  Deep Learning and DRUG-seq (Digital RNA with perturbation of genes) have
attracted attention in drug discovery. However, the public DRUG-seq dataset is
too small to be used for directly training a deep learning neural network from
scratch. Inspired by the transfer learning technique, we pretrain a drug
efficacy prediction neural network model with the Library of Integrated
Network-based Cell-Signature (LINCS) L1000 data and then use human neural cell
DRUG-seq data to fine-tune it. After training, the model is used for virtual
screening to find potential drugs for Alzheimer's disease (AD) treatment.
Finally, we find 27 potential drugs for AD treatment including Irsogladine
(PDE4 inhibitor), Tasquinimod (HDAC4 selective inhibitor), Suprofen (dual
COX-1/COX-2 inhibitor) et al.
