Model Building Toolkit
Utilize our universal molecular encoder and build custom predictive models with deep chemical understanding. Follow this 5-step process to transform your lab data into AI-powered formulation optimization.
Select Industry Example:
Our platform supports formulation development across diverse industries including pharmaceuticals, agriculture, food & beverage, personal care, and more.
Choose Data Type
Select the type of model that matches your prediction goal. Different data types enable different kinds of predictions. Anything with data that fits in these categories can be predicted.
Classification
Predict from 2 or more classes: true/false, categories, or scores/ranks
Examples:
Regression
Predict a numerical value with continuous output
Examples:
Time Series
Predict numerical values on a graph over time or conditions
Examples:
Set Target Criteria
Define what property you're predicting and the classes or range of values you expect. For this example, we chose Classification.
Example: Cross-Hatch Adhesion (ASTM)
Classification model with 6 adhesion grade classes
Upload Your Data
Model training starts automatically once you upload your formulation data with ingredients, conditions, and target property measurements.
| Sample | pH | Acrylates Copolymer | Titanium Dioxide | HASE | Temp (C) | Adhesion |
|---|---|---|---|---|---|---|
| Sample 1 | 7.5 | 20% | 8% | 1% | 25 | 0B |
| Sample 2 | 7.5 | 22% | 9% | 1% | 25 | 1B |
| Sample 3 | 7.5 | 25% | 10% | 1.2% | 25 | 2B |
Data shown for illustrative purposes. Training starts automatically after upload.
Select Optimization Criteria
Choose ingredients for your new formulation, set concentration ranges, and define optimization targets for multiple properties.
Selected Ingredients
Concentration ranges and values for each ingredient can be specified to constrain the optimization space.
Optimization Targets
Get Ranked List of Best Potential Concentrations
The optimizer runs dozens of simulations to find the best possible compositions. Get a ranked list to do informed trials instead of trial-and-error.
Use these ranked formulations to guide your lab experiments with higher confidence
Choose Data Type
Select the type of model that matches your prediction goal. Different data types enable different kinds of predictions. Anything with data that fits in these categories can be predicted.
Classification
Predict from 2 or more classes: true/false, categories, or scores/ranks
Examples:
Regression
Predict a numerical value with continuous output
Examples:
Time Series
Predict numerical values on a graph over time or conditions
Examples:
Set Target Criteria
Define what property you're predicting and the classes or range of values you expect. For this example, we chose Classification.
Example: Texture
Classification model with 5 texture classes
Upload Your Data
Model training starts automatically once you upload your formulation data with ingredients, conditions, and target property measurements.
| Sample | pH | SLES | Cocamidopropyl Betaine | Glycerin | PEG-7 Glyceryl Cocoate | Fragrance | Temp (C) | Texture |
|---|---|---|---|---|---|---|---|---|
| Sample 1 | 5.5 | 14% | 8% | 3% | 1.50% | 0.30% | 25 | Smooth |
| Sample 2 | 5.5 | 10% | 3% | 1% | 0.50% | 0.20% | 25 | Watery |
| Sample 3 | 5 | 16% | 4% | 3% | 2.50% | 0.50% | 25 | Creamy |
Data shown for illustrative purposes. Training starts automatically after upload.
Select Optimization Criteria
Choose ingredients for your new formulation, set concentration ranges, and define optimization targets for multiple properties.
Selected Ingredients
Concentration ranges and values for each ingredient can be specified to constrain the optimization space.
Optimization Targets
Get Ranked List of Best Potential Concentrations
The optimizer runs dozens of simulations to find the best possible compositions. Get a ranked list to do informed trials instead of trial-and-error.
Use these ranked formulations to guide your lab experiments with higher confidence
Choose Data Type
Select the type of model that matches your prediction goal. Different data types enable different kinds of predictions. Anything with data that fits in these categories can be predicted.
Classification
Predict from 2 or more classes: true/false, categories, or scores/ranks
Examples:
Regression
Predict a numerical value with continuous output
Examples:
Time Series
Predict numerical values on a graph over time or conditions
Examples:
Set Target Criteria
Define what property you're predicting and the classes or range of values you expect. For this example, we chose Classification.
Example: Shelf Stability
Classification model with 2 stability classes
Upload Your Data
Model training starts automatically once you upload your formulation data with ingredients, conditions, and target property measurements.
| Sample | pH | Soybean Oil | Distilled White Vinegar | Sugar | Ground Mustard | NaCl | Lecithin | Temp (C) | Shelf Stability |
|---|---|---|---|---|---|---|---|---|---|
| Sample 1 | 3.6 | 80% | 4% | 1% | 1% | 1% | 4% | 25 | Unstable |
| Sample 2 | 3.6 | 80% | 4% | 1% | 1% | 1% | 7% | 25 | Stable |
| Sample 3 | 3.6 | 80% | 4% | 1% | 1% | 1% | 10% | 25 | Stable |
Data shown for illustrative purposes. Training starts automatically after upload.
Select Optimization Criteria
Choose ingredients for your new formulation, set concentration ranges, and define optimization targets for multiple properties.
Selected Ingredients
Concentration ranges and values for each ingredient can be specified to constrain the optimization space.
Optimization Targets
Get Ranked List of Best Potential Concentrations
The optimizer runs dozens of simulations to find the best possible compositions. Get a ranked list to do informed trials instead of trial-and-error.
Use these ranked formulations to guide your lab experiments with higher confidence
Ready to transform your formulation process?
See how FastFormulator can help you reduce development time, minimize failed experiments, and bring products to market faster with AI-powered predictions.