Artificial intelligence (AI) is a key component in the development of novel medications, and artificial neural networks, including deep neural networks and recurrent neural networks, are the main driving force behind this research. In recent years, applications for predicting molecular properties or activities, such as physicochemical features and ADMET traits, have increased substantially. These applications rely on quantitative structure–property relationships (QSPR) and quantitative structure–activity relationships (QSAR), which further strengthen the versatility of this technology. In de novo drug design, artificial intelligence guides the synthesis of novel, physiologically active, and functional molecules toward specific desired properties. Several examples have demonstrated the success of artificial intelligence in this field. Through synthesis planning and improved ease of synthesis, AI enables the integration of synthesis with drug discovery, and consequently, computer-assisted drug development is expected to expand in the near future. However, the pharmaceutical industry is currently facing challenges in sustaining drug development efforts due to reduced productivity and increasing research and development expenditures. Therefore, this article examines the main factors influencing attrition rates in new drug approval, explores possible solutions, reviews different types of AI-based software aimed at enhancing the effectiveness of the drug research process, and discusses partnerships between AI-driven pharmaceutical companies and major pharmaceutical industry leaders.