Sponsored Content by TTP plcReviewed by Maria OsipovaJul 15 2026
In this interview, Wenshu Xu discusses how connecting make and test workflows can accelerate drug discovery by improving decision-making across the DMTA cycle.
Artificial intelligence (AI) is accelerating many stages of drug discovery. Why has the relationship between make and test become such an important focus in the DMTA cycle?
Pharmaceutical companies are investing heavily in artificial intelligence to accelerate the design, make, test, and analyze (DMTA) cycle. However, improving the speed of decision-making alone will not transform drug discovery if the make and test stages continue to operate as disconnected processes.
For many years, purification has served as an essential gateway between chemical synthesis and biological evaluation, creating a clear separation between chemistry and biology. Today, advances in chemistry, assay technologies, and workflow integration are challenging this traditional model, creating new opportunities to bring these two disciplines closer together and accelerate discovery workflows.
As AI increases the number of compounds being designed, the primary limitation increasingly becomes the speed at which those molecules can be evaluated. For many organizations, the separation between chemistry and biology now represents one of the most significant remaining constraints on DMTA throughput.
Why has purification traditionally been considered an essential step before biological testing?
For decades, drug discovery has operated according to a straightforward principle: compounds should be purified before biological evaluation.
This practice developed for sound practical reasons. Impurities could interfere with biological assays, generate false-positive results, or obscure structure-activity relationships. Experiences during the era of combinatorial chemistry reinforced this cautious approach, as poorly characterized mixtures frequently produced unreliable experimental outcomes.
As a result, purification became an established gateway separating the make and test stages.
However, the technological landscape has markedly changed. Chemical synthesis was slower and operated at much lower throughput, biological assays were less sensitive and less selective, and the volume of available experimental data was far more limited than it is today.
The real question is not whether purification continues to play an important role, but whether it still needs to occupy the same position within the overall discovery workflow.
What developments are making closer integration between make and test increasingly feasible?
Several technological advances are making a much closer relationship between the make and test stages both practical and attractive.
Modern biological assays have become considerably more sophisticated than those available when current workflows were originally established. Techniques such as biophysical methods, high-content screening, and orthogonal validation approaches are often capable of handling, or helping researchers interpret, more complex samples than traditional assay formats.
At the same time, artificial intelligence, laboratory automation, and data-driven workflows are contributing to this shift by providing much greater visibility into reaction outcomes.
These technologies make it easier to understand the composition of reaction mixtures and distinguish genuine biological activity from experimental artifacts. Increasingly, they are being integrated into laboratory workflows that minimize manual intervention while accelerating decision-making.
Has chemistry itself also evolved in ways that support greater integration?
Yes: modern synthetic chemistry is becoming increasingly predictable and controlled. High-yielding reactions, one-pot synthetic methods, and well-characterized reaction pathways reduce uncertainty surrounding crude reaction mixtures and increase confidence in downstream biological testing.
Rather than purifying every compound as a matter of routine, future discovery workflows can prioritize the rapid generation of biological insight while reserving purification for situations where it provides the greatest scientific value. Collectively, these developments do not eliminate the need for purification. Instead, they allow purification to be used more strategically.
What emerging technologies are helping to reduce the separation between chemistry and biology?
Several innovative approaches are beginning to bring chemistry and biology together, both physically and within the overall workflow.
One such strategy is direct to biology (D2B), in which arrays of compounds are synthesized directly in assay-compatible plate formats before being evaluated with little or no intermediate purification.
D2B workflows have generated considerable interest because they eliminate one of the largest sources of delay between compound synthesis and biological evaluation.
The growing availability of highly selective building blocks, together with clean synthetic transformations, including click chemistry and carbon-carbon and carbon-hydrogen activation reactions, is making these workflows increasingly practical for drug discovery.
DNA-encoded libraries (DELs) offer another strategy for reducing reliance on traditional purification workflows. By encoding each compound's identity within a DNA sequence, very large collections of molecules can be synthesized and screened while maintaining complete traceability throughout the process.
How are these technologies evolving? Is the pharmaceutical industry beginning to adopt them?
Emerging approaches combine bead-based synthesis, microfluidic technologies, and functional biological assays, allowing active compounds to be identified through DNA sequencing rather than conventional isolation and characterization.
Although these techniques provide exceptional throughput, challenges remain in areas such as droplet handling, sorting efficiency, and assay design.
The increasing maturity of DNA-encoded library technologies is reflected by their growing adoption across the pharmaceutical industry.
Amgen has reported using DNA-encoded library screening to identify a clinical candidate targeting PRMT5, demonstrating the ability of these methods to contribute directly to active drug discovery programs.
Similarly, AstraZeneca has expanded its use of encoded library technologies through collaborations with specialist partners, highlighting growing industry confidence in highly parallel screening approaches.
Beyond D2B synthesis and DNA-encoded libraries, what other technologies are helping integrate chemistry and biology?
Advances in liquid-handling technologies are making it possible to bring chemistry and biology together within nanoliter-scale environments.
Chemical reactions can now be performed inside nanoliter droplets or printed microarrays and then transferred directly into biological testing without requiring intermediate handling steps.
These miniaturized platforms are becoming increasingly capable of supporting more sophisticated biological systems, including organoids and other physiologically relevant models.
As a result, they provide opportunities to perform high-throughput screening while simultaneously generating richer and more biologically meaningful datasets.
What role does automation play in these emerging platforms?
When combined with automated liquid-handling technologies and advanced imaging systems, these screening platforms provide a pathway toward discovery workflows that are both more tightly integrated and more easily scalable.
Recent developments in droplet microfluidics and miniaturized cell-based screening have demonstrated that thousands of biological experiments can be performed using only nanoliter volumes. This dramatically reduces reagent consumption while preserving the depth and quality of biological information generated.
Many organizations are bringing chemistry and biology teams closer together physically. Is co-location alone enough?
Reducing the need to transport compounds between facilities shortens turnaround times and enables greater experimental flexibility.
However, the real opportunity extends well beyond simply placing these disciplines in the same location. The greater opportunity lies in eliminating unnecessary barriers between synthesis and biological testing.
Historically, the make and test stages have functioned as separate disciplines connected primarily through purification and sample transfer. Future workflows are expected to become much more integrated, allowing chemistry and biology to function as components of a continuous experimental system rather than isolated activities.
How would this integrated workflow operate in practice? Is this becoming more important as AI advances?
Within such a system, synthesis, sample preparation, biological testing, and data collection become part of a continuous experimental loop rather than a sequence of independent operations.
Compounds can move directly from synthesis into biological evaluation, while assay results are rapidly incorporated into decision-making processes. This minimizes idle time between stages, shortens the journey from scientific hypothesis to experimental insight, and enables researchers to explore chemical space much more efficiently.
As AI-driven design platforms continue to generate more candidate molecules, maintaining productivity in drug discovery will depend not only on synthesizing compounds more rapidly but also on evaluating them more efficiently.
Integrated workflows therefore provide an effective strategy for increasing DMTA iteration velocity without simply expanding existing processes.
What challenges remain before this vision can become reality?
Achieving this vision will not be without obstacles. Cultural resistance remains an important factor, largely influenced by previous experiences with impure samples and unreliable experimental data.
In addition, the longstanding separation between chemistry and biology remains deeply embedded within many organizations, with chemistry traditionally performed in glassware and biology conducted in microplate formats.
Although overcoming these established practices will require time, the transition toward greater integration has already begun.
Ultimately, the future of the DMTA cycle will not be determined solely by faster synthetic chemistry or more advanced biological assays.
Instead, success will depend on how effectively these capabilities are connected. As synthesis, biological screening, automation, and data analysis become increasingly integrated, the traditional boundary separating make and test will continue to disappear.
Which organizations will be best positioned to benefit from AI-driven drug discovery?
Organizations that successfully establish continuous discovery workflows will be best positioned to capitalize on the full potential of AI-enabled drug discovery.
By creating seamless connections between molecular design, synthesis, biological evaluation, and data-driven decision-making, these organizations will accelerate the journey from molecular concepts to meaningful biological insight.
Organizations that achieve the greatest competitive advantage from AI-enabled drug discovery will be those that minimize the time required to move from compound synthesis to biological learning.
How does TTP help pharmaceutical and biotechnology companies modernize their discovery workflows?
TTP works alongside pharmaceutical and biotechnology companies to accelerate drug discovery by eliminating bottlenecks throughout the DMTA cycle.
The drug discovery tools team develops bespoke technologies that enable faster experimentation, higher-quality data generation, and more efficient scientific decision-making.
Drawing upon expertise in biology, chemistry, automation, microfluidics, software, and instrumentation, the team helps clients address complex challenges spanning synthesis, purification, screening, organoid systems, assay development, and integrated laboratory workflows.
Whether the objective is improving a specific workflow or creating an entirely new discovery platform, TTP partners with organizations to increase DMTA iteration velocity and unlock the full potential of AI-enabled drug discovery.
References and further reading
- Hübner, A.F. and Barthels, F. (2026). Direct‐to‐Biology: Streamlining the Path From Chemistry to Biology in Drug Discovery. ChemMedChem, 21(4). DOI:10.1002/cmdc.202501080. https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/cmdc.202501080.
- Gironda-Martínez, A., et al. (2021). DNA-Encoded Chemical Libraries: A Comprehensive Review with Succesful Stories and Future Challenges. ACS Pharmacology & Translational Science, 4(4), pp.1265–1279. DOI:10.1021/acsptsci.1c00118. https://pubs.acs.org/doi/10.1021/acsptsci.1c00118.
- Amgen Inc. (2020). Driving Drug Design Using DNA Encoded Libraries. Available at: https://www.amgen.com/stories/2025/04/driving-drug-design-using-dna-encoded-libraries.
- X-Chem. (2018). X‑Chem Enters Expanded Global Drug Discovery and Technology Transfer Collaboration with AstraZeneca - X-Chem. Available at: https://www.x-chemrx.com/projects/x%e2%80%91chem-enters-expanded-global-drug-discovery-and-technology-transfer-collaboration-with-astrazeneca/.
About Wenshu Xu
Wenshu Xu is Head of Drug Discovery Tools at TTP, where she leads the development of innovative technologies designed to accelerate drug discovery through advanced automation, high-throughput experimentation, and autonomous laboratory systems.
Her work focuses on enabling faster, more efficient DMTA workflows by bridging artificial intelligence and experimental science. Wenshu has extensive experience in developing bespoke instrumentation, microfluidics, automation platforms, and next-generation screening technologies that support pharmaceutical and biotechnology innovation.
She is particularly interested in the concept of "physical AI," where computational predictions are rapidly validated through automated wet-lab experimentation. Her expertise spans drug discovery technologies, workflow optimization, and laboratory automation, helping organizations generate high-quality experimental data at scale.
Through her work at TTP, Wenshu is helping to shape the future of autonomous drug discovery, in which AI, automation, and advanced analytics work together to accelerate the development of new therapies.
About TTP plc
At TTP we work with start-ups through to global corporates across Defense, Energy & Industrials, MedTech, Life Sciences, Satellite and Space, solving complex challenges rooted in technology.
By combining passion and flexibility with deep expertise in science, engineering and design, we help our clients unlock opportunities that make brilliant things happen. We’re independent, free-thinking and agile to the core, and for nearly 40 years that’s helped us find better solutions, faster - time and time again. Our multidisciplinary teams are shaped around the needs of each project, enabling us to tackle problems holistically from the outset. We work alongside you as one team, committed to your success as much as you are.
Building the next generation of DMTA for future-ready drug discovery