DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance.

ABSTRACT: The progesterone receptor (PR) is important therapeutic target for many malignancies and endocrine disorders due to its role in controlling ovulation and pregnancy via the reproductive cycle. Therefore, the modulation of PR activity using its agonists and antagonists is receiving increasing interest as novel treatment strategy. However, clinical trials using the PR modulators have not yet been found conclusive evidences. Recently, increasing evidence from several fields shows that the classification of chemical compounds, including agonists and antagonists, can be done with recent improvements in deep learning (DL) using deep neural network. Therefore, we recently proposed a novel DL-based quantitative structure-activity relationship (QSAR) strategy using transfer learning to build predict

SUBMITTER: Matsuzaka Y 

PROVIDER: S-EPMC6987043 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature