A5101518428- Computational Drug Discovery Methods
- Protein Structure and Dynamics
- Chemical Synthesis and Analysis
- Enzyme Structure and Function
- Lung Cancer Treatments and Mutations
- Analytical Chemistry and Chromatography
- Bioinformatics and Genomic Networks
- Cancer-related gene regulation
- Pharmacological Effects of Natural Compounds
- Plant-based Medicinal Research
- Advanced Graph Neural Networks
- Pharmacogenetics and Drug Metabolism
- Machine Learning in Bioinformatics
- Machine Learning in Materials Science
China University of Petroleum, East China
2020-2021
Nanfang Hospital
2014
Southern Medical University
2014
Deep learning methods, which can predict the binding affinity of a drug–target protein interaction, reduce time and cost drug discovery. In this study, we propose novel deep convolutional neural network called SE-OnionNet, with two squeeze-and-excitation (SE) modules, to computationally protein–ligand complex. The OnionNet is used extract feature map from three-dimensional structure protein–drug molecular SE module added second third layers improve non-linear expression model performance....
Background: Drug development requires a lot of money and time, the outcome challenge is unknown. So, there an urgent need for researchers to find new approach that can reduce costs. Therefore, identification drug-target interactions (DTIs) has been critical step in early stages drug discovery. These computational methods aim narrow search space novel DTIs elucidate functional background drugs. Most developed so far use binary classification predict presence or absence between target....
In computational drug discovery, accurately predicting drug-target interaction (DTI) is vital for repositioning and developing new drugs. With DTI data rapidly accumulated in recent years, it recently hot to use deep learning technology predict DTIs, but still a challenge design light frameworks by using less protein descriptors. this work, address the challenge, novel convolutional neural network (namely LDCNN) proposed which small number of descriptors are produced convolving amino acid...
Traditional Chinese medicine has been used to treat and prevent infectious diseases for thousands of years, accumulated a large number effective prescriptions. Deep learning methods provide powerful applications in calculating interactions between drugs targets. In this study, we try use the method deep reposition molecules medicines (CMs) targets syndrome coronavirus 2 (SARS-CoV-2). A convolution neural network with residual module (DCNN-Res) is constructed trained on KIBA dataset. The...
Human papillomavirus (HPV) infection is linked to several diseases, the most prominent of which are cervical cancer and genital condyloma acuminatum. PI3K-Akt-mTOR signaling pathway one important in regulation proliferation, differentiation apoptosis human cells, this has potential become a novel target for development therapeutics. Previous studies have suggested that drug therapy can modulate PI3K-AKT-mTOR reduce HPV viral load effectively through autophagy apoptosis. Therefore, our study,...