CASTELO: clustered atom subtypes aided lead optimization-a combined machine learning and molecular modeling method.
BMC Bioinformatics
; 22(1): 338, 2021 Jun 22.
Article
in En
| MEDLINE
| ID: mdl-34157976
ABSTRACT
BACKGROUND:
Drug discovery is a multi-stage process that comprises two costly majorsteps:
pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots.RESULTS:
The initial data collection is achieved with physics-based molecular dynamics simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization.CONCLUSION:
With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Molecular Dynamics Simulation
/
Machine Learning
Language:
En
Journal:
BMC Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2021
Type:
Article
Affiliation country:
United States