What is MolFlux?#
Background#
MolFlux is a foundational package for molecular predictive modelling. It covers the five main pillars of molecular machine learning
🗂️ Datasets: Accessing and handling data
🌈 Features: Computing and extracting features
🔀 Splits: Partitioning data for model training and benchmarking
🤖 Modelzoo: Handling and training models
📈 Metrics: Computing metrics for a variety of tasks
MolFlux provides self-contained access to the machine learning ecosystem to enable you to build machine learning models from scratch.
The Standard API#
One of the main challenges of building machine learning models and keeping up to date with the state-of-the-art is the variety of APIs and interfaces that different models and model packages follow. Even the same submodules in the same package can have different APIs. This makes the work of using and comparing the rapidly increasing number of models and features difficult and time-consuming.
The unifying principle of MolFlux is standardisation. Whether you’re trying to extract basic features from data, use a simple random forest regressor, or trying to train a complicated neural network, the API is the same. The motto is “if you learn it once, you know it always”! What the standard API also provides is smooth interaction between the different submodules.
Modular#
Including so much functionality in one package is not trivial and python dependencies can often become daunting. The MolFlux package handles this by being highly modular. You can easily unlock more functionality by installing their corresponding dependencies.
The modular design of the system greatly simplifies the integration of new models, features, and datasets. Its robust, yet simple abstractions are capable of managing everything from basic to complex models and features.
Acknowledgements#
The molflux package has been developed by researchers and engineers at Exscientia
Alan Bilsland
Julia Buhmann
Ward Haddadin
Jonathan Harrison
Dom Miketa
Emil Nichita
Stefanie Speichert
Hagen Triendl
Alvise Vianello