  Graph classification is an important area in both modern research and
industry. Multiple applications, especially in chemistry and novel drug
discovery, encourage rapid development of machine learning models in this area.
To keep up with the pace of new research, proper experimental design, fair
evaluation, and independent benchmarks are essential. Design of strong
baselines is an indispensable element of such works.
  In this thesis, we explore multiple approaches to graph classification. We
focus on Graph Neural Networks (GNNs), which emerged as a de facto standard
deep learning technique for graph representation learning. Classical
approaches, such as graph descriptors and molecular fingerprints, are also
addressed. We design fair evaluation experimental protocol and choose proper
datasets collection. This allows us to perform numerous experiments and
rigorously analyze modern approaches. We arrive to many conclusions, which shed
new light on performance and quality of novel algorithms.
  We investigate application of Jumping Knowledge GNN architecture to graph
classification, which proves to be an efficient tool for improving base graph
neural network architectures. Multiple improvements to baseline models are also
proposed and experimentally verified, which constitutes an important
contribution to the field of fair model comparison.
