Flow chemistry or HTE? When machine learning enters the lab, the 'best setup' isn’t one-size-fits-all. I often get this question from clients and prospects:
"We want to automate our laboratory workflow to leverage new digital tools. Should we set up a flow setup with self-driving capabilities, or go for high-throughput experimentation?"
And my answer is always the same: it depends.
It depends on the scope of your work — and where you are in the drug discovery process.
In discovery chemistry, you're often building large molecule libraries by rapidly exploring different structures.
High-Throughput Experimentation (HTE) is perfect here - you can run up to hundreds of parallel reactions on well plates, varying catalysts, solvents, or additives across a plate to find the best combinations fast.
In process development, the challenge shifts: you're optimizing the synthesis of a specific molecule to prepare for scale-up and manufacturing.
This is where Continuous Flow Chemistry combined with self-driving lab automation can be a game-changer.
Flow chemistry is a technique where chemical reactions are run continuously through small, controlled channels or tubing, allowing precise control over reaction conditions like temperature, pressure, and reaction time.
Here’s a quick breakdown to highlight the two methods in more detail:
Flow Chemistry + Self-Driving Labs
Strengths:
Challenges:
High-Throughput Experimentation (HTE)
Strengths:
Challenges:
In short:
And the most efficient labs use both - first explore broadly with HTE, then translate, fine-tune and scale with flow.
At ReactWise, we provide an AI copilot for chemical process optimization and also help our clients choose the right automation capabilities for their needs.
We integrate seamlessly with equipment for both high-throughput and continuous flow workflows, and we even support batch-to-flow transfer using transfer learning to accelerate your transition.