title: Deep learning based approach for prevention of Alzheimer’s disease

Aman Chandra Kaushik (TALK)

institution: School of life Sciences and Biotechnology, Shanghai Jiao Tong University


Aman Chandra Kaushik, Dong-Qing Wei*

State Key Laboratory of Microbial Metabolism and School of life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China


After long-established investigations amyloid-beta (Aβ) is reported to be the high profile risk factor associated with the onset and progression of Alzheimer’s disease (AD). Accumulation of extracellular senile plaques facilitated by amyloid-beta (Aβ), synaptic degeneration and intracellular neurofibrillary tangles (NFT) are the three most important features linked with this disease. In this paper we proposed new screening technique to screen targets on the basis of their fingerprint. Optimized deep neural network that operates directly on compounds structure; this approach allow one point of structure to other end learning of prediction. Proposed deep neural network algorithm were applied for training of chemical compounds library; obtained wgx-50 with (GOLD, SPION and MN) as a target compound against Amloyid Beta, and then further molecular docking, systems biology and time course simulation were done for wgx-50 with GOLD, SPION and Mn to validate these obtained results for Alzheimer’s disease.

KEYWORDS: Alzheimer’s disease, Neurodegenerative disorder, β-amyloid, Deep Learning, wgx-50, Nanoparticle.