In this paper, a novel neural network is proposed based on quantum rotation gate and controlled- NOT gate. Both the input layer and the hide layer are quantum-inspired neurons. The input is given by qubits, and the output is the probability of qubit in the state. By employing the gradient descent method, a training algorithm is introduced. The experimental results show that this model is superior to the common BP networks in Quantum-Inspired Neural Networks with Application in silico drug-target flexibility complement methodology-design for the generation of a peptide-mimic novel pharmacoelement binding to the amino acid conserved sequences of the active loop of a Haemophilus influenzae porin P2.
Quantum-Inspired,Neural Networks;Application;rational in silico;drug-target;flexibility;complement methodology-design;generation peptide-mimic;novel pharmacoelement;binding amino acid;conserved sequences; active loop; Haemophilus influenzae porin P2.