The accurate prediction of molecular properties is a critical ingredient toward the societal and technological progress since it could speed up much research progress, such as drug designing and substance discovery. Also, it would cause more initiatives towards a personalized medicine. However, complete exploring “chemical universes” that potentially include infinite sets of compounds seems to be computationally intractable. Obviously, researchers and pharmaceutical industries around the world have brought various methodologies to explore the chemical space that can be categorized into two groups, i.e., in silico and in vitro that includes combinatorial libraries, or high throughput screening. In recent years, advances in the development of deep learning models have spawned a mass of promising methods to address the molecular property prediction task. During our presentation we will introduce the related background and share the experiences connected with implementing one of the models that learns and predicts molecular properties on unseen data. Furthermore, we will also provide the results and compare them with the state-of-the-art results from other works.