There’s no free lunch, but you can get a discount – applying active learning in drug discovery | Pat Walters & James Thompson
Molecular Modelling and Drug Discovery12 Apr 2023

There’s no free lunch, but you can get a discount – applying active learning in drug discovery | Pat Walters & James Thompson

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠YouTube channel ⁠⁠⁠to see the presented slides.

Try datamol.io - the open source toolkit that simplifies molecular processing and featurization workflows for machine learning scientists working in drug discovery: https://datamol.io/

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Abstract: While computational methods have become a mainstay in drug discovery programs, many calculations are too time-consuming to be applied to large datasets. Active learning (AL), a machine learning method used to direct a search iteratively, can enable the application of computationally expensive methods such as relative binding free energy (RBFE) calculations to sets containing thousands of molecules. Moreover, AL can also be applied to virtual screening, enabling the rapid processing of billions of molecules. This presentation will provide an overview of active learning and highlight some applications in drug discovery.

Speakes: Pat Walter & James Thompson

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