The study aims to evaluate the potential of Diclofenac derivatives as new drugs. For this purpose, the quantum mechanics and artificial intelligence methods are mixed. Using semi empirical PM3 implemented in HyperChem software, we developed and analyzed the properties of several Diclofenac derivatives. First and foremost, the physical properties, such as total energy, dipole moment, ionization energy, and electron affinity were calculated. The results were then compared with those of the Diclofenac molecule. It was discovered that a derivative with the –CH3 group shows identical stability and polarity to the original drug. It means that the derivative has similar therapeutic properties, whereas the pharmacokinetic properties may vary. The –Br derivative shows the highest stability, meaning long-lasting character. However, the –F derivative, despite the high degree of stability, is not reactive enough. The –O derivative also requires modification for safety reasons. This work reveals the importance and significance of mixing quantum mechanics and artificial intelligence. It provides a model for estimating the efficacy and safety of a new drug, discovered through quantum mechanics. The work can be viewed in the context of the existing literature on the issue of computational drug design that advocates mixing the new technology with the old one.