- Develop and validate ADMET and efficacy models.
- Apply AI in preclinical and clinical decision-making.
- Operationalize MLOps with quality, security, and traceability.
- Navigate FDA/EMA expectations for AI and software assurance.
- Design controls for data privacy, IP protection, and model security.
- Measure impact with reliable KPIs and risk metrics.
Audience:
- Drug discovery and development professionals
- Data scientists and AI engineers
- Clinical operations and biostatistics teams
- Regulatory affairs and quality leaders
- Manufacturing and supply chain professionals
- Cybersecurity Professionals
- IT/enterprise architects and product managers
- Pharmacovigilance and medical affairs teams
Program Modules:
Module 1 — AI Foundations for Pharma
- Value mapping across discovery to market
- Regulated data lifecycles and metadata
- Problem framing and model selection
- Evidence standards and decision thresholds
- Risk taxonomy for AI in GxP contexts
- KPIs, ROI, and change management
Module 2 — Molecule Design, Screening, and Lead Optimization
- Generative design and docking workflows
- Virtual screening at scale
- Multi-objective lead optimization
- QSAR/QSPR feature engineering
- Benchmarking and hit triage
- IP and data security controls
Module 3 — ADMET Prediction and Safety-by-Design
- ADME/Tox endpoints and datasets
- Interpretable models and uncertainty
- Translational relevance and external validation
- Human-in-the-loop review practices
- Safety margins and exposure modeling
- Documentation and audit trails
Module 4 — AI in Preclinical and Clinical Development
- Biomarker discovery and enrichment
- Trial design, simulation, and forecasting
- Adaptive and Bayesian methods
- Digital endpoints and RWE integration
- Bias, fairness, and subgroup analysis
- Data privacy and patient safety
Module 5 — Manufacturing, Quality, and Supply Chain Analytics
- Process modeling and CMC analytics
- Predictive quality and release readiness
- Yield, throughput, and deviation insights
- Demand forecasting and inventory health
- Cold-chain and serialization analytics
- Supplier risk and cyber resilience
Module 6 — Compliance, Ethics, and Trustworthy MLOps
- 21 CFR Part 11/Annex 11 alignment
- Validation, change control, and SOPs
- Model cards, lineage, and traceability
- Monitoring drift and performance
- Threats: data poisoning and model theft
- Ethical use, transparency, and consent
Exam Domains:
- AI Strategy and Value Realization in Pharma
- Regulated Data Engineering and Governance (GxP)
- Model Risk Management and Assurance
- Clinical Evidence Generation and Decision Support
- Manufacturing, Quality, and Supply Analytics
- Regulatory Affairs, Ethics, and Trust
Course Delivery:
The course is delivered through expert-led lectures, interactive discussions, guided walk-throughs, and case-based learning. Participants access curated readings, templates, and checklists, plus short knowledge checks and instructor feedback.
Assessment and Certification:
Participants complete quizzes, practical assignments, and a concise capstone brief. Upon successful completion, participants receive the Certified AI in Drug Discovery & Development (CAID3) certificate from Tonex.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified AI in Drug Discovery & Development (CAID3) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to accelerate safe, compliant AI in pharma? Enroll now to turn promising models into regulated, real-world impact—with security and quality by design.