Many pharmaceutical and chemical companies store experimental data for regulatory reasons - few actually use it to accelerate innovation.

Many pharmaceutical and chemical companies store experimental data for regulatory reasons - few actually use it to accelerate innovation.

Reaction optimization is critical in pharmaceutical and fine chemical manufacturing, yet it remains slow, expensive, and heavily reliant on experience, trial-and-error, or Design of Experiments.

Machine learning, particularly Bayesian optimization, has transformed how we find the best reaction conditions, reducing the number of experiments needed.

But there’s still a major limitation: every new optimization campaign starts from scratch - requiring fresh experimental data.

What if past experiments could help optimize future ones?

That’s where transfer learning comes in.This is how transfer learning works in reaction optimization:

1) Pretraining on existing data: Train ML models on historical reaction data (e.g., similar or same chemical reactions).

2) Feature mapping and adaptation: Identify generalizable patterns (e.g., solvent effects, catalyst behavior, temperature dependencies).

3) Fine-tuning with new experimental data: Use past knowledge to recommend better starting conditions and refine predictions as new data comes in.

Transfer learning is particularly valuable when working with expensive chemicals or very limited amounts of material, for example, during late-stage functionalization of small molecule drugs.

However, our research has shown that data quality is crucial - poor-quality data can introduce biases that negatively impact optimization.

That’s why at Reactwise, we’re building high-quality datasets covering the most relevant transformations in pharma and fine chemicals.

By ensuring data integrity, our users can kick-start optimization using pre-trained reactivity models with confidence, rather than relying on incomplete or biased datasets.

This can save months of iteration.

If you’re interested in seeing transfer learning in action, book a free discovery session.