Traditional and Novel Computer-Aided Drug Design (CADD) Approaches in the Anticancer Drug Discovery Process

Area of Science:

• Oncology
• Medicinal Chemistry
• Computational Biology

Background:

• Cancer remains a leading global cause of death, with current therapies often lacking selectivity and causing significant toxicity.
• The development of targeted, innovative small molecule anticancer drugs is a critical area of research.
• In silico tools, particularly computer-aided drug design (CADD), are increasingly vital for advancing oncology drug discovery.

Approach:

• This review analyzes various computer-aided drug design (CADD) methodologies applied to anticancer drug discovery.
• A systematic literature review was conducted, selecting relevant articles published between 2008 and 2021.
• Selection criteria included information relevance and journal impact factor to ensure high-quality data.

Key Points:

• Traditional and novel CADD approaches are crucial for identifying and optimizing anticancer drug candidates.
• Sub-segments of CADD, including molecular modeling, virtual screening, and QSAR, are essential tools in oncology research.
• Numerous successful applications of CADD in anticancer drug discovery underscore its utility and potential.

Conclusions:

• Computer-aided drug design (CADD) significantly enhances the efficiency and success rate of anticancer drug development.
• CADD facilitates better resource allocation, leading to successful outcomes and a promising future in clinical and market settings.
• The integration of CADD into drug discovery pipelines is essential for advancing oncology therapeutics.