This review provides the feasible literature on drug discovery through ML tools and
techniques that are enforced in every phase of drug development to accelerate the research …

The discovery and advances of medicines may be considered as the ultimate relevant
translational science effort that adds to human invulnerability and happiness. But advancing …

Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …

Many of the existing machine learning algorithms, both supervised and unsupervised,
depend on the quality of the input characteristics to generate a good model. The amount of …

The concept of autoencoder was originally proposed by LeCun in 1987, early works on
autoencoder were used for dimensionality reduction or feature learning. Recently, with the …

We investigate the non-identifiability issues associated with bidirectional adversarial training
for joint distribution matching. Within a framework of conditional entropy, we propose both …

Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where
there is a single, labeled, source and a single target domain. However, in many real-world …

Thanks to the fast improvement of the computing power and the rapid development of the
computational chemistry and biology, the computer-aided drug design techniques have …

Generative adversarial networks (GANs) have achieved significant success in generating
real-valued data. However, the discrete nature of text hinders the application of GAN to text …

Abstract A Triangle Generative Adversarial Network ($\Delta $-GAN) is developed for semi-
supervised cross-domain joint distribution matching, where the training data consists of …