Generating Bioactive Natural Product-inspired Molecules with Machine Intelligence
DOI:
https://doi.org/10.2533/chimia.2022.396PMID:
38069710Keywords:
Artificial intelligence, Drug discovery, Machine learning, Natural productAbstract
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.Funding data
-
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Grant numbers 205321_182176
Downloads
Published
2022-05-25
Issue
Section
Scientific Articles
License
Copyright (c) 2022 Petra Schneider, Karl-Heinz Altmann, Gisbert Schneider
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
[1]
P. Schneider, K.-H. Altmann, G. Schneider, Chimia 2022, 76, 396, DOI: 10.2533/chimia.2022.396.