Generating Bioactive Natural Product-inspired Molecules with Machine Intelligence

Abstract

The computer-assisted design of new chemical entities has made a leap forward with the development of machine learning models for automated molecule generation. The overarching goal of this conceptual approach is to augment the creativity of medicinal chemists with a machine intelligence. In this Perspective we highlight prospective applications of “de novo” drug design and target prediction, aiming to generate natural product-inspired bioactive compounds from scratch. A virtual chemist transforms pharmacologically active natural products into new, easily synthesizable small molecules with desired properties and activity. Computational activity prediction and automated compound generation offer the possibility to systematically transfer the wealth of pharmaceutically active natural products to synthetic small molecule drug discovery. We present selected prospective examples and dare a forecast into the future of natural product-inspired drug discovery.