CASTELO: clustered atom subtypes aided lead optimization—a combined machine learning and molecular modeling method

CASTELO: clustered atom subtypes aided lead optimization—a combined machine learning and molecular modeling method

Background: Drug discovery is a multi-stage process that comprises two costly major steps: 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...

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Bibliographic Details
Main Authors: Cong, G. (Author), Domeniconi, G. (Author), Kang, S.-G (Author), Yang, C.-C (Author), Zhang, L. (Author), Zhou, R. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Based clustering
Budget control
Clinical research
cluster analysis
Cluster Analysis
Clustering
drug development
Drug discovery
Drug Discovery
Drug modification
Lead optimization
machine learning
Machine learning
Machine Learning
Matrix algebra
molecular dynamics
Molecular dynamics
Molecular dynamics simulation
Molecular Dynamics Simulation
Molecular dynamics simulations
Molecular modeling
Multistage process
Outsourcing
Structure activity
Temporal dynamism
Temporal information
Variational autoencoder
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