Quantum algorithms which were derived from quantum mechanics principles are further revolutionizing this process with their potential for detailed simulation of molecular interactions. Highly notable observations can be made by delving into quantum chemistry and molecular dynamics, which facilitates the identification of suitable candidates for drug synthesis. This capability enables the identification of potential drug candidates with greater efficiency and accuracy. The critical challenges that must be addressed to realize the potential of quantum computing in drug discovery are suppression of noise due to the susceptibility of quantum structures, enhancing the scalability of tests, and design optimization for complex algorithms. These requirements necessitate the design and implementation of improved quantum algorithms with guaranteed positive computational consistency. This will empower precise simulations of molecular interactions, establishing the groundwork for more sophisticated, world-class, and selective drug discovery methodologies. The proposed quantum algorithm model was tested with PubChem, BindingDB, Tox21, and Maximum Unbiased Validation (MUV) datasets. The performance was compared to existing machine learning algorithms in terms of accuracy, precision, recall, F1 score, qubit fidelity, quantum volume, measurement time, error rate, success rate, scalability, variational quantum eigensolver convergence, entanglement entropy, resource requirements, chemical accuracy, and parallelism, which improved outcomes in most of the parameters. The experimental study shows the transformative potential of integrating quantum algorithms in the pharmaceutical industry, paving the way for the development of more effective and targeted therapeutic solutions.
