Drug Discovery Approaches using Quantum Machine Learning
This addresses the problem of inefficient drug discovery for pharmaceutical researchers, but it appears incremental as it applies existing quantum methods to this domain without new breakthroughs.
The paper tackles the high cost and time of traditional drug discovery by proposing quantum machine learning techniques, including GANs, CNNs, and VAEs, to generate small and large drug molecules and classify protein binding pockets, though no concrete results or numbers are provided.
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.