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Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

Structure-Activity Relationships and Drug Design

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...

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Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

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Biopharmaceutical Factors Influencing Drug Product Design: Overview

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Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Updated: May 24, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Multi-objective optimization methods in de novo drug design.

C A Nicolaou¹, C Kannas, E Loizidou

¹Computation-based Science and Technology Research Center, The Cyprus Institute, 15 Kypranoros Street, Nicosia 1061, Cyprus. cnicolaou@cs.ucy.ac.cy

Mini Reviews in Medicinal Chemistry

|March 17, 2012

Summary

De-novo drug design (DND) tackles complex, multi-objective pharmaceutical challenges. This review examines computational methods and applications for optimizing drug discovery solutions.

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Last Updated: May 24, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Computational chemistry
Medicinal chemistry
Drug discovery

Background:

De-novo drug design (DND) is a complex, multi-objective optimization problem in pharmaceutical research.
Satisfying numerous pharmaceutically important objectives simultaneously presents significant challenges.

Purpose of the Study:

To review computational methodologies for de-novo drug design.
To examine challenges, solutions, and applications in multi-objective drug discovery.

Main Methods:

Exploration of composite methods transforming multi-objective problems into single-objective ones.
Examination of Pareto methods for finding multiple compromising solutions.
Review of various computational approaches and optimization techniques.

Main Results:

Overview of diverse computational strategies applied to DND.
Identification of key considerations for multi-objective drug design.
Highlighting successful applications within the drug discovery community.

Conclusions:

Computational methods are crucial for addressing the multi-objective nature of DND.
A variety of optimization techniques are employed to balance competing drug design objectives.
Continued development in computational approaches is vital for advancing drug discovery.