Trainer Engine
Predict molecular properties and boost the efficiency of machine learning workflows.
Trainer Engine makes chemical, physical and biological activity predictions available by streamlining learning from input data with high accuracy, reliability and confidence at scale. The framework simplifies sharing models, and managing the machine learning lifecycle.
Translate data to prediction
The biological, chemical and physical properties of molecules are encoded in their molecular structure. The grand challenge is to discover the relationship between structural properties and the measured activity. Where data is measured, collected and curated for a series of compounds, there is an opportunity to find the hidden relationships.
From input data to implementation of validated models.
Chemical structure normalization, high-quality and customizable descriptors.
Rich feedback and visualization for model optimization.
Central model repository to support selecting production grade models.
Access to predictions from a built-in graphical interface, Design Hub or other design platforms.
Successful models built on bio-activity, ADMET and phys-chem targets.
Machine learning
Trainer Engine offers automatized, high performant, and configurable descriptor generation on normalized chemical data. It provides a wide range of machine learning algorithms including Random Forest, Gradient Boosted Trees, Support Vector Machine, and Logistic Regression. Model performance is automatically evaluated, and the most important statistical parameters are calculated both for regression and classification cases. Feature selection is supported by seamless re-training, based on feature importance in the case of Random Forest. Calibrated error is calculated using the conformal prediction framework. Applicability domain assessment is enabled by returning the most similar structures and corresponding activity data from the training set.
Visualization
The collection of generated models is accessible from the central service in order to benchmark their prediction power and provide insights into their behavior. Trainer Engine stores the models in a repository to ensure reproducibility and comparison of their parameters conveniently. The configurable analysis view comes with a classification and a regression layout presets with optimized tables, charts and molecule visualizations.
Usage
