Computer-aided drug discovery and development (CADD) has emerged as a
transformative approach in the pharmaceutical industry, revolutionizing the traditional
drug development process. This abstract provides a comprehensive overview of the
latest advancements, methodologies, and applications in CADD. The first section
outlines the fundamental principles of CADD, emphasizing its integration of
computational techniques, algorithms, and databases to expedite the identification of
potential drug candidates. Molecular modeling, virtual screening, and quantitative
structure-activity relationship (QSAR) analysis are highlighted as primary techniques
used to predict ligand-target interactions and optimize drug properties. The second
section discusses the role of machine learning (ML) and artificial intelligence (AI) in
CADD, showcasing their capability to analyze vast datasets, identify patterns, and
predict novel drug-target interactions with unparalleled accuracy. ML algorithms, such
as deep learning, have shown promising results in de novo drug design, target
identification, and toxicity prediction. In the third section, the application of CADD in
various stages of drug discovery and development is explored. From hit identification
and lead optimization to pharmacokinetic/pharmacodynamic (PK/PD) modeling and
clinical trial design, CADD tools streamline decision-making processes, reduce costs,
and accelerate the development timeline. Furthermore, this chapter addresses the
challenges and future prospects of CADD. Despite its remarkable achievements,
CADD still faces limitations, such as the accurate representation of biological systems
and the integration of multi-scale modeling approaches. Additionally, ethical
considerations regarding data privacy, intellectual property rights, and regulatory
compliance remain pivotal in the widespread adoption of CADD methodologies
Keywords: CADD, QSAR, Virtual screening.
