Advanced Modeling Collection for the SciTegic Pipeline Pilot Platform

The Pipeline Pilot Advanced Modeling component collection provides methods for Recursive Partitioning (RP) and Multi-objective Pareto Optimization. The Recursive Partitioning components provide a variety of RP methods including single tree and forest of trees learners. The methods can learn on single or multiple response variables. The Pareto Optimization components provide methods for multiobjective optimization problems, provide solutions whose criteria trade off amongst two or more partially conflicting goals.

 

With the Recursive Partitioning Components you can

• Perform very rapid learning and data mining experiments on very large datasets with very large numbers of descriptors
• Learn molecular datasets using fingerprints as descriptors
• Visualize trees to understand the relationships between descriptors and responses
• Analyze descriptor usage to identify the most discriminating descriptors
• Rapidly apply models to new predict new data sets

With the Pareto Optimization Components you can

• Optimize solutions for problems as diverse as combinatorial library design, formulation ingredient optimization or stock portfolio risk management
• Find individual samples with a dataset that have the best tradeoff of desired property values
• Find subsets of samples from a larger dataset that collectively have the best trade-offs between desired property values