TIDES 2026 Shows that Oligonucleotide Discovery is Moving to ‘The Beach’
July 7, 2026
Ming Wang
At TIDES 2026, one theme stood out: oligonucleotide discovery is no longer split between wet and dry labs — it’s becoming a unified, data-driven system.
A new ‘tide’ is rising in the oligonucleotide field: experimental scientists and data scientists are now deliberately and purposedly coming together to work at the metaphorical 'beach', an amicable place where the wet and the dry labs actively collaborate in harmony. With wet and dry groups traditionally working in parallel, this type of intentional 'damp' collaboration has been growing for the past few years. In recent times, focus on this topic has intensified, as seen in presentations at TIDES US 2026, and new partnerships such as that between Alnylam and Inceptive.
In Silico Methods Help Navigate a Vast Design Space
In silico design is now essential to navigate the large combinatorial search space for oligo drug design. Computational models can predict the most promising sequences and chemistries or at least narrow the field to a manageable shortlist for experimental validation. While dry scientists build predictive algorithms, the wet scientists design and execute experiments that generate the datasets these models depend on.
Big companies such as Roche, Servier and Alnylam have invested in this approach for a while, but what was clear at TIDES 2026 is that smaller oligo biotechs are now doing the same. Companies such as ProQR, Korro Bio, and Splisense, showcased how closer wet-dry collaboration can directly improve drug efficacy and oligonucleotide discovery speed.
Automation Accelerates the Tide
From the wet side, what seems key is the use of automation. Automation hardware, together with automation software, in particular for data analysis, help generate the large AI-ready datasets needed to train predictive models within reasonable timeframes.
ProQR showed how their automated high-throughput synthesis and screening platforms produce the clean and broad datasets that allow dry scientists to develop effective predictive ML models. These algorithms have produced great results, where they are able to design oligo candidates with several fold increase in efficacy, at lead times of “a few months rather than 30 months” (from a presentation by Gerard Platenburg, CSO of ProQR at TIDES 2026). Korro Bio demonstrated similar results: by combining automation with ML algorithms, they reduced the time from ‘design to data’ to just four weeks, and produced oligos several times more effective than manual designs.
Beyond GalNAc: Scaling the Search
The same principles are now applied beyond sequence design. Companies such as Manifold Bio and Gensaic are applying a similar approach to oligonucleotide targeting rather than sequence and chemistry optimization. Their goal is to identify the next GalNAc: targeting molecules that direct oligos away from the liver and towards other tissues of interest. Since the screening is done in vivo, multiplexed barcoding technologies help limit the number of animals used in experimentation. The resulting data then feed the AI models that guide the design of next generation targeting molecules.
The Takeaway: Connected Data is the New Discovery Advantage
Taken together, these perspectives suggest a clear shift: oligonucleotide discovery is moving from isolated wet and dry science, toward a more intentional and collaborative effort. With automation at the heart of wet science, competitive advantage is moving away from individual molecules and will depend on how well organizations can generate, structure, and learn from large-scale datasets.
I felt this shift firsthand at TIDES, where conversations around high-throughput oligo screening and AI-ready data all pointed in the same direction: better discovery starts with better-connected data.
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