A drug-like-molecule library can contain 1023–1060 molecules, among which only approximately 1012 molecules may be synthesized in labs. However, it is still challenging for researchers to find the most promising candidates among the vast number of synthesizable compounds in a reasonable time. Moreover, although molecules are picked for their predicted bioactivities, their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are often difficult to predict and modify. This is often a bottleneck for downstream studies and applications. It would be more productive if candidate molecules are generated, rather than screened from libraries, with suitable ADMET properties as prerequisites at the beginning of the molecule design process. Recently, artificial intelligence (AI)-based generative models have been described for designing drug candidates using prior biological and chemical knowledge. A spectacular example was the use of a combination of AI generative techniques and reinforcement learning by the biotechnology company, Insilico Medicine, to successfully create new DDR1 kinase inhibitors to treat fibrosis in only 21 days. We will describe how reinforcement learning (RL) algorithms can be applied to generative AI for better real-world effectiveness while better utilizing modern distributed hardware assets. In this chapter, we will review simple and advanced AI generative models and discuss the advantages and disadvantages of each model.