  Designing new chemical compounds with desired pharmaceutical properties is a
challenging task and takes years of development and testing. Still, a majority
of new drugs fail to prove efficient. Recent success of deep generative
modeling holds promises of generation and optimization of new molecules. In
this review paper, we provide an overview of the current generative models, and
describe necessary biological and chemical terminology, including molecular
representations needed to understand the field of drug design and drug
response. We present commonly used chemical and biological databases, and tools
for generative modeling. Finally, we summarize the current state of generative
modeling for drug design and drug response prediction, highlighting the
state-of-art approaches and limitations the field is currently facing.
