An automated framework for QSAR model building.

ABSTRACT:

Background

In-silico quantitative structure-activity relationship (QSAR) models based tools are widely used to screen huge databases of compounds in order to determine the biological properties of chemical molecules based on their chemical structure. With the passage of time, the exponentially growing amount of synthesized and known chemicals data demands computationally efficient automated QSAR modeling tools, available to researchers that may lack extensive knowledge of machine learning modeling. Thus, a fully automated and advanced modeling platform can be an important addition to the QSAR community.

Results

In the presented workflow the process from data preparation to model building and validation has been completely automated. The most critical modeling tasks (data cur

SUBMITTER: Kausar S 

PROVIDER: S-EPMC5770354 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature