*4.3.2 Predicting drug-protein interactions*

Quantum mechanics (QM) is a very effective tool for predicting protein–ligand (drug) interactions [52, 53]. These methods takes the help of quantum effects for the simulated system at the atomic resolution, resulting in substantially higher precision than conventional MM methods. The time–cost of QM-based methods is far higher than that of MM methods since MM methods only use basic energy functions based on atomic coordinates [54, 55]. As a result, applying AI methods to QM calculations necessitates a tradeoff between QM accuracy and molecular mechanics(MM) models' favorable time–cost [56]. AI models have been trained to replicate QM energies from atomic coordinates, and they can outperform MM methods in terms of calculation speed. Deep learning can be used to predict the potential energies of small molecules, thus replacing computationally challenging quantum chemistry calculations with a fast ML method [56]. AI is mainly utilized for atomic simulations and predictions of electrical properties, while DL has been utilized to predict the potential energies of small molecules, thus replacing computationally demanding quantum chemistry calculations with a fast ML method. DFT (density functional theory) potential energies derived from quantum chemistry have been measured and used to train DNNs for large data sets. For example, the accuracy of an ML model improved with increasing sample size in a study of two million elpasolite crystals, reaching 0.1 eV/atom for DFT formation energies trained on 10,000 structures. The model was then used to test different compositional choices for different properties (**Table 2**) [57].
