## 4 Results and Discussion  
Pharmaceutical industries are witnessing a major transformation with the introduction of machine learning and deep learning techniques. While many see this new technology as a potential threat, it could be the solution to our persistent pharmaceutical deficits. Indeed,AI has already proven to be useful in several aspects of pharmaceutical research, from assisting researchers to find new possible treatments, to predicting which pharmaceuticals will fail clinical testing. There is really no doubt that these innovations will have a profound impact on the development of drugs, as more pharmaceutical firms embrace them. Drug development is a time-consuming and expensive venture. Moreover, it is a gradual procedure, that starts with the discovery of a promising molecule, and finishes with the creation of a new molecular entity. This research is important to pharmaceutical companies, particularly in the production of liquid medications and drugs for medicinal purposes, because it aids in the discovery of an effective particle, whose end purpose is to affect the human body and demonstrate its effectiveness, pureness, and relevance in medical therapy. The preceding conditions ensure that the new pharmaceuticals that are approved by the authorities improve patients' wellbeing, not only through curing their disease but also through guaranteeing that the treatment does not cause serious side effects and lead to other problems, such as potential complications. The simulation results achieved in this paper can be used to improve the quality and contents of liquid and solid drugs, by determining the molecular properties of the drugs. Both medication reactions in the body over time, and drug effects on the body or on microorganisms within or on the body, are influenced by physico-chemical and molecular characteristics. This could also address challenging issues around medication safety. In this regard, the current tendency in drug development is to take absorption, distribution, metabolism, excretion, and toxicity (ADMET) qualities into account alongside target affinity. To help medicinal chemists prioritize drug ideas, the idea of \"drug-likeness\" establishes acceptable limitations of fundamental features stated as a set of rules. Liquid drug makers investigating the treatments for illnesses such as Alzheimer's, cancer, and other challenging conditions are interested in learning about new targets that could be the primary goal of a new drug development initiative. As an algorithm-based strategy, deep learning incorporates numerous mathematical and computational ideas. It can analyze vast volumes of data and has unnaveering coordination and matching powers. It can be used to enhance drug discovery tasks such as target recognition, drug design, and drug repurposing. Instead of rendering scientists' jobs obsolete, it serves a crucial role in developing their talents. This is due to the need for subject matter experts in the data structuring for AI analysis, as well as benefitting the review process and end-to-end result confirmation. Conventional drug development can only analyze a small number of tests or data at a time, increasing the risk of prejudice. The performance of the recurrent neural network (RNN), long short term memory (LSTM), gated recurrent unit (GRU), one-dimensional convolutional neural network (1D-CNN), and the proposed 1D convolutional gated recurrent unit neural network (1D-CNN-GRU) molecular property systems is explained in this section. Table 3 shows the summary statistics for each of the columns of molecular properties. The mean, standard deviation, minimum, 25%, 50%, 75%, and maximum are analyzed. The mean of atom_index_1 is small compared to other molecular properties. Atom_index_1 also has a small volatility of 3.27 compared with other molecular properties, which indicating that the atom index fluctuates slowly and tends to be more stable, while Id has a maximum value according to Table. The minimum value of the molecular property dataset is within the range of 0 to \(-\)44.