Certified AI in Biotech Innovation (CAIBI) Certification Program by Tonex
Artificial intelligence is reshaping biotech R&D—from hypothesis generation to in-silico design and regulatory submission. This program builds practical fluency in AI methods used across protein design, CRISPR workflows, and multi-omics integration. You will learn how to translate business goals into scientifically valid AI pipelines, select models, and evaluate results with rigorous controls.
Emphasis is placed on safety, ethics, and compliance so teams can innovate responsibly. We also address cybersecurity impacts: securing genomic and clinical data, protecting IP in MLOps pipelines, and hardening AI services against model and data attacks. By the end, participants can design end-to-end AI solutions that accelerate discovery while meeting biotech’s quality and governance requirements.
Learning Objectives:
- Map biotech R&D problems to suitable AI techniques.
- Build data pipelines for sequence, structure, and omics data.
- Apply generative and predictive models with proper validation.
- Integrate AI into CRISPR and bioengineering workflows.
- Ensure compliance, ethics, and documentation readiness.
- Protect data, models, and pipelines with cybersecurity controls.
Audience:
- Biotech and pharma researchers
- Computational biologists and bioinformaticians
- Data scientists and ML engineers
- R&D and innovation leaders
- Regulatory and quality professionals
- Cybersecurity Professionals
Program Modules:
Module 1: Foundations of AI in Biotech R&D
- Problem framing and success metrics for R&D
- Data lifecycle, FAIR principles, and GxP context
- Model families: deep learning, graphs, probabilistic methods
- Experimental design, controls, and statistical rigor
- GxP-aware MLOps and documentation practices
- Human-in-the-loop review and decision safeguards
Module 2: Protein Design and Synthetic Biology
- Structure prediction pipelines and confidence measures
- Protein–ligand interaction modeling and docking basics
- De novo protein and peptide design strategies
- Pathway and chassis selection for synthetic biology
- Constraint handling and fitness/objective functions
- Sequence curation, filtering, and safety screens
Module 3: Generative AI for Bioengineering
- Diffusion and transformer models for sequences and structures
- Inverse design and constrained generation workflows
- Active learning and Bayesian optimization loops
- Prompting, adapters, and domain-specific tokenization
- Uncertainty estimation and calibration checks
- IP, data rights, and model governance considerations
Module 4: AI-Enabled Gene Editing
- Guide design scoring and off-target prediction
- On-target efficiency and repair outcome modeling
- Delivery vector and modality selection aids
- Variant calling, QC metrics, and traceability
- Integration with LIMS and audit trails
- Ethical boundaries and safety review processes
Module 5: Bioinformatics and Multi-Omics Integration
- ETL for genomics, transcriptomics, proteomics, metabolomics
- Feature engineering and harmonization across assays
- Graph and knowledge-network integration patterns
- Cohort stratification and biomarker discovery
- Federated and privacy-preserving analytics
- Reproducibility, provenance, and versioning
Module 6: Ethics, Compliance, and Cybersecurity
- Ethical risk assessment and harm mitigation
- Biosecurity threat modeling and safeguards
- Privacy-preserving techniques for sensitive data
- FDA/EMA expectations and documentation readiness
- Model interpretability and change control
- Secure deployment, monitoring, and incident response
Exam Domains:
- AI Systems Architecture for Biotech
- Experimental Design and In-Silico Validation
- Data Governance and Quality in Life Sciences
- Secure MLOps and Regulatory Compliance
- Risk, Safety, and Biosecurity Management
- Strategic Innovation and Tech Transfer
Course Delivery:
The course is delivered through lectures, interactive discussions, expert demonstrations, case studies, and guided projects led by Tonex specialists. Participants receive curated online resources, readings, and templates to support implementation in their organizations.
Assessment and Certification:
Participants are assessed via quizzes, assignments, and a capstone project. Upon successful completion, participants receive the Certified AI in Biotech Innovation (CAIBI) certificate from Tonex.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified AI in Biotech Innovation (CAIBI) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to accelerate biotech innovation with trustworthy AI? Enroll today. Bring your team, align on best practices, and build secure, compliant AI pipelines with Tonex.