Preface

The rapid advancement of computational technologies has expanded the scope of computer-aided drug design (CADD) far beyond its traditional applications. Increasing computational power, sophisticated algorithms, extensive molecular databases, and the emergence of artificial intelligence have collectively transformed the strategies employed in modern drug discovery. These developments have enabled researchers not only to predict molecular properties and investigate drug-target interactions but also to design novel therapeutic candidates with greater efficiency and precision.

This volume, Computer-Aided Drug Design: Advanced Techniques and Emerging Applications, focuses on the modern methodologies that are shaping the future of pharmaceutical research. Building upon foundational concepts, the chapters presented herein explore innovative computational approaches that address current challenges in drug discovery and development. Emphasis is placed on the integration of advanced modeling techniques with practical applications that accelerate therapeutic innovation.

Among the major themes of this volume are de novo drug design and virtual screening, both of which exemplify the capacity of computational tools to identify and optimize promising candidates from vast chemical spaces. Pharmacophore mapping is presented as an important strategy for defining essential molecular features associated with biological activity, thereby facilitating lead identification and refinement. The application of computational methods in drug repurposing further illustrates how existing therapeutic agents can be systematically evaluated for novel clinical indications. The transformative role of artificial intelligence and machine learning in CADD constitutes another important focus of this volume. The discussions highlight the incorporation of deep learning methodologies, reinforcement learning strategies, and advanced analytical approaches into drug discovery workflows. These developments demonstrate the potential of emerging technologies to enhance predictive performance and support the design of innovative molecular entities.

Dedicated chapters also address molecular dynamics simulations as powerful tools for investigating biomolecular flexibility, conformational changes, and the thermodynamic factors governing drug–target interactions. Through these approaches, readers gain insight into the dynamic nature of biological systems and the mechanisms that influence therapeutic efficacy. To strengthen practical understanding, the volume incorporates tutorials and software-oriented exercises that encourage the direct application of theoretical concepts within academic and research environments.

This volume reflects the collaborative contributions of experts representing diverse areas of pharmaceutical sciences, computational chemistry, and bioinformatics. Their collective knowledge has resulted in a text that combines advanced methodologies with real-world perspectives on their implementation in drug discovery. It is intended to support the needs of researchers, educators, and graduate students seeking to understand the expanding applications of computational approaches in the pharmaceutical sciences.

As computer-aided methodologies continue to evolve, their influence on the discovery and development of medicines will undoubtedly increase. It is our sincere hope that this volume will not only provide readers with an overview of current advances but also inspire further innovation in the pursuit of safer, more targeted, and more effective therapeutic interventions.