De novo drug design offers several benefits, such as the ability to explore a broader range of chemical possibilities, the creation of compounds that represent innovative intellectual property, the possibility of developing new and enhanced therapies, and the efficient development of drug candidates in terms of cost and time. One of the primary obstacles encountered in de novo drug design is the synthetic inaccessibility of the molecular structures produced ( ). Although de novo drug design benefits, it has limitations encompass several desired properties or chemical characteristics, such as a predetermined range of solubility, toxicity below a certain threshold, and the inclusion of specified chemical groups in the structure ( ). Fortunately, machine learning applications in De Novo Drug discovery have the potential to overcome limitations such as computational intensity and limited performance ( ). Their more considerable computing cost limits QM-based simulations to smaller systems. While more efficient, MM-based methods sometimes derive from experimental data and depend on various approximations. QM-based machine learning aims to improve the efficacy of QM techniques while keeping their capacity to be applied to various scenarios, precisely anticipate outcomes, and adequately explain complex bonding patterns, including the formation and breaking of chemical bonds ( ).