ChemGenerator: a web server for generating potential ligands for specific targets.
Brief Bioinform
; 22(4)2021 07 20.
Article
in En
| MEDLINE
| ID: mdl-33381797
ABSTRACT
In drug discovery, one of the most important tasks is to find novel and biologically active molecules. Given that only a tip of iceberg of drugs was founded in nearly one-century's experimental exploration, it shows great significance to use in silico methods to expand chemical database and profile drug-target linkages. In this study, a web server named ChemGenerator was proposed to generate novel activates for specific targets based on users' input. The ChemGenerator relies on an autoencoder-based algorithm of Recurrent Neural Networks with Long Short-Term Memory by training of 7 million of molecular Simplified Molecular-Input Line-Entry System as the basic model, and further develops target guided generation by transfer learning. As results, ChemGenerator gains lower loss (<0.01) than existing reference model (0.2~0.4) and shows good performance in the case of Epidermal Growth Factor Receptor. Meanwhile, ChemGenerator is now freely accessible to the public by http//smiles.tcmobile.org. In proportion to endless molecular enumeration and time-consuming expensive experiments, this work demonstrates an efficient alternative way for the first virtual screening in drug discovery.
Key words
Full text:
1
Database:
MEDLINE
Main subject:
Software
/
Neural Networks, Computer
/
Internet
/
Drug Discovery
/
Databases, Chemical
Type of study:
Prognostic_studies
Language:
En
Year:
2021
Type:
Article