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GEN1962 Leading Drug Discovery Informatics in the AI Era

$198.00
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The Executive Diagnostic and Governance Toolkit

Leading Drug Discovery Informatics in the AI Era

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 Drug discovery and research informatics.

$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.
Your informatics function is expected to enable faster discovery, but the work is getting harder.

The situation this is built for

Discovery teams demand real-time data access, reproducible analytics, and seamless integration across wet and dry labs. Legacy architectures buckle under new data volumes. Experimental workflows evolve faster than informatics can adapt. You are held accountable for outcomes, yet lack authority over key decisions. The tools change, but the core work—orchestrating data, people, and decisions—remains yours to own.

Who this is for

Head of Research Informatics in a biotech or pharma organization, responsible for data strategy, infrastructure, and analytics support across discovery programs.

Who this is not for

This is not for IT managers focused on enterprise systems, nor for computational scientists building isolated models. It is for those who own the end-to-end informatics function in drug discovery.

What you walk away with

  • Map where your informatics function creates or loses value in discovery workflows
  • Identify decision rights and accountabilities across data, tools, and teams
  • Assess the resilience of your current data architecture under increasing experimental complexity
  • Define your role in hybrid discovery models blending automation and human insight
  • Build a tailored action plan to evolve your function with confidence

How this maps to your situation

  • Diagnose current state
  • Understand system dynamics
  • Identify leverage points
  • Plan strategic evolution

Before vs. after

Before
Overwhelmed by competing demands, unclear on where to focus improvement efforts, and reactive to discovery team needs.
After
Confident in assessing function maturity, prioritizing high-impact changes, and leading strategic evolution with clarity.

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 8-12 weeks.

If nothing changes
Continued misalignment between informatics capabilities and discovery expectations will erode trust, increase technical debt, and result in loss of influence over critical data decisions.

How this compares to the alternatives

Unlike vendor-specific training or generic data science courses, this program focuses exclusively on the strategic and operational realities of leading drug discovery informatics functions.

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 Evolving Role of Informatics
Examine how the expectations of the informatics function have shifted in modern discovery environments.
12 chapters in this module
  1. How discovery velocity redefines informatics responsibilities
  2. Mapping the changing expectations of discovery scientists
  3. Identifying hidden bottlenecks in data request workflows
  4. Assessing the cost of delayed data access in projects
  5. Recognizing when informatics becomes a gating factor
  6. Evaluating the balance between standardization and flexibility
  7. Understanding how experimental design drives data needs
  8. Tracking the expansion of data types in discovery
  9. Diagnosing miscommunication between wet and dry labs
  10. Clarifying ownership of data quality and curation
  11. Measuring the impact of informatics on project timelines
  12. Defining success from the perspective of discovery teams
Module 2. Mapping the Discovery Informatics Ecosystem
Create a systems view of how people, data, and decisions interact across discovery programs.
12 chapters in this module
  1. Charting data flows from bench to analysis pipeline
  2. Identifying key decision points in hit identification
  3. Tracing ownership of data models across teams
  4. Mapping dependencies between assay development and analytics
  5. Documenting handoffs between experimental and computational teams
  6. Visualizing data lineage in lead optimization workflows
  7. Locating decision logs for compound selection meetings
  8. Tracking metadata requirements across screening campaigns
  9. Identifying integration points for automated platforms
  10. Assessing data format compatibility across instruments
  11. Evaluating version control for analysis scripts
  12. Connecting data provenance to regulatory readiness
Module 3. Diagnosing Data Architecture Limitations
Evaluate whether your current infrastructure supports current and future discovery demands.
12 chapters in this module
  1. Assessing scalability of data storage under high-throughput screening
  2. Evaluating query performance during SAR analysis
  3. Identifying single points of failure in data pipelines
  4. Reviewing data schema adaptability for new modalities
  5. Measuring latency in data availability after experiments
  6. Auditing access controls for sensitive compound data
  7. Testing reproducibility of analysis environments
  8. Evaluating metadata capture completeness in raw data
  9. Diagnosing data silos between therapeutic areas
  10. Assessing compatibility with federated analysis models
  11. Reviewing backup and recovery procedures for critical datasets
  12. Evaluating long-term preservation of discovery data
Module 4. Aligning with Discovery Team Workflows
Understand the actual work of discovery scientists to design responsive informatics support.
12 chapters in this module
  1. Observing compound prioritization meetings for data needs
  2. Mapping data use in structure-activity relationship discussions
  3. Identifying ad hoc data manipulation practices in teams
  4. Documenting workarounds for missing informatics services
  5. Assessing integration of predictive models into decision making
  6. Tracking how scientists validate computational suggestions
  7. Evaluating timing of data delivery relative to project gates
  8. Identifying unmet needs in visualization tools
  9. Understanding preferences for data access interfaces
  10. Measuring time spent on data preparation versus analysis
  11. Documenting feedback loops for model performance
  12. Assessing adoption barriers for new informatics tools
Module 5. Governance in Practice
Examine how decisions about data, standards, and access are actually made and enforced.
12 chapters in this module
  1. Mapping approval workflows for new data sources
  2. Identifying who decides on data classification levels
  3. Documenting change management for data models
  4. Reviewing escalation paths for data quality issues
  5. Assessing participation in cross-functional data councils
  6. Evaluating enforcement of metadata standards
  7. Tracking resolution of data ownership disputes
  8. Measuring compliance with data retention policies
  9. Auditing access revocation after team changes
  10. Reviewing processes for onboarding new collaborators
  11. Assessing documentation practices for data dictionaries
  12. Evaluating consistency in compound naming conventions
Module 6. Evaluating Analytics Enablement
Determine how effectively your function enables data-driven hypothesis generation and testing.
12 chapters in this module
  1. Assessing availability of curated datasets for modeling
  2. Evaluating speed of feature engineering pipelines
  3. Measuring reusability of analysis workflows
  4. Identifying barriers to sharing analytical methods
  5. Tracking usage of self-service analytics platforms
  6. Assessing documentation quality for analytical models
  7. Evaluating reproducibility of published results
  8. Measuring time to deploy new analytical methods
  9. Reviewing validation procedures for predictive models
  10. Assessing integration of external data sources
  11. Evaluating support for iterative model refinement
  12. Documenting version control for analytical outputs
Module 7. Managing Hybrid Discovery Models
Understand how automation and human expertise intersect in modern discovery programs.
12 chapters in this module
  1. Mapping decision authority in automated experimentation
  2. Identifying human oversight points in self-driving labs
  3. Assessing data feedback loops from robotic platforms
  4. Evaluating error handling in autonomous workflows
  5. Tracking intervention frequency by experimental scientists
  6. Documenting edge cases not handled by automation
  7. Reviewing data quality thresholds for automated processing
  8. Assessing calibration requirements for integrated systems
  9. Measuring throughput gains from hybrid workflows
  10. Evaluating training needs for hybrid team members
  11. Identifying communication gaps in mixed teams
  12. Assessing safety protocols for autonomous operations
Module 8. Building Adaptive Data Pipelines
Design data workflows that evolve with changing discovery priorities and technologies.
12 chapters in this module
  1. Assessing modularity of current data processing workflows
  2. Evaluating ease of incorporating new data types
  3. Measuring time to deploy pipeline updates
  4. Reviewing testing procedures for data transformations
  5. Assessing monitoring coverage for pipeline health
  6. Identifying failure recovery mechanisms
  7. Evaluating data validation rules at ingestion
  8. Measuring data drift detection capabilities
  9. Reviewing audit trails for pipeline changes
  10. Assessing scalability of batch processing jobs
  11. Evaluating containerization of analytical steps
  12. Documenting dependencies between pipeline components
Module 9. Leading Cross-Functional Collaboration
Strengthen your ability to coordinate across discovery, computational, and technical teams.
12 chapters in this module
  1. Mapping communication channels between teams
  2. Identifying decision rights in joint initiatives
  3. Assessing meeting structures for cross-team alignment
  4. Evaluating shared documentation practices
  5. Measuring response times to inter-team requests
  6. Reviewing conflict resolution mechanisms
  7. Assessing joint problem-solving effectiveness
  8. Evaluating shared understanding of project goals
  9. Documenting information handoff procedures
  10. Measuring consistency in terminology across teams
  11. Assessing participation in cross-functional retrospectives
  12. Reviewing recognition of collaborative contributions
Module 10. Assessing Talent and Capability Gaps
Evaluate whether your team has the right skills and structure to meet current demands.
12 chapters in this module
  1. Mapping skills against current project requirements
  2. Identifying knowledge silos within the team
  3. Assessing onboarding effectiveness for new members
  4. Evaluating mentorship opportunities
  5. Measuring cross-training completion rates
  6. Reviewing role clarity in team charters
  7. Assessing workload distribution across specialists
  8. Evaluating response capacity during peak demand
  9. Documenting career development conversations
  10. Reviewing performance evaluation criteria
  11. Assessing retention of critical expertise
  12. Measuring engagement in continuous learning
Module 11. Defining Value in Informatics Services
Establish clear metrics and narratives that demonstrate the impact of your function.
12 chapters in this module
  1. Identifying key performance indicators for data teams
  2. Measuring time saved through automated reporting
  3. Assessing reduction in data request turnaround time
  4. Evaluating adoption rates of standardized tools
  5. Measuring reuse of analytical workflows
  6. Tracking reduction in data reconciliation efforts
  7. Assessing improvements in data findability
  8. Evaluating stakeholder satisfaction with services
  9. Measuring impact on compound selection quality
  10. Reviewing cost avoidance from error reduction
  11. Assessing contribution to publication readiness
  12. Documenting informatics contributions to project milestones
Module 12. Planning Strategic Evolution
Synthesize insights into a coherent, actionable plan for evolving your function.
12 chapters in this module
  1. Prioritizing changes based on discovery impact
  2. Identifying quick wins in workflow optimization
  3. Assessing resource requirements for major initiatives
  4. Mapping stakeholder alignment for proposed changes
  5. Evaluating risk tolerance for architectural changes
  6. Defining success metrics for transformation efforts
  7. Sequencing initiatives based on dependencies
  8. Building communication plan for organizational change
  9. Identifying pilot projects for new approaches
  10. Establishing feedback mechanisms for iteration
  11. Documenting assumptions underlying the roadmap
  12. Preparing executive summary for leadership review

Frequently asked

Who is this course designed for?
This course is for Heads of Research Informatics who own the strategy, infrastructure, and delivery of data services across drug discovery programs.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
What kind of tools or software do I need?
No specific software is required. The course uses text-based learning with downloadable templates and examples.
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 8-12 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.
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