DRUG DEVELOPMENT BY IN SILICO METHODS

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Multicenter studies confirm a direct link between drug spatial structure and biological activity.
• Stereoisomeric factors significantly influence drug efficacy and side effects, driving research in this area.

Purpose of the Study:

• To highlight the relevance of computational approaches in drug discovery.
• To emphasize the importance of Quantitative Structure-Activity Relationships (QSAR) and in silico methods.

Main Methods:

• Utilizing computational approaches to analyze the relationship between substance structure and biological properties.
• Employing Quantitative Structure-Activity Relationships (QSAR) and molecular modeling techniques.
• Leveraging in silico methods for preliminary drug screening and selection.

Main Results:

• QSAR is widely adopted in modern drug chemistry for quantitative structure-activity relationship analysis.
• In silico methods, including QSAR and molecular modeling, have demonstrated success in discovering new biologically active substances.
• The vast number of known organic compounds and biological activities makes global experimental testing infeasible.

Conclusions:

• In silico methods are highly relevant for drug discovery, particularly for the preliminary selection of unpromising compounds.
• Computational approaches offer an efficient alternative to exhaustive experimental screening.
• The integration of QSAR and molecular modeling accelerates the identification of potential drug candidates.