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Automated activity prediction models

We are developing AutoML tools for chemistry data to facilitate adoption of AI/ML
Quick baseline modeling of chemistry data can be done with LazyQSAR, our fast modelling tool. LazyQSAR produces light-weight models for binary classification and regression tasks.
Our flagship AutoML tool for chemistry is ZairaChem. This Python library offers robust ensemble-based modeling capabilities applicable to a wide range of modeling scenarios. At the moment, ZairaChem is focused on binary classification and regression tasks.
In addition, we have developed a model distillation pipeline named Olinda aimed at producing light, interoperable models in ONNX format.