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Abstract: Machine learning methodologies have become increasingly established in many chemistry-related disciplines. Recent advances in quantum machine learning (QML) have enabled the prediction of QM-properties at a fraction of the cost of first-principle methods such as density functional theory (DFT). However, previous work has generally been focused on molecular systems of limited size and atom type diversity, hindering its application to adjacent fields such as drug discovery. The limited availability of open-source, high-performance models has further rendered the adoption of this progress to new fields challenging. We introduce two contributions towards overcoming these issues: The QMugs data collection (Quantum Mechanical properties of drug-like molecules) provides a wide array of QM-properties for large and biologically relevant molecules, increasing the chemical space accessible to ML models. 

Speaker: Clemens Isert - https://twitter.com/clemensisert 

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