Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches.

ABSTRACT: As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)populations. Traditional experimental testing regimens to identify toxicants that induce endocrine disruption can be expensive and time-consuming. Computational modeling has emerged as a promising and cost-effective alternative method for screening and prioritizing potentially endocrine-active compounds. The efficient identification of suitable chemical descriptors and machine-learning algorithms, including deep learning, is a considerable challenge for computational toxicology studies. Here, we sought to apply classic machine-learning algorithms and

SUBMITTER: Ciallella HL 

PROVIDER: S-EPMC7873171 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature