Book

This book offers a comprehensive analysis of advanced computational methodologies viz. Multiwfn, ONIOM and Molecular Docking, which have become an essential tool in modern theoretical chemistry research. Multiwfn, an advanced wavefunction analysis software packages enhance the understanding of electronic structures by providing detailed quantitative and visual analysis of molecular orbitals, electron density distributions, and intermolecular interactions. This tool excels in performing various real-space function calculations, such as electron localization and electrostatic potential mapping, which are crucial for studying complex quantum interactions and analysing molecular behaviour across different chemical environments. A hybrid QM/MM framework i.e., ONIOM method enables the efficient analysis of complex molecular systems by dividing them into different layers of atoms. This multiscale modeling approach optimizes computational resources by applying high-level QM calculations to the bio-active regions while relying on more cost-effective MM for the remaining sites. ONIOM is particularly valuable in predicting chemical reactivity, catalysis, and biological interactions with great accuracy. Molecular docking, a key technique in structure-based drug design, precisely predicts ligand-receptor interactions and aids in the discovery and optimization of lead compounds. The molecular docking simulation holds promise for a deeper understanding of ligand-protein binding mechanism; while challenges remain in addressing their protein flexibility for accurately predicting binding affinities. The synergy among Multiwfn’s wavefunction analysis, ONIOM’s multiscale modeling and molecular docking’s role in drug discovery creates a robust framework for studying molecular interactions. This book highlights their collective contribution in biomolecular interactions and material science, expanding the horizons of applied computational chemistry to address the complex challenges in chemical and biological systems, and their potential for driving future innovations.