Advancing Drug Discovery and Material Science Using Quantum Simulations for Molecular Structures

Quantum simulations are changing the way drugs are found and materials are studied because they can model chemical structures and interactions with a new level of accuracy. This study looks at how to use quantum computing techniques, like Variational Quantum Eigensolvers (VQE) and Quantum Approximate Optimisation Algorithms (QAOA), to make traditional methods better at modelling complex chemical systems. Using quantum physics, these models make it possible to accurately predict molecule traits, reaction paths, and energy states. This speeds up the creation of new drugs and materials. Quantum models help find lead chemicals in drug development by correctly modelling how proteins and ligands interact, how strongly they bind, and how reactions happen. In material science, they make it possible to create new materials with specific qualities, like high-performance catalysts, ways to store energy, and long-lasting materials that can be used in industry. Quantum computing and artificial intelligence work well together to make these models more accurate and scalable, which means they can be used on a big scale. This study also looks at problems like quantum technology limits, fixing mistakes, and making algorithms work better. It suggests combining quantum and traditional methods to make computing more efficient. Case studies show that quantum models can be used to solve real-world problems, such as creating focused medicines and materials that are good for the environment. The results show that quantum models have the power to change everything and speed up creativity in many fields. This study shows how quantum computing can change the future of molecular science by connecting theory and practice. It could lead to big steps forward in drug development and material design that help solve important world problems.