  Traditional drug discovery pipeline takes several years and cost billions of
dollars. Deep generative and predictive models are widely adopted to assist in
drug development. Classical machines cannot efficiently produce atypical
patterns of quantum computers which might improve the training quality of
learning tasks. We propose a suite of quantum machine learning techniques e.g.,
generative adversarial network (GAN), convolutional neural network (CNN) and
variational auto-encoder (VAE) to generate small drug molecules, classify
binding pockets in proteins, and generate large drug molecules, respectively.
