| Literature DB >> 33118816 |
Thomas Blaschke1, Josep Arús-Pous1,2, Hongming Chen3, Christian Margreitter1, Christian Tyrchan4, Ola Engkvist1, Kostas Papadopoulos1, Atanas Patronov1.
Abstract
In the past few years, we have witnessed a renaissance of the field of molecular de novo drug design. The advancements in deep learning and artificial intelligence (AI) have triggered an avalanche of ideas on how to translate such techniques to a variety of domains including the field of drug design. A range of architectures have been devised to find the optimal way of generating chemical compounds by using either graph- or string (SMILES)-based representations. With this application note, we aim to offer the community a production-ready tool for de novo design, called REINVENT. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. It can facilitate the idea generation process by bringing to the researcher's attention the most promising compounds. REINVENT's code is publicly available at https://github.com/MolecularAI/Reinvent.Year: 2020 PMID: 33118816 DOI: 10.1021/acs.jcim.0c00915
Source DB: PubMed Journal: J Chem Inf Model ISSN: 1549-9596 Impact factor: 4.956