Simplify the lifecycle of sharing learning models! Deploy predictions of biological activity and physicochemical properties within the organization and streamline them efficiently!
The "Trainer Engine" can build and deploy high-precision predictive models, uncovering relationships between hidden chemical structures and activity values. It also supports various machine learning algorithms, automatically normalizing chemical data to generate high-performance descriptors. The most important statistical parameters are calculated for both regression and classification cases. It excels in reproducibility and visualization, ensuring reproducibility by centrally managing models in a repository, making it easy to compare parameters. Various display formats for analysis views are available, allowing for customization according to purpose. 【Features】 ■ Comprehensive coverage from data ingestion to the internal deployment of validated models ■ Intelligence utilizing standardized structural formulas and high-quality, customizable descriptors ■ Abundant feedback and visual convenience for model optimization ■ Reproducibility that supports the selection of adopted models in a central repository ■ Integration allowing access to predictive functions from an embedded graphical interface and other design platforms ■ Capability to predict biological activity, ADMET, and physicochemical properties
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【Examples of Corresponding Machine Learning Algorithms】 ■ Random Forest ■ Gradient Boosting ■ Support Vector Machine ■ Logistic Regression 【Examples of Visualization】 ■ Table ■ Chart ■ Classification Layout ■ Regression Layout
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Patocoa Inc. is a specialized company in drug discovery support systems. We are also focused on improving and streamlining compliance-related operations concerning regulated substances, and we have provided various solutions primarily to pharmaceutical companies, as well as chemical companies, universities, and government agencies. We contribute to accelerating drug discovery and reducing costs with our cutting-edge cheminformatics systems.