# How SuntheticsML Outperformed High-Throughput Experimentation in Cross-Coupling Chemistry

94% Experiment Reduction in Categorical Reaction Optimization

#### Quick summary

Discover how SuntheticsML optimized solvent and base selection using algorithm driven experiment design.

##### Industry

##### Pharmaceuticals R&D

#### **Client**  
This collaboration between **Ghent University** and **Sunthetics** focused on optimizing categorical variables in a complex Suzuki-Miyaura cross-coupling. The joint team aimed to compare the predictive power and resource efficiency of SuntheticsML with traditional high-throughput experimentation (HTE)​.

#### **Challenge**  
- Determine the **best-performing catalyst, base, and solvent** combination from a large pool of categorical options.  
- Handle **high dimensionality** in experimental design without requiring parameterization.  
- Reduce the **resource cost and scale** of traditional HTE workflows, which demand hundreds of reactions and significant infrastructure.

#### **Goal**  
- Achieve or surpass the performance of HTE using **significantly fewer experiments**.  
- Demonstrate that ML can **efficiently optimize categorical variables** without prior assumptions or encoding.  
- Reveal **non-intuitive variables of influence** to inform future R&D strategy.

#### **Approach & Solution**  
- Used **SuntheticsML’s proprietary Supervised + Active Learning (SL + AL)** framework.  
- No one-hot encoding or parameter mapping was needed—true **categorical optimization** was performed directly.  
- Each ML seed (5 total) started from **36 randomly selected experiments** (out of 768).  
- At each iteration, SuntheticsML suggested **6 experiments**:
  - 5 predicted optimal points  
  - 1 exploratory point for model refinement  
- The experiment recommendations adapted dynamically as performance data improved.

#### **Results & Metrics**  
- **Traditional HTE**:
  - Required **768 experiments** to map the full design space.  
- **SuntheticsML outcomes**:
  - **Average performance**:
    - **6 iterations**, **72 experiments**, **91% experiment reduction**  
  - **Best performance**:
    - **2 iterations**, **48 experiments**, **94% experiment reduction**  
  - **Worst case**:
    - **9 iterations**, **84 experiments**, **89% reduction**  
- **Speed to optimum**:
  - Reached the same global maximum as HTE with a **fraction of the cost and time**.  
- **Insight from variable importance analysis**:
  - Contrary to expectation, **base selection had the largest influence** on reaction yield.  
  - Solvent and catalyst changes had significantly **less predictive weight**, shifting future optimization priorities.

#### **The Sunthetics Edge**  
> "This study shows why categoricals don’t have to be the bottleneck. With SuntheticsML, turned a 768-experiment challenge into a 48-experiment solution—without sacrificing accuracy. The model revealed that base selection—not catalyst or solvent—was the key driver of performance. That’s the kind of insight you don’t get from brute force."

#### **Key Takeaways**  
- **94% fewer experiments** than HTE, with equivalent outcomes.  
- **Optimizes true categorical variables** without any transformation—no parameterization required.  
- **Identified base as the key driver** of reaction yield, challenging conventional assumptions.  
- **Works with small data**—just 36 random starting points were enough to converge on the optimum.  
- **Rapid iteration cycles** (as few as 2 needed) accelerate discovery and reduce costs.