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This book builds on the foundation laid by the 2020 Springer volume Quantum Mechanics in Drug Discovery. Whereas that earlier work provided a broad overview of the role of quantum mechanics (QM) in pharmaceutical research, the present volume focuses specifically on practical applications of QM methods for designing new drug candidates and for supporting decision-making in modern drug design. Over the past decade, computational chemistry has become an integral component of contemporary drug design pipelines. Analyses of published projects suggest that programs supported by computational methods are approximately 16–20% more likely to succeed and may require 30–40% fewer compounds during hit-to-lead and lead-optimization stages compared with projects that do not systematically employ such approaches. These trends highlight the growing importance of physics-based modeling as a key enabler of efficiency, mechanistic understanding, and innovation in medicinal chemistry. The adoption of QM in drug design has expanded significantly in recent years, although it is not yet fully mainstream across the industry. This expansion has been driven by rapid advances in high-performance computing (HPC), cloud infrastructure, and workflow automation, together with increasing integration of QM with artificial intelligence (AI) and machine learning (ML). These developments are reshaping computational drug design, enabling researchers to address increasingly complex biological systems and chemical modalities with improved accuracy and predictive power. QM methods contribute to drug design in multiple complementary ways. They enable detailed characterization of protein–ligand, protein–water, and protein–protein interactions; support structure-based drug design (SBDD); enhance molecular dynamics simulations; provide mechanistic rationalization of structure–activity relationships; and serve as high-accuracy scoring approaches in docking and virtual screening. Among the available methods, the Fragment Molecular Orbital (FMO) approach has emerged as a leading QM framework for structure-based design. Over the past decade, major technical challenges—including fragmentation strategies, treatment of solvation and polarization, geometry optimization, and computational speed—have been addressed. As a result, FMO has evolved into increasingly automated and user-friendly platforms capable of supporting high-throughput workflows and industrial drug design applications. FMO and related QM methodologies are now routinely applied to protein–small molecule systems and are rapidly extending to emerging therapeutic modalities, including proteolysis-targeting chimeras (PROTACs), protein–protein interaction (PPI) modulators, molecular glue degraders, antibody–drug conjugates (ADCs), peptides and peptidomimetics, and nucleic-acid-targeting therapeutics. Beyond these applications, QM approaches support the design of enzyme inhibitors, the interpretation of mutagenesis data, and the refinement of ligand binding hypotheses. In predictive toxicology and pharmacokinetics, QM methods contribute to ADMET modeling, including evaluation of metabolic pathways, reactivity, and potential liabilities such as hERG-related cardiotoxicity. QM also plays an important role in understanding the contribution of water molecules to ligand binding and residence time, clarifying GPCR activation mechanisms, and analyzing conformational preferences of drug candidates. This volume brings together contributions from leading researchers across academia and industry to provide a comprehensive and practical overview of QM methods in drug design. The chapters cover methodological developments, integration with molecular dynamics and AI, applications to diverse target classes and modalities, and perspectives on automation and high-throughput deployment. The book is intended for a broad readership, including computational and medicinal chemists, structural biologists, pharmacologists, and researchers working at the interface of physics, chemistry, and drug design. Our goal is to provide both an authoritative reference and a practical guide for scientists seeking to harness quantum mechanical methods to accelerate and improve the design of new therapeutics.