Philipp Renz, Dries Van Rompaey, Jörg Kurt Wegner, Sepp Hochreiter, and Günter Klambauer

generated and reference molecules

The generated high scoring compounds show unwanted substructures.

There has been a wave of generative models for molecules triggered by advances in the field of Deep Learning. These generative models are often used to optimize chemical compounds towards particular properties or a desired biological activity. The evaluation of generative models remains challenging and suggested performance metrics or scoring functions often do not cover all relevant aspects of drug design projects. In this work, we highlight some unintended failure modes of generative models and how these evade detection by current performance metrics.
Source code and datasets are available on GitHub.

ChemRxiv, 2020-04-30.

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IARAI Authors
Dr Sepp Hochreiter
Research
Health and Well-being
Keywords
Deep Learning, Drug Discovery, Generative Models for Molecules

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