Rethinking the DMTA Cycle: How AI, Automation, and Integration Are Reshaping Drug Discovery

The design-make-test-analyze (DMTA) cycle has long served as the backbone of preclinical drug discovery. But as artificial intelligence dramatically accelerates molecular design, the rest of the workflow is struggling to keep pace—and that imbalance is forcing the pharmaceutical industry to confront a fundamental question: can the DMTA cycle itself be reimagined for an AI-enabled era?

According to Jon Wingfield (search)Search term, a senior business development leader at TTP with nearly 30 years of pharmaceutical R&D experience, the answer will determine which organizations thrive in the next generation of drug discovery. "The primary limitation in drug discovery is shifting away from idea generation and toward the productivity of the DMTA cycle itself," Wingfield explains. "Increasing the speed of molecular design only creates value if the remainder of the discovery workflow can progress at a similar pace."

The Shifting Bottleneck

For decades, improvements in drug discovery productivity came largely from advances within individual functions—high-throughput screening, laboratory automation, and computational modeling. Yet meaningful gains in overall productivity have remained elusive. Many of the most straightforward opportunities have already been captured, while the growth of personalized medicine is simultaneously increasing demand for evaluating a greater number of compounds.

Now, AI is reshaping the landscape across multiple disciplines, from molecular design and synthesis planning to data analysis and decision-making. But this acceleration creates a new problem: the volume of compounds entering early-stage discovery workflows is expected to grow substantially. "For many organizations, generating candidate molecules may soon become less challenging than making, testing, and learning from them," Wingfield notes.

Wenshu Xu (search)Search term, Head of Drug Discovery Tools at TTP, reinforces this point from the experimental side. "AI has become a major driver of change, but its success depends on access to high-quality experimental data," she says. "Building robust foundation models requires large, reliable datasets that include not only experimental results but also metadata about the experimental environment. Traditional manual workflows are unlikely to provide the volume and quality of information required."

Three Critical Opportunities

Across the DMTA workflow, three areas stand out as particularly promising for improvement. The first is purification, where labor-intensive handling procedures continue to limit throughput despite advances in synthetic chemistry. The second is testing, where emerging biological models—including organoids—offer richer, more predictive data but remain difficult to implement at scale. The third, and perhaps most consequential, is the interface between make and test, where conventional workflows continue to introduce delays and create barriers between chemistry and biology.

"Historically, chemistry and biology have often operated as distinct functions, sometimes separated geographically as organizations optimized for cost," Wingfield explains. "Today, speed has become far more important than cost alone." Several leading pharmaceutical organizations have already begun investing in integrated discovery environments that bring chemistry, biology, and automation teams together within the same location.

This physical integration opens the door to more radical workflow redesigns. Rather than maintaining vast centralized compound libraries—where the majority of compounds are unlikely to demonstrate activity in most assays—organizations could synthesize compound libraries on demand using commercially available building blocks. Newly synthesized compounds would move directly into testing, with only those demonstrating long-term value being resynthesized and added to permanent storage. Where chemistry and biology permit, this concept could extend to testing unpurified reaction mixtures directly, creating a streamlined workflow well-suited to automation.

High-Throughput Experimentation and the Make Stage

Within the make stage itself, high-throughput experimentation (HTE) is gaining recognition as a foundational capability. Using microscale chemistry, reactions can be performed in microtiter plates with the assistance of high-precision technologies such as acoustic dispensing. "Although microtiter plates are traditionally associated with biochemical applications, there is no fundamental reason they cannot be adapted for synthetic chemistry," Wingfield states. "With appropriate materials science expertise and innovative engineering, these platforms could be designed to accommodate commonly used organic solvents and chemical reactants."

The growing adoption of HTE platforms across the pharmaceutical industry demonstrates the appeal of this strategy. These systems allow hundreds or even thousands of reactions to be screened simultaneously, producing richer reaction datasets while enabling more efficient exploration of chemical space.

Another promising strategy involves applying machine learning to generate new synthetic protocols automatically from standardized literature databases. These AI-generated protocols can then be combined with robotic automation, allowing existing laboratory glassware and established synthetic processes to remain in use without requiring major modifications.

From Automation to Autonomy

Xu describes automation as evolving from simple task execution toward genuine autonomy. "Much like the progression from cruise control to self-driving vehicles, laboratories are moving beyond systems that merely perform repetitive tasks toward systems that can diagnose issues, adapt, and potentially self-correct," she says.

A major challenge remains cultural rather than technical. Scientists often intervene when workflows encounter problems because manual intervention appears faster in the short term. Truly autonomous laboratories, Xu argues, require robust systems that can monitor themselves, preserve valuable samples, recover from errors, and continue operating with minimal human involvement.

At the same time, automation is increasingly viewed as an end-to-end process rather than a collection of individual instruments. Modern systems integrate logistics, experimentation, data capture, analytics, and decision-making into a continuous workflow, enabling teams to focus more on scientific strategy and less on repetitive operational tasks.

The Data Imperative

Underpinning all of these developments is a growing recognition that data quality and comprehensiveness are paramount. Wingfield points to an important evolution in the industry's thinking: "Some of the most valuable learning comes from understanding why molecules failed, whether due to synthesis challenges, purification issues, biological inactivity, or toxicity concerns." As a result, more organizations are investing in generating their own comprehensive datasets that include both successful and unsuccessful outcomes.

Xu sees technologies such as microarrays, microfluidics, and ultra-high-throughput screening playing a critical role in generating the datasets needed to fully realize AI's potential. Beyond early discovery, she notes that AI is beginning to influence clinical development through applications such as patient stratification, recruitment optimization, and analysis of organoid-derived datasets. "Better understanding of toxicity, ADME, PK/PD, and efficacy using more human-relevant models may ultimately reduce costs and improve outcomes across the development pipeline," she says.

The Path Forward

The forces reshaping DMTA are already in place. AI is accelerating molecular design, while continued advances in automation and data analytics are improving the speed and quality of decision-making. The remaining question is whether the rest of the discovery workflow can evolve quickly enough.

Meeting this challenge will require coordinated improvements across multiple areas: more scalable purification methods, testing systems capable of delivering richer and more predictive biological data at industrial scale, and closer integration of chemistry and biology throughout the discovery process. A unified laboratory IT platform capable of capturing highly granular, time-stamped performance data could enable organizations to monitor project progress in real time, identify bottlenecks quickly, and optimize workflows continuously.

"Future success will belong to organizations that no longer treat designing, making, testing, and analyzing as separate activities, but instead optimize DMTA as one integrated system," Wingfield concludes. "Achieving the iteration speed required for AI-enabled drug discovery will require much more than faster algorithms. It will also depend on new approaches to data infrastructure, decision-making, synthesis, purification, testing, and the interfaces that connect each of these components."