AI Drug Discovery and Development Training Course

Name: AI Drug Discovery and Development Portfolio Course
Availability: InStock

Assess AI opportunities from targets through trials, govern evidence and human review, prioritize initiatives, and monitor lifecycle performance.

AI Drug Discovery and Development Training Course

At a glance

Duration: 12 days
Format: Classroom
Cities: Vienna, Dubai, Montreux, Porto, Nairobi, Amman and more
Next session: 5 – 16 October 2026, Vienna
Average fee: 9,900 €

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AI Drug Discovery and Development Portfolio Course

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Overview

AI Drug Discovery and Development Portfolio Course is a ten-day course for pharmaceutical research managers, drug development leaders, clinical innovation teams, biomedical research coordinators, R&D portfolio personnel, and life-sciences governance teams, who leave with an AI Drug Development Portfolio Plan. Participants assess targets, evidence, data, candidates, preclinical and clinical uses, safety signals, model credibility, human review, portfolio choices, and lifecycle monitoring. Agile Leaders Training Center provides training in AI drug discovery and development portfolio management.

Who Should Attend

Pharmaceutical research personnel responsible for discovery priorities and evidence
Drug development personnel responsible for candidate progression and decisions
Clinical innovation personnel responsible for trial-support use cases
Biomedical research personnel responsible for data and reproducibility
R&D portfolio personnel responsible for investment and stage-gate choices
Life-sciences governance personnel responsible for credible and monitored AI use

The course assumes participants contribute to pharmaceutical research or development decisions and leaves out computational chemistry coding, bioinformatics specialization, laboratory technique, and clinical diagnosis.

Departments and Industries

The course supports governed AI decisions across pharmaceutical discovery and development environments.

Drug discovery and translational research
Preclinical and clinical development
Clinical operations and trial innovation
Pharmacovigilance and safety functions
Pharmaceutical and biotechnology organizations
Research institutes and contract research organizations

Learning Objectives

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

Analyze AI use cases across the drug lifecycle
Evaluate target, data, candidate, and trial evidence
Apply context-of-use and credibility controls
Build human review and reproducibility safeguards
Prioritize initiatives and monitor lifecycle risks
Build an AI Drug Development Portfolio Plan

Course Agenda

Day 1: Lifecycle Opportunities and Decisions

Medicinal Product AI Lifecycle Map
Drug R&D Value-Pool Assessment
AI Use-Case Definition Canvas
Scientific Decision Impact Matrix
Multidisciplinary Ownership and RACI Chart

Day 2: Target Evidence and Data Readiness

Target Identification Evidence Map
Target Validation Question Set
Biomedical Data Source Register
Data Provenance and Quality Checklist
Population and Bias Review Card

Day 3: Molecules and Candidate Prioritization

Molecular Design Use-Case Screen
Virtual Screening Evidence Table
Candidate Prioritization Scorecard
Prediction Uncertainty Record
Human Scientific Review Gate

Day 4: Preclinical Planning and Reproducibility

Preclinical Evidence Dependency Map
Experimental Design Support Checklist
Reproducibility and Replication Plan
Model Limitation and Applicability Statement
Preclinical Decision Traceability Log

Day 5: Context of Use and Credibility

AI Context-of-Use Statement
Risk-Based Credibility Assessment
Performance Evidence Requirement Matrix
Interpretability and Explainability Review
Model Documentation and Change Register

Day 6: Clinical Development Support

Clinical Development Use-Case Map
Protocol Design Support Boundary
Patient Selection Evidence Checklist
Clinical Endpoint Support Review
Human Clinical Decision Gate

Day 7: Trial Operations and Data Quality

Trial Operations Workflow Map
Site and Recruitment Support Screen
Clinical Data Integrity Control List
Protocol Deviation Signal Review
Trial AI Output Verification Record

Day 8: Safety Signals and Lifecycle Monitoring

Safety Signal Use-Case Assessment
Pharmacovigilance Human Review Flow
Model Performance Monitoring Baseline
Drift Alert and Escalation Rules
Lifecycle Reassessment Schedule

Day 9: Portfolio Governance and Sequencing

AI Initiative Value and Risk Matrix
Drug R&D Portfolio Prioritization Board
Stage-Gate Evidence Decision Template
Capability and Workforce Readiness Map
Implementation Sequence and Dependency Plan

Day 10: Drug Development Portfolio Practice

Suggested Exercise: Map Lifecycle Use Cases and Evidence
Suggested Exercise: Review Targets, Data, and Candidates
Suggested Exercise: Assess Clinical and Safety Controls
Suggested Exercise: Prioritize Initiatives and Monitoring
Capstone Exercise: AI Drug Development Portfolio Plan

Practical Exercises

The course uses suggested activities that turn AI opportunities into evidence-based and governed drug-development decisions.

Suggested activity: map lifecycle opportunities, define contexts of use, and assign decision ownership
Suggested activity: review target evidence, data provenance, candidate uncertainty, and reproducibility
Suggested activity: set clinical boundaries, verify trial outputs, and design safety-signal review
Suggested activity: rank initiatives, set stage gates, sequence capabilities, and plan lifecycle monitoring

FAQs

Who suits AI drug discovery and development training, and what does it assume?

AI drug discovery and development training suits personnel responsible for research, candidate progression, clinical innovation, biomedical evidence, R&D portfolios, or governance. It assumes familiarity with the drug-development lifecycle and requires no coding.

How does AI drug development portfolio training differ from computational drug design training?

Portfolio training focuses on use cases, evidence, credibility, human review, stage gates, governance, and monitoring, while computational drug design training develops technical modeling and coding skills.

What makes an AI model credible for a drug-development decision?

Credibility depends on a clear context of use, risk-proportionate evidence, suitable data, documented performance, uncertainty and limitations, reproducibility, human review, change control, and lifecycle monitoring.

How should teams prioritize AI opportunities in drug development?

Teams should compare scientific value, decision impact, evidence readiness, risk, data access, workflow fit, review capacity, implementation dependencies, monitoring needs, and reversibility.

What belongs in an AI Drug Development Portfolio Plan?

The plan should include lifecycle use cases, contexts of use, evidence requirements, data sources, decision owners, human review, stage gates, initiative priorities, capability gaps, sequencing, performance measures, and reassessment triggers.

Conclusion

Participants take back an AI Drug Development Portfolio Plan connecting discovery, evidence, candidates, preclinical and clinical work, safety, credibility, governance, and monitoring. It changes how teams select and oversee AI-supported R&D decisions. The plan provides a basis for risk-proportionate evidence, reproducible work, accountable review, sequenced investment, and lifecycle control.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Showing 61-76 of 76 events

Location	Dates	Duration	Mode	Price
London	Week 31, 2027 2 – 13 August 2027	12 Days	Onsite	€10,000
Lisbon	Week 31, 2027 2 – 13 August 2027	12 Days	Onsite	€10,000
Amsterdam	Week 32, 2027 9 – 20 August 2027	12 Days	Onsite	€10,000
Milan	Week 32, 2027 9 – 20 August 2027	12 Days	Onsite	€10,000
Dubai	Week 33, 2027 16 – 27 August 2027	12 Days	Onsite	€8,500
Frankfurt	Week 33, 2027 16 – 27 August 2027	12 Days	Onsite	€10,000
Toronto	Week 33, 2027 22 August – 2 September 2027	12 Days	Onsite	€16,000
Madrid	Week 35, 2027 30 August – 10 September 2027	12 Days	Onsite	€10,000
Trabzon	Week 35, 2027 5 – 16 September 2027	12 Days	Onsite	€10,000
Johannesburg	Week 36, 2027 12 – 23 September 2027	12 Days	Onsite	€6,400
Paris	Week 37, 2027 13 – 24 September 2027	12 Days	Onsite	€10,000
Bangkok	Week 37, 2027 19 – 30 September 2027	12 Days	Onsite	€9,000
Tbilisi	Week 38, 2027 20 September – 1 October 2027	12 Days	Onsite	€8,800
Rome	Week 39, 2027 27 September – 8 October 2027	12 Days	Onsite	€10,000
San Diego	Week 39, 2027 27 September – 8 October 2027	12 Days	Onsite	€28,000
Kuwait	Week 39, 2027 3 – 14 October 2027	12 Days	Onsite	€11,000

Frequently asked questions

What does this course cover?

OverviewAI Drug Discovery and Development Portfolio Course is a ten-day course for pharmaceutical research managers, drug development leaders, clinical innovation teams, biomedical research coordinators, R&D portfolio personnel, and life-sciences governance teams, who leave with an AI Drug Development Portfolio Plan. Participants assess targets, evidence,…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

AI Drug Discovery and Development Training Course