Sponsored Content by TTP plcReviewed by Maria OsipovaJul 15 2026
In this interview, Jon Wingfield discusses how the next generation of DMTA is creating a future-ready drug discovery engine through AI and automation.
Artificial intelligence (AI) is transforming many aspects of pharmaceutical research. Why do you believe it is time to rethink the design, make, test, and analyze (DMTA) cycle?
The DMTA cycle has long served as the foundation of preclinical drug discovery. However, its relatively slow iteration speed is becoming a significant limitation as pharmaceutical organizations pursue higher levels of productivity.
Although AI-driven workflow improvements represent an important part of the solution, they alone will not be enough. The make stage, along with its connection to test, must also evolve in terms of both operational processes and underlying technologies.
Without these changes, these stages are likely to become the next major bottlenecks within the drug discovery pipeline.
AI is already accelerating molecular design. How is that changing the overall discovery process?
Artificial intelligence is reshaping drug discovery across multiple disciplines. From molecular design and synthesis planning to data analysis and decision-making, AI has the potential to greatly accelerate the generation and evaluation of new therapeutic concepts.
However, increasing the speed of molecular design only creates value if the remainder of the discovery workflow can progress at a similar pace.
What new challenges emerge as AI enables researchers to explore larger areas of chemical space?
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. Put simply, the primary limitation in drug discovery is shifting away from idea generation and toward the productivity of the DMTA cycle itself.
Why is improving productivity becoming such a critical issue for the pharmaceutical industry?
This shift is particularly important because commercial pressures within drug discovery continue to increase. The industry is now concentrating on increasingly complex biological targets, while advances in patient stratification are creating greater demand for personalized therapies.
Collectively, these developments require organizations to evaluate more compounds, produce larger volumes of data, and make higher-quality decisions, all while reducing development timelines and controlling costs.
Have previous advances in drug discovery not addressed these productivity challenges?
Over the past several decades, improvements in drug discovery productivity have largely resulted from advancements within individual parts of the workflow, including high-throughput screening, laboratory automation, and computational modeling.
However, incremental improvements within isolated functions are unlikely to meet the demands of the next generation of drug discovery. As the number of compounds and the volume of data continue to increase, bottlenecks that were once manageable are becoming significant barriers to innovation.
What will distinguish organizations that succeed in the next era of drug discovery?
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.
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.
Despite decades of technological progress, why has improving productivity remained so difficult?
Although significant innovation has occurred in areas such as high-throughput screening, laboratory automation, and molecular modeling, meaningful gains in overall drug discovery productivity have remained difficult to achieve.
Many of the more straightforward opportunities have already been addressed, while the growth of personalized medicine is increasing demand for generating more data and efficiently evaluating a greater number of compounds.
Against this backdrop, AI presents tremendous opportunities. However, its value will depend not only on generating better molecular candidates but also on accelerating the translation of those candidates into experimental learning.
One consequence of the AI revolution is that significantly more compounds will progress through the early stages of the DMTA cycle. Meeting this demand will require a dramatic increase in the iteration velocity of the entire DMTA process.
This objective cannot be achieved simply by making incremental improvements to disconnected processes within departmental silos, as has traditionally been the case.
Instead, organizations must adopt a more holistic view of DMTA, focusing on efficiently exploring molecular space while generating high-quality data that supports faster and more informed decision-making.
Which parts of the workflow currently offer the greatest opportunities for improvement?
Across the DMTA workflow, three areas stand out as particularly promising opportunities 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 and more predictive data but remain difficult to implement at scale.
The third is the interface between make and test, where conventional workflows continue to introduce delays and create barriers between chemistry and biology.
Together, these challenges are likely to become the defining constraints on DMTA iteration velocity over the next decade.
The industry is already seeing this transition take place. Investments in AI are accelerating the early stages of drug discovery, allowing research teams to explore broader regions of chemical space and generate promising candidates more efficiently than ever before.
However, expanding design capacity provides only limited benefit if downstream discovery activities cannot keep pace. As a result, increasing attention is being directed toward improving the productivity of the entire DMTA system and addressing the bottlenecks that restrict its iteration velocity.
If you could redesign the DMTA cycle from scratch, what would an ideal system look like?
If starting with a blank slate, the first priority would be overcoming the structural inertia that still exists across many areas of the pharmaceutical industry. Achieving this would require implementing more efficient IT systems and governance frameworks.
A critical step would be adopting a unified laboratory IT platform capable of capturing highly granular, time-stamped performance data without temporal gaps. Such a platform could monitor project progress in real time, allowing organizations to identify bottlenecks quickly, optimize workflows continuously, and ensure that processes operate at maximum efficiency.
An equally important priority is improving how project decisions are made. This requires placing greater emphasis on data-driven decision frameworks that reduce subjective bias while increasing consistency across decision-making processes.
With such frameworks in place, samples could be routed rapidly to the most appropriate assays without requiring continuous human intervention.
This approach would also help address a common issue within drug discovery: projects often continue longer than they should because teams become personally invested in achieving success and are therefore reluctant to terminate unsuccessful programs or learn from those failures.
Once these organizational and digital changes have been established, companies are far more likely to realize the full value of rethinking the technologies that underpin DMTA processes. A stronger operational foundation creates the conditions needed for more transformative improvements throughout the discovery workflow.
Among the different stages of DMTA, which areas have progressed the most?
Within the DMTA cycle, the test stage has already benefited from extensive automation, largely as a result of knowledge transferred from the high-throughput screening revolution of the 1990s.
Furthermore, continued advances in testing technologies, including phenotypic assays, deep metabolomic analysis, and increasingly sophisticated organoid models, suggest that generating richer and more predictive biological data at scale is becoming increasingly achievable.
These next-generation testing platforms have the potential to significantly improve decision quality, provided they can be manufactured, standardized, and deployed at sufficient throughput to support future DMTA workflows.
However, though progress in testing has been substantial, these improvements have historically delivered only limited reductions in overall discovery timelines. This is largely because many of the most obvious workflow and organizational efficiencies have already been captured.
Today, the primary bottlenecks are more commonly found within the make stage, where successful synthesis continues to depend heavily on the expertise and judgment of experienced chemists.
How can the traditional limitations of synthetic chemistry be overcome?
If DMTA is to evolve as required, new approaches must be found to overcome the longstanding throughput limitations of synthetic chemistry.
One promising strategy currently under investigation 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.
Are there other technologies that could significantly increase synthetic throughput?
Another compelling opportunity lies in high-throughput experimentation (HTE) for synthetic chemistry.
Using microscale chemistry, reactions can be performed in the wells of microtiter plates with the assistance of high-precision technologies such as acoustic dispensing. This enables researchers to explore chemical space far more efficiently.
Although microtiter plates are traditionally associated with biochemical applications, there is no fundamental reason they cannot be adapted for synthetic chemistry. With appropriate materials science expertise and innovative engineering, these platforms could be designed to accommodate commonly used organic solvents and chemical reactants.
Is industry already beginning to adopt this approach?
Yes. The growing adoption of high-throughput experimentation platforms across the pharmaceutical industry demonstrates the appeal of this strategy. These systems allow hundreds or even thousands of reactions to be screened simultaneously.
By producing richer reaction datasets while enabling more efficient exploration of chemical space, HTE is increasingly recognized as a foundational capability for future DMTA workflows.
Besides synthesis itself, where else can throughput be improved?
Another opportunity lies in addressing the purification bottleneck by redesigning the manual operations that occur between synthesis and chromatography.
Alternatively, purification requirements could be reduced or even eliminated by selecting clean, single-pot multicomponent reactions or by employing solid-phase technologies that enable direct-to-biology (D2B) testing.
You have mentioned that the individual stages of DMTA are important, but what about the connections between them?
In many respects, the abbreviation DMTA itself contributes to the problem by encouraging people to think of design, make, test, and analyze as four entirely separate stages. In reality, the interfaces between these activities are equally important. One of the most significant constraints on DMTA efficiency is often the transition between the make and test stages.
During the early 2000s, many pharmaceutical companies relocated the labor-intensive make stage, particularly during the early DMTA cycles, to offshore locations to reduce staffing costs.
Although this decision made good business sense at the time, the requirement to transport compounds across the globe has become increasingly impractical. Shipping introduces delays, creates supply chain dependencies, and increases the environmental footprint of the discovery process.
Consequently, the long-term advantages of locating the make and test functions in close physical proximity have become increasingly evident.
Several leading pharmaceutical organizations have already begun investing in integrated discovery environments that bring chemistry, biology, and automation teams together within the same location.
While the implementation strategies differ from one organization to another, the objective remains consistent: reducing the time between compound synthesis and biological testing so decisions can be made more rapidly and with greater confidence.
Does bringing these functions together also create opportunities to improve compound management?
Absolutely. As organizations rethink the relationship between make and test, they should also reconsider how compounds are managed across this interface.
For many years, pharmaceutical companies have regarded their long-term compound collections as valuable strategic assets. However, the reality is that the majority of compounds within these collections are unlikely to demonstrate activity in most assays or screening campaigns.
This inefficiency is compounded by the centralization of compound libraries, which was originally intended to eliminate duplication but has instead increased both the time and cost associated with processing compounds through these collections.
A more effective strategy would be to synthesize compound libraries on demand within the organization using commercially available building blocks and, ideally, the advanced synthetic approaches discussed earlier.
Newly synthesized compounds could then move directly into the test stage, while only those demonstrating long-term value would be resynthesized and added to permanent storage.
Where both the chemistry and biology permit, this concept could be extended even further by testing unpurified reaction mixtures directly. Such an approach would strengthen the integration between make and test, creating a more streamlined workflow that is well-suited to automation.
What is driving the need to rethink the DMTA cycle today?
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 to match these advances.
Meeting this challenge will require coordinated improvements across several areas. More scalable purification methods are needed, along with testing systems capable of delivering richer and more predictive biological data at industrial scale. Equally important is the closer integration of chemistry and biology throughout the discovery process.
Although none of these developments will transform DMTA on its own, together they have the potential to deliver a substantial improvement in overall discovery productivity.
What will ultimately distinguish the leaders in the next generation of drug discovery?
In the end, the organizations that achieve the greatest success will be those that no longer optimize design, make, test, and analyze as four independent activities. Instead, they will treat DMTA as a single, integrated learning system designed to maximize the speed and quality of discovery.
Organizations exploring ways to use AI, automation, advanced data infrastructure, or emerging laboratory technologies to eliminate bottlenecks within their DMTA workflows are encouraged to engage with TTP's drug discovery team to explore technologies and operating models that can enable higher-velocity discovery.
How does TTP help pharmaceutical and biotechnology companies accelerate drug discovery?
TTP works with pharmaceutical and biotechnology organizations to accelerate drug discovery by addressing bottlenecks throughout the DMTA cycle.
The drug discovery tools team develops bespoke technologies that enable faster experimentation, higher-quality data generation, and more effective decision-making.
Drawing on expertise in biology, chemistry, automation, microfluidics, software, and instrumentation, the team helps clients solve complex challenges involving synthesis, purification, screening, organoid systems, assay development, and integrated laboratory workflows.
Whether the objective is to optimize a specific workflow or develop an entirely new discovery platform, TTP collaborates with clients to increase DMTA iteration velocity and unlock the full potential of AI-enabled drug discovery.
Acknowledgments
Produced using materials originally authored by John Wingfield.
About Jon Wingfield
Jon Wingfield is a senior business development leader within TTP's Drug Discovery and Preclinical Tools team, where he works closely with pharmaceutical, biotechnology, and life sciences organizations to accelerate innovation across the drug discovery pipeline.
With nearly 30 years of experience in pharmaceutical research and development, Jon has developed deep expertise in laboratory automation, high-throughput screening, and design-make-test-analyze (DMTA) workflows. Throughout his career, he has played a key role in helping organizations implement technologies that improve efficiency, data quality, and decision-making in drug discovery.
Jon is also a former President of the Society for Laboratory Automation and Screening (SLAS), reflecting his long-standing leadership within the laboratory automation community. His experience spans both scientific and commercial roles, giving him a unique perspective on how engineering, automation, and data-driven approaches can be combined to accelerate the development of new medicines and improve patient outcomes worldwide.
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