A molecular generative model of ADAM10 inhibitors by using GRU-based deep neural network and transfer learning
Sign Up to like & getrecommendations! Published in 2020 at "Chemometrics and Intelligent Laboratory Systems"
DOI: 10.1016/j.chemolab.2020.104122
Abstract: Abstract Recurrent neural network (RNN) is one of the most representative architectures in deep learning and widely adopted in many research fields especially in natural language processing. In this work, a gated-recurrent-unit (GRU)-based deep neural… read more here.
Keywords: gru based; neural network; generative model; adam10 inhibitors ... See more keywords
SSAE and GRU Based Joint Modeling for Nonlinear Distributed Parameter Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3206950
Abstract: The modeling and control issues for distributed parameter systems (DPSs) have received a great deal of attention. Because linear model order reduction (MOR) methods may ignore the nonlinear dynamics and lose some details, it is… read more here.
Keywords: gru based; distributed parameter; ssae gru; parameter systems ... See more keywords
De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach
Sign Up to like & getrecommendations! Published in 2021 at "Pharmaceuticals"
DOI: 10.3390/ph14121249
Abstract: Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore,… read more here.
Keywords: caspase inhibitors; caspase; recurrent neural; gru based ... See more keywords