Workflows in pharmaceutical manufacturing are often data-hungry, but many R&D teams are stuck working with small, noisy datasets.
Whether it’s chemical process optimization, process scale-up, or troubleshooting, chemists often don’t have the luxury of running hundreds of experiments to generate large datasets.But here’s the good news: You don’t need big data to achieve significant insights.
At ReactWise, we’ve built an AI platform with Bayesian Optimization at its core — a technique that’s well suited for small data problems.It’s designed to work with limited, high-uncertainty data, guiding chemists toward the most promising experiments faster than traditional methods.
We don’t just rely on your data — we can kickstart your process optimization by leveraging our proprietary reaction database and pre-trained models.
Our high-quality datasets, generated through high-throughput experimentation (HTE) campaigns, allow us to support clients even when they have very few data points.
What does it mean?
- Faster optimization
- More reliable predictions
- Fewer experiments needed to achieve process breakthroughs.
Imagine you’re optimizing a reaction yield. Instead of running 200+ experiments to identify the ideal temperature, solvent, and catalyst, ReactWise helps you get there in as few as 15-30 experiments.
With smart, data-driven suggestions after every experiment, we help chemists:
✅ Focus on what matters
✅ Avoid dead-end conditions
✅ Extract maximum insights from minimal data
The data scarcity problem in pharma is real — but it's solvable.
Machine learning tools like ReactWise empower R&D teams to unlock significant process improvements with the data they already have — or by leveraging our proprietary models and datasets.
If you’re struggling to optimize your reactions with limited experimental data, maybe it’s time to rethink your approach.
You don’t need exhaustive experimentation — you need smarter experimentation.
Let’s stop worrying about the lack of data and start focusing on what’s possible.