## 1 Introduction  
Predicting molecular properties such as atomization energy is one of the most significant challenges in quantum physical chemistry [1]. Indeed, it has sparked considerable interest in important fields of chemical science, physical science, and computer science, as it accelerates technological improvements in the process of exploring materials with desirable properties, which includes pharmaceutical research with a clear objective and new material manufacturing [2]. Density functional theory (DFT) has been extensively used in the literature to predict molecular properties. It is a common belief that subatomic interactions between components (for example, molecules) cause molecular coherence and synchronization, which are strongly connected to their fundamental features [3]. Numerous subatomic particle techniques based on DFT have been devised, to describe the quantum interactions of Kohn and Sham [4]. On the other hand, DFTs are computationally expensive, since they often require specialized functions to identify particle interactions, which takes a long time. For instance, experimental data have revealed that predicting the characteristics of just one molecule with 20 atoms takes approximately an hour [5]. Predictions that emanate from a huge number of molecules in a chemical compound space can be quite cumbersome and time-consuming. Efficient methods are needed to make the prediction process easy and fast. The majority of healthcare liquid medications have chemical ingredients. Efficient approaches can aid in the prediction of a large number of molecules in the chemical components of possible liquid medications. Unlike the previously described approaches (density functional theory and subatomic particle techniques), machine learning (ML) allows for the exact and quick prediction of molecules, which can subsequently be used to filter millions of organic molecules of interest using descriptors generated from energies [6]. Many machine learning approaches, including artificial neural networks, Gaussian process regression, kernel ridge regression [6], and quantum machine learning (QML) [7], have been shown to achieve an acceptable balance of accuracy and computing cost. Other studies have demonstrated that integrating information from quantum chemistry techniques (also known as delta learning) [8] and training a model on multiple qualities (also known as transfer learning) [9] may anticipate the difference between low- and high-fidelity computations. There have also been several recent articles [10; 11; 12] on using machine learning to anticipate the energy of large organic molecules. However, the effectiveness of these approaches for estimating the energy of large organic molecules has not been well evaluated. Scientists have previously demonstrated the potential of these data-driven methods for molecular property prediction [13; 14], motivated by the impressive growth of deep learning in various fields such as pattern recognition, computational linguistics, and environmental and human science [15; 16; 17; 18; 19]. In principle, these methods depend on rule-based feature extraction (for example, a bag of atom bonds) or viewing molecules as lattice-shaped formations, such as two-dimensional images or text. Unfortunately, few of them actually consider the underlying quantum synergies of molecules, resulting in significant information leakage, and leaving the molecular property prediction puzzle largely unsolved. Regrettably, there are numerous technological and domain obstacles in the way. To start with, quantum interactions in molecules, such as attention pull, exchange aversion, and electrically charged contact, are immensely complicated, particularly in large molecules [20]. Analytical approaches to model them are difficult. Additionally, the volume of labeled molecular data is relatively constrained compared to classic tasks, such as systems that use artificial intelligence to train computers to understand and interpret visual worlds, necessitating a generalizable approach for prediction.