Computational Drug Design and Molecular Docking-Based Screening of Novel Lead Compounds for Therapeutic Applications: A Comprehensive Review

Abstract

Abstract Computer-Aided Drug Design (CADD) has drastically reshaped the paradigm of modern biopharmaceutical research, drastically decreasing the temporal and financial burdens historically associated with traditional wet-lab high throughput screening. Molecular docking-based virtual screening serves as a cornerstone of CADD, enabling the rapid computational identification, ranking, and mechanistic interrogation of novel small-molecule candidate leads against target macromolecules implicated in complex human diseases. This comprehensive review systematically investigates state-of-the-art computational drug design methodologies, evaluating structure-based (SBDD) and ligand-based drug design (LBDD) paradigms with a specific focus on molecular docking force fields, scoring function mechanics (knowledge-based, empirical, physics-based, and machine learning-driven), and molecular dynamics (MD) trajectory simulations. We critically assess virtual screening cascade architectures, incorporating filter-based ADMET prediction, PAINS removal, and binding free energy calculations (MM-PBSA/GBSA) to eliminate false positives and optimize lead selectivity. Furthermore, recent translational advancements—including deep learning-enhanced docking algorithms, generative artificial intelligence for de novo scaffold generation, and quantum mechanics/molecular mechanics (QM/MM) hybrid modeling—are benchmarked. Through detailed comparative tables, empirical data visualizations, and methodological workflows, this review illuminates existing technical bottlenecks such as receptor flexibility, explicit solvent representation, and induced-fit binding dynamics, providing a clear roadmap for the future integration of computational pipelines into precision drug discovery.

Keywords: Computer-Aided Drug Design; Molecular Docking; Virtual Screening; Structure-Based Drug Design; Lead Optimization; Molecular Dynamics; Scoring Functions; ADMET Profiling.