The development and application of Artificial Intelligence (AI) in many areas, such as drug discovery, have grown exponentially in recent years. The success in applying techniques based on deep neural networks has been made possible by increased computational power and the availability of large databases. In this context, this project aims to develop de novo methodologies supported by AI techniques and their application/adaptation in the strategic area of planning and developing new drugs and modelling against nanocarriers for therapeutic purposes. More specifically, so-called de novo techniques will be developed and applied to design novel and synthetically feasible molecules according to certain desired characteristics (e.g., biological activity against a drug target of interest, absence of toxicity, suitable pharmacokinetic parameters). In this project, the in silico planning of prototype compounds using proprietary de novo methodologies will be carried out collaboratively with research groups specialized in organic synthesis and in vitro/in vivo experiments to validate the developed techniques. The execution of this project will be of great relevance to increase the success rates in developing molecules of interest in Medicinal Chemistry and Nanotechnology through de novo methodologies. Thus, we aim to increase the impact and accelerate the research of the national and international scientific community, which already has free access to the DockThor-VS portal, the computational platform for virtual screening coupled with the Santos Dumont supercomputer (SDumont).