QUANTUM CHEMICAL METHODS AND MULTISCALE MODELING IN COMPUTER-AIDED DRUG DESIGN

Computer-Aided Drug Design (CADD) leverages a diverse toolkit of computational methods to accelerate the discovery and development of novel therapeutics. Among these, quantum chemical calculations provide unparalleled accuracy in understanding molecular interactions, albeit at a higher computational cost. This accuracy is crucial for identifying and quantifying fundamental interactions that dictate drug efficacy and selectivity, as exemplified by our investigation of ruthenium polypyridyl complexes. These metal-based compounds are model systems for studying covalent coordination bonds between ruthenium and its ligands and noncovalent interactions with DNA and protein targets. Local Mode Vibrational Theory emerges as a powerful lens within this framework, enabling the assessment of drug candidates by probing their intrinsic stability and interaction strengths with intended targets. This analysis utilizes local mode force constants and their correlations with other chemical properties to provide a detailed picture of molecular behavior. Deep learning models significantly enhance this framework by accelerating conformational sampling in protein dynamics simulations, enabling more efficient exploration of the vast conformational landscape. Furthermore, introducing mechanical forces through mechanochemistry offers a unique avenue for influencing chemical reactions, potentially leading to more efficient and controlled synthesis of drug molecules. By integrating quantum chemical calculations, Local Mode Vibrational Theory, mechanochemical simulations, and data-driven approaches like machine learning, we aim to establish a comprehensive and multi-scale theoretical framework for drug discovery. This approach, encompassing everything from the quantum interactions of drug molecules to the dynamics of protein targets, paves the way for designing more effective and targeted therapies.