Future success in pharmaceutical research will fundamentally rely on the combination of advanced synthetic and analytical technologies that are embedded in a theoretical framework that provides a rationale for the interplay between chemical structure and biological effect. A driving role in this setting falls on leading edge concepts in computer-assisted molecular design and machine learning, by providing access to a virtually infinite source of novel tool compounds and lead structures, and guiding experimental screening campaigns. We will discuss representations of molecular structure, predictive models of structure-activity relationships using constructive machine learning, automated molecular de novo design, and showcase prospective applications. Emphasis will be put on the automated construction of potent and selective new chemical entities. As we are currently witnessing strong renewed interest in bioactive natural products we will present applications of this approach to natural-product inspired molecular design.
Gisbert Schneider (ETH)
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