Skip to main content
Image coming soon

GEN2781 Lead Drug Development in the Age of AI

$199.00
Adding to cart… The item has been added

The Executive Diagnostic and Governance Toolkit

Lead Drug Development in the Age of AI

Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing deciding what to adopt, in what order, and defending that choice when the budget round asks why this and not that.

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

What you walk out with
A scored, ranked picture of your own function, and a defensible answer to what to fix first.
1 You stop guessing where you stand.
You finish with a score, not an opinion: every part of your function rated red, amber or green, with the weakest ranked first. Evidence: a Quick Scan for the shape of it, then seven domain assessments of 30 scored questions each, 210 in all, rolled into one scorecard, plus a maturity radar and a current-versus-target gap analysis.
2 You can defend the decision.
You walk into the budget round with the gap named, the owner named and done defined, instead of a case built on instinct. Evidence: project charter, scope statement, RACI, requirements traceability and work breakdown structure, pre-filled in your domain's language.
3 The work actually moves.
The month after the decision is already built, so nothing stalls waiting for someone to design a form. Evidence: more than 60 project templates across all five PMBOK process groups, plus runbooks, SOPs, a KPI framework, audit checklists and a risk matrix. 55 to 65 files in total.
4 You use it the day it lands.
No blank templates to interpret. Every workbook opens with what it is, who uses it, when, how, a 1 to 5 scoring guide, what good looks like, and a worked example you delete and type over.
The Quick Scan is one sitting. You will know your weakest area before the day is out.
Nothing in it is generic project management: the build rejects any file that could belong to another course. Updated after you enrol, so it reflects where the work stands now. The 144-chapter course is included behind it, for the parts you want to go deeper on.
You're expected to lead AI adoption in drug development—but no one has given you the framework to decide what to do, in what order, or how to defend it.

The situation this is built for

AI is no longer a future possibility in drug discovery. It is actively reshaping target identification, lead optimization, and clinical trial design. As the leader responsible for this function, you face a flood of new capabilities, each promising transformation. Yet your mandate is clear: decide what to adopt, in what sequence, and justify those choices under scrutiny. The risk isn't falling behind—it's adopting the wrong tools at the wrong time and derailing pipeline momentum. You need a way to cut through the noise, assess your team's actual readiness, and build a credible, defensible plan that aligns with your organization's science and strategy.

Who this is for

A senior leader in biopharma responsible for drug discovery or early clinical development, typically at the director level or above, with direct oversight of pipeline strategy, team execution, and cross-functional alignment. They are technically fluent, operationally grounded, and accountable for delivering viable candidates to the clinic.

Who this is not for

This is not for technical contributors without decision authority, AI researchers focused on model development, or executives seeking high-level trend summaries. It is for those who must translate technical potential into operational reality.

What you walk away with

  • Assess your drug discovery function's readiness for AI integration
  • Prioritize adoption areas based on pipeline impact and technical feasibility
  • Build defensible implementation plans for internal and external review
  • Anticipate regulatory and operational constraints in AI-driven workflows
  • Lead cross-functional alignment on AI adoption without relying on vendor narratives

How this maps to your situation

  • Assessment: Where your function stands today
  • Prioritization: What to adopt and why
  • Governance: How decisions are made and reviewed
  • Evolution: How your approach matures over time

Before vs. after

Before
Overwhelmed by competing AI claims, lacking a framework to assess what to adopt, in what order, and how to justify it to leadership or regulators.
After
Confident in your ability to assess readiness, prioritize adoption, and lead defensible integration of AI into your drug discovery function.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 3 hours per module, designed to be completed at your pace over 8 to 12 weeks.

If nothing changes
Continuing without a structured approach risks adopting tools that create technical debt, misalign with pipeline goals, or fail under regulatory scrutiny—delaying candidates and eroding team credibility.

How this compares to the alternatives

Unlike vendor-led training or academic courses, this course focuses exclusively on the operational decisions you must make as a leader. It does not teach AI theory or promote specific tools—it equips you to make sound, defensible choices in your unique context.

Also included: the full course, for when you want the reasoning behind a finding (12 modules, 144 chapters)

Depth reference. The diagnostic and the templates stand on their own; this is what to read when you want the reasoning behind a finding.

Module 1. Understanding the Shift in Discovery Science
Establish the context of AI-driven transformation in drug development and define what has changed for your team.
12 chapters in this module
  1. How AI is redefining target identification today
  2. The shift from high-throughput screening to predictive modeling
  3. New expectations for hit-to-lead timelines
  4. Redefining success in preclinical candidate selection
  5. Assessing the impact on medicinal chemistry workflows
  6. Changes in data requirements for early discovery
  7. The role of biological validation in AI-generated leads
  8. Evaluating reproducibility of algorithmic predictions
  9. New collaboration models between computational and experimental teams
  10. Tracking accuracy of in silico toxicity flags
  11. Understanding limitations of current generative models
  12. Documenting assumptions in AI-driven hypothesis generation
Module 2. Mapping Your Current Discovery Capabilities
Conduct a structured audit of your team's technical, data, and operational maturity.
12 chapters in this module
  1. Inventorying existing computational infrastructure and tools
  2. Assessing data quality across preclinical datasets
  3. Evaluating team proficiency in machine learning concepts
  4. Mapping data flow from lab to analysis environment
  5. Identifying bottlenecks in model validation cycles
  6. Documenting current use of predictive analytics
  7. Benchmarking against internal cycle time metrics
  8. Assessing integration between wet and dry labs
  9. Evaluating data annotation practices for model training
  10. Reviewing version control for computational pipelines
  11. Auditing model interpretability in decision-making
  12. Cataloging regulatory documentation for AI-assisted workflows
Module 3. Defining Readiness for AI Integration
Establish clear criteria for when and how your team can adopt AI tools effectively.
12 chapters in this module
  1. Setting thresholds for data completeness and structure
  2. Defining minimum team fluency in model evaluation
  3. Establishing data governance for AI training sets
  4. Creating standards for model validation experiments
  5. Determining infrastructure readiness for model deployment
  6. Setting expectations for model reproducibility
  7. Aligning AI outputs with regulatory submission needs
  8. Building cross-functional review checkpoints
  9. Defining success criteria for pilot adoption
  10. Assessing compatibility with existing informatics systems
  11. Evaluating model update and maintenance requirements
  12. Documenting risk tolerance for algorithmic recommendations
Module 4. Prioritizing Areas for AI Adoption
Identify where AI can have the highest impact with the lowest operational disruption.
12 chapters in this module
  1. Scoring target identification against data availability
  2. Evaluating lead optimization for model feasibility
  3. Prioritizing ADMET prediction integration points
  4. Assessing synthesis feasibility predictions
  5. Mapping clinical biomarker discovery opportunities
  6. Identifying high-impact data gaps for modeling
  7. Ranking projects by AI readiness potential
  8. Aligning AI use cases with pipeline priorities
  9. Evaluating cost of delay for manual processes
  10. Assessing team capacity to validate AI outputs
  11. Balancing innovation with regulatory pragmatism
  12. Documenting decision rationale for deferral
Module 5. Designing Pilot Integration Pathways
Create structured, low-risk entry points for AI adoption in active projects.
12 chapters in this module
  1. Selecting pilot projects with clear success metrics
  2. Defining scope boundaries for initial implementation
  3. Setting up parallel workflows for comparison
  4. Establishing data handoff protocols for modeling teams
  5. Creating feedback loops between prediction and experiment
  6. Documenting model inputs and assumptions
  7. Scheduling interim review milestones
  8. Planning for model recalibration based on results
  9. Integrating AI outputs into project team meetings
  10. Tracking deviation from predicted outcomes
  11. Evaluating impact on team decision velocity
  12. Preparing lessons learned for broader rollout
Module 6. Evaluating Technical and Operational Fit
Assess how well proposed AI approaches align with your team's science and systems.
12 chapters in this module
  1. Testing compatibility with internal data formats
  2. Evaluating model transparency for scientific scrutiny
  3. Assessing computational resource requirements
  4. Reviewing model update frequency and dependencies
  5. Validating model performance on internal benchmarks
  6. Checking integration points with ELN and LIMS
  7. Assessing need for external data licensing
  8. Evaluating model drift detection mechanisms
  9. Reviewing documentation for audit readiness
  10. Assessing team ability to retrain models
  11. Evaluating dependency on proprietary algorithms
  12. Documenting fallback plans for model failure
Module 7. Building Cross-Functional Alignment
Secure buy-in from medicinal chemistry, pharmacology, and regulatory teams.
12 chapters in this module
  1. Communicating AI capabilities without overstatement
  2. Aligning on interpretation of model outputs
  3. Establishing joint review processes for AI-generated leads
  4. Defining roles in model validation cycles
  5. Creating shared vocabulary between disciplines
  6. Integrating AI timelines into project plans
  7. Addressing concerns about reduced experimental iteration
  8. Building trust through transparent failure analysis
  9. Documenting disagreements on model recommendations
  10. Aligning on criteria for overriding AI suggestions
  11. Planning for regulatory Q&A on AI use
  12. Creating escalation paths for model disputes
Module 8. Developing Implementation Governance
Establish oversight structures to guide responsible AI adoption.
12 chapters in this module
  1. Defining review board composition and mandate
  2. Setting thresholds for model validation rigor
  3. Establishing approval pathways for AI-assisted decisions
  4. Creating audit trails for model-driven choices
  5. Defining data retention policies for AI workflows
  6. Setting standards for model version documentation
  7. Establishing revalidation requirements
  8. Creating escalation paths for unexpected outcomes
  9. Documenting model limitations in team communications
  10. Aligning on liability for AI-influenced decisions
  11. Reviewing insurance coverage for AI-driven workflows
  12. Planning for regulatory inspections of AI use
Module 9. Anticipating Regulatory and Compliance Needs
Prepare for scrutiny from regulatory bodies on AI use in drug development.
12 chapters in this module
  1. Mapping AI use to regulatory submission categories
  2. Documenting model development and training processes
  3. Creating validation packages for regulatory review
  4. Anticipating questions on algorithmic decision-making
  5. Aligning model transparency with regulatory expectations
  6. Tracking model performance over time
  7. Establishing change control for model updates
  8. Preparing for inspection of training data provenance
  9. Documenting human oversight in AI workflows
  10. Aligning with evolving regulatory guidance on AI
  11. Planning for audit of model decision logs
  12. Building regulatory engagement strategy for AI use
Module 10. Scaling Adoption Across the Pipeline
Expand AI integration beyond pilots to broader pipeline impact.
12 chapters in this module
  1. Assessing scalability of computational infrastructure
  2. Planning for increased data storage demands
  3. Evaluating team bandwidth for model oversight
  4. Standardizing AI integration playbooks
  5. Creating training materials for new users
  6. Establishing model monitoring dashboards
  7. Developing model retraining schedules
  8. Integrating AI milestones into project timelines
  9. Scaling validation protocols across teams
  10. Optimizing model inference costs
  11. Planning for technical debt in AI systems
  12. Documenting institutional knowledge from pilots
Module 11. Measuring Impact and Refining Strategy
Track outcomes and adapt your AI adoption approach over time.
12 chapters in this module
  1. Defining KPIs for AI-assisted discovery
  2. Tracking reduction in experimental cycles
  3. Measuring time to candidate selection
  4. Evaluating quality of AI-generated leads
  5. Assessing team confidence in model outputs
  6. Monitoring false positive and false negative rates
  7. Tracking cost savings from reduced wet lab work
  8. Evaluating impact on project team velocity
  9. Comparing AI-driven results to historical benchmarks
  10. Assessing regulatory acceptance of AI-supported data
  11. Updating risk profiles based on performance
  12. Refining adoption criteria based on results
Module 12. Sustaining Leadership in AI-Driven Discovery
Institutionalize AI adoption and maintain strategic advantage.
12 chapters in this module
  1. Embedding AI evaluation into project intake
  2. Establishing ongoing training for discovery teams
  3. Creating feedback loops with computational scientists
  4. Updating governance as models evolve
  5. Planning for next-generation AI capabilities
  6. Maintaining regulatory compliance over time
  7. Sharing best practices across therapeutic areas
  8. Evaluating external collaboration opportunities
  9. Assessing competitive positioning through AI use
  10. Documenting leadership decisions for succession
  11. Building resilience to model performance shifts
  12. Sustaining culture of evidence-based AI adoption

Frequently asked

Who is this course for?
It is for senior leaders in drug discovery and early clinical development who are accountable for pipeline decisions and team execution in the context of AI-driven change.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover specific AI tools or platforms?
No. The course focuses on your decision-making framework, not vendor technologies or implementation details.
Will I receive support during the course?
Yes. You will have access to updated templates and a hand-built implementation playbook tailored to your function's maturity.
Can I share this course with my team?
Each enrollment is for individual use. Team licensing is available upon request.
What formats do the templates come in?
The implementation playbook downloads as PDF and editable XLSX. The course reads in your learning environment and exports to PDF for offline use. The files are yours to keep.
Can I share this with my team?
The licence is per person. Team pricing opens from three seats: reply to the order confirmation with TEAM and we will set it up.
How quickly can I start?
The diagnostic is one sitting and the templates work straight out of the kit. Account access takes up to 24 hours rather than being instant, because every order is checked and updated against the latest sources before it is delivered.
$199 one-time. Approximately 3 hours per module, designed to be completed at your pace over 8 to 12 weeks..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee·Know your weakest area today·210 scored questions·Course included· Account access within 24 hours
30-day money-back guarantee, no questions asked.
Thousands of organisations have bought from The Art of Service since 2000.
!