The Design-Make-Test-Analyze cycle has evolved from an aspirational framework to an operational imperative in modern drug discovery. Yet many organizations struggle to translate DMTA principles into functioning systems that deliver faster cycle times, higher-quality decisions, and sustained competitive advantage. This course addresses the implementation gap, the space between knowing what DMTA is and building systems that work. We assume participants already understand DMTA fundamentals; this is not an introduction. Instead, the focus is on the operational questions practitioners face when building and scaling DMTA capabilities: how to launch a pilot project that demonstrates value, when to build custom tools versus adapt commercial platforms, how to measure cycle performance and diagnose bottlenecks, and what 'good enough' looks like at each stage.
Structured around the full DMTA cycle, the course offers deep dives into best practices for each stage: Design, Make, Test, and Analyze, while also addressing the human and organizational factors that determine program success. The course concludes with a forward-looking discussion on emerging technologies, including generative AI for molecular design, cloud laboratories, advanced robotics, and real-time analytics, and how practitioners can evaluate and time adoption decisions to maximize ROI. Participants leave with actionable frameworks, decision trees, and practical checklists they can apply immediately in their own organizations.
Who Should Attend?
- Automation scientists and research scientists actively building or running DMTA workflows in drug discovery environments
- Laboratory managers and team leaders responsible for cross-functional DMTA program execution
- Computational chemists, medicinal chemists, and biologists seeking to better integrate their work into closed-loop discovery cycles
- Data scientists and informatics professionals developing analytical pipelines that feed into DMTA decision-making
- Senior leaders and directors evaluating technology investments and organizational readiness for DMTA implementation
Course Benefits
- Apply best practices across all four DMTA stages from hypothesis generation and compound prioritization through assay design, data quality, and SAR-driven decision-making
- Navigate the practical realities of launching DMTA programs: securing stakeholder buy-in, selecting high-value pilot projects, building cross-functional teams, and defining success metrics
- Evaluate tradeoffs in synthesis and assay automation knowing when to build custom solutions, when to adopt commercial platforms, and how to avoid common deployment pitfalls
- Build analytical infrastructure that closes the loop: turning raw assay data into predictive models and prioritized compound lists ready for the next design cycle
- Establish continuous optimization practices that reduce cycle time, improve prediction accuracy, and surface systemic bottlenecks before they stall programs
- Develop a strategic framework for evaluating emerging technologies AI-driven design, cloud-based experimentation, and advanced robotics and making adoption decisions with confidence
Course Topics
Design Stage Best Practices
- Hypothesis generation, compound selection strategies, and experimental design principles that maximize learning per cycle
- Integrating machine learning, active learning, and medicinal chemistry expertise for smarter compound prioritization
- Structuring design decisions to accelerate iteration velocity without sacrificing scientific rigor
Make Stage Implementation
- Synthesis automation tradeoffs: throughput vs. flexibility vs. reliability in library preparation and compound management
- When parallel synthesis automation adds value versus when it introduces new bottlenecks
- Practical lessons from deploying automated synthesis workflows at scale, including compound logistics and downstream handoff
Test Stage Excellence
- Designing robust, reproducible assay workflows under real-world constraints: reagent stability, plate effects, and instrument variability
- Assay automation strategies that balance throughput with data quality and interpretability
- Common failure modes in test workflows and how to build resilience into assay design from the start
Analyze Stage: Closing the Loop
- Building analytical pipelines that convert raw data into structure-activity relationships, predictive models, and prioritized compound lists
- Data infrastructure requirements for effective DMTA iteration: what you need before you scale
- Diagnostic approaches for identifying where analytical bottlenecks are constraining cycle performance
Getting DMTA Off the Ground
- Securing organizational buy-in; how to frame DMTA value for executive stakeholders and cross-functional partners
- Selecting pilot projects with high probability of early wins and clear success criteria
- Building cross-functional teams with clear ownership and communication structures that prevent information silos
Continuous Optimization
- Establishing feedback loops that systematically reduce cycle time and improve decision quality across successive iterations
- Identifying and resolving technical, organizational, and cultural bottlenecks that constrain DMTA performance
- Instrumenting workflows for measurement: what to track, how to diagnose failure modes, and how to implement targeted improvements
Future Directions
- How generative AI for molecular design, cloud laboratories, and advanced robotics are reshaping DMTA workflows
- Frameworks for evaluating new tools: distinguishing genuine capability improvements from vendor hype
- Timing adoption decisions to maximize ROI while minimizing implementation risk over a 3–5-year horizon
Instructors
Ruth Petersen
Strategic Advisor, Ruloras Strategies
Ruth Petersen brings the commercial and strategic connective tissue that holds programs together. As the founder of Ruloras Strategies, a boutique firm specializing in life sciences and lab automation marketing, she has spent over two decades bridging the gap between technical capability and organizational execution, working alongside automation scientists, research leaders, and executive stakeholders to translate complex workflows into scalable programs. She chairs the SLAS Automated Chemistry Topical Interest Group and has taught at the SLAS annual conference, including co-instructing both a DMTA introductory short course and an Advanced DMTA Strategies course at SLAS 2026.
Philip Hopcroft
Associate Principal Scientist, AstraZeneca
Philip Hopcroft is an Associate Principal Scientist at AstraZeneca, where he has been integral to implementing and driving the adoption of automated workflows within one of the world's leading pharmaceutical R&D organizations. He brings Test and Analyze expertise grounded in the realities of large-scale, high-throughput drug discovery, including assay design challenges, data quality considerations, and analytical pipeline decisions that determine whether DMTA cycles yield actionable insights or merely data volume. Philip's perspective reflects what it takes to make automation not just function, but produce reproducible, interpretable results in a demanding industrial environment.
