Scientific Update Runs Another Round of Its AI-for-Process-Chemistry Course This November

Scientific Update is running another session of its "Applications of Artificial Intelligence to Process Chemistry" course on 2-3 November 2026, delivered live online across two sessions. Dr Ben Littler is back as tutor. If you've been trying to figure out where AI actually earns its keep in process chemistry versus where it's just marketing, this is the kind of practitioner-facing training that tries to answer that directly.

The format is built for a global audience rather than one time zone's convenience. Sessions run 7-10am PST, 9am-12pm CST, 10am-1pm EST, 3-6pm GMT, and 4-7pm CET, splitting the course across two days to keep each session to a manageable three hours. Standard rate is £599 plus VAT for UK-based companies, and Scientific Update notes this is already a reduced rate with no additional discounts stacking on top, though group bookings can be arranged through their in-house training channel.

For anyone in process chemistry, process R&D, or chemical engineering who's watched AI tools proliferate in drug discovery and wondered when similar capability would land on their side of the pipeline, this course is squarely aimed at that gap. Process chemistry sits downstream of discovery, where the real work of scaling a reaction from milligrams to kilograms (or tonnes) happens, and it's a domain that's historically been harder to automate than discovery-stage screening because the variables multiply: solvent selection, temperature profiles, impurity control, reactor geometry, and safety margins all interact in ways that don't always generalize from small-scale data.

Why Process Chemists Keep Signing Up

The course has a track record worth noting. A previous attendee from a March 2025 session in Charleston called it "an excellent course," adding that "it is clear the lecturer is passionate about the subject of AI and how its evolution will impact the lives of process chemists." That's the kind of endorsement that matters more in a specialized technical training market than star ratings, because it signals the material connects with people who actually do this work for a living, not just AI enthusiasts dabbling in chemistry.

What makes process chemistry a genuinely interesting test case for AI tools is the data problem. Unlike fields with massive public datasets, process chemistry data tends to be proprietary, scattered across internal lab notebooks and electronic lab notebook systems (ELNs), and often incomplete in ways that make training robust models tricky. Reaction yield prediction, retrosynthesis planning, and reaction condition optimization all depend on having enough clean, labeled data, and that's exactly where a lot of AI hype in chemistry runs into real-world friction.

• Yield prediction models need large, consistent datasets across reaction types, which many companies simply don't have in a usable format
• Retrosynthesis tools are powerful for proposing routes but still need expert chemists to validate feasibility at scale
• Optimization algorithms like Bayesian optimization or reinforcement learning can cut the number of experimental runs needed, but only if the search space is well-defined
• Automation and robotics integration (think self-driving labs) is where AI recommendations actually get tested physically, closing the loop between prediction and reality

That last point, closing the loop between model output and physical experimentation, is probably the single biggest shift happening in industrial process chemistry right now. It's not enough for a model to suggest a promising reaction condition. Someone, or something, has to run it, measure the result, and feed that back in. Courses like this one tend to spend real time on where that loop is tightening in industry and where it's still mostly aspirational.

Scientific Update is also already advertising a follow-up date: 20-21 April 2027, also online across two sessions, with booking currently open. That's worth flagging for anyone who misses the November window or whose team needs more lead time to get budget approval, since £599 per seat adds up fast for a multi-person team looking to get trained together.

What to Watch

The bigger story here isn't really about one training course, it's about what training demand like this signals. When a specialist provider runs the same AI-in-process-chemistry course repeatedly, across multiple years and multiple cohorts (Charleston in 2025, now online in late 2026, already scheduled again for 2027), that's a decent proxy for sustained industry appetite rather than a passing trend chasing AI headlines.

Watch for how fast process chemistry specifically, as opposed to drug discovery broadly, starts generating its own benchmark datasets and shared tooling. Discovery-stage AI has benefited enormously from public datasets and competitive benchmarks that let researchers compare methods apples-to-apples. Process chemistry hasn't had that same open-data moment yet, largely because the data is tangled up in proprietary manufacturing know-how that companies are understandably reluctant to share.

Also worth tracking: how self-driving lab infrastructure, the robotics and automation layer that actually executes AI-suggested experiments, keeps maturing. That's the physical bottleneck that determines whether an AI recommendation translates into an actual process improvement or just stays a theoretical best guess. Companies investing in that integration now are likely to be the ones that see real throughput gains first, while everyone else is still manually pipetting their way through validation runs.

If you're weighing whether a course like this is worth the time and £599, the honest answer depends on where your organization currently sits. If you're still figuring out what AI can realistically do for your reaction optimization workflow versus what's vendor hype, structured training from someone who's clearly spent real time in both the chemistry and the AI side, as the Charleston attendee's feedback suggests Littler has, is probably a reasonable investment. If you're already running production ML pipelines against proprietary reaction data, you might get more value from the networking and comparing notes with other attendees than from the core material itself.