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The drug-discovery process is a trial-and-error run and time-consuming. Currently, using in silico tools to find a molecule that binds to the target protein has made computational chemistry a valuable tool in drug discovery. However, common empirical methods are difficult to accurately and effectively study complex protein-ligand interactions. In the computer-aided drug design (CADD) process, quantum chemistry can significantly reduce the time and cost required for compound discovery and optimization. Moreover, it could assist in overcoming the scaling restrictions of conventional computational techniques and enable numerically precise solutions for larger and more complex molecular systems. The pharmaceutical industry has a tremendous amount of potential with quantum computing, which is a fundamentally different computing approach based on the laws of quantum mechanics. Quantum computing enables certain computations to be performed much more quickly and efficiently than traditional computing does. Creative Biolabs has set up quantum task forces and invested significant sums to explore the application of quantum computers and software to chemistry and biology.
Fig.1 Implementation of quantum mechanics in pharmaceutical companies’ drug design workflow. (Arodola & Soliman, 2017)
Molecular structure computations span a wide range of issues, including conformational analysis, studies of scattering phenomena, intermolecular energy surfaces, the kinetics of chemical reactions, and the ground state of systems. With the development of computer science and quantum chemistry, a variety of molecular structure calculation tools have emerged. The structure, characteristics, and behavior of molecules are expected to be more accurately predicted and simulated by quantum chemical computation than by traditional computing. Theoretically, quantum computers can effectively simulate the complete problem, including atomic-scale interactions. Furthermore, applying machine learning (ML) approaches for quantum chemistry simulation is exhibiting the ability to make simulation substantially less computationally demanding with sufficient accuracy for chemical applications. Calculating stable structural conformations and creating coarse-grained models for molecular dynamics simulations are two examples of uses for machine learning. This technology has the potential to considerably advance synthetic molecular design, leading to impressive advancements in chemical research.
Fig.2 Three-dimensional graph of the HOMO and LUMO orbitals of the dibromo-naphthalene molecule. (Sertbakan & Özçelik, 2022)
As a system gets bigger, it gets harder to predict chemical and physical attributes like molecular energies or reaction rates. Using a controlled quantum machine to simulate the unidentified process, quantum computers promise to find a natural solution to the chemical simulation dilemma. Quantum chemical simulations of molecular properties are essential for insights into a wide range of chemical and biological phenomena. Computational modeling of electronic excitations is essential for expanding, enhancing, and supporting experimental results. Current CADD methods may be improved by quantum chemistry since it can more precisely anticipate molecule characteristics. This could affect development in several ways, such as the modeling of protein folding and interactions between drug candidates and physiologically significant proteins. Here, quantum chemical calculations may enable researchers to simultaneously assess computational libraries against a variety of potential target structures.
Chemical reaction mechanisms are networks of short- or long-lived intermediates represented by molecular structures and linked by transition structures. As the heart of any chemical process, the reaction mechanisms require the identification of all relevant stable intermediates and transition states. Therefore, extremely precise electronic structure approaches are needed for the thorough comprehension and prediction of complicated reaction pathways. With the rapid development of quantum technology in recent years, the computing power of quantum devices is close to the threshold that can exceed the classical supercomputer. Quantum computing can be used to elucidate reaction mechanisms in complex chemical systems. Usually, the reaction mechanism of chemical reactions is unknown. By using simulations and experiments together, as well as trial and error, synthesis may be effectively enhanced. The comprehension of the synthesis is considerably improved by reaction mechanism analysis, allowing for knowledge-based conversion and selectivity improvement.
Fig.3 Hardware architectures for quantum computers. (Reiher, et al., 2017)
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