An integrated drug development approach applying topological descriptors.

Area of Science:

• Computational chemistry and cheminformatics
• Medicinal chemistry and drug discovery

Background:

• Drug discovery faces challenges from high attrition rates and an exponentially growing number of potential drug molecules.
• Innovation in drug development is declining, necessitating novel approaches.

Purpose of the Study:

• To explore the potential of computer-assisted drug design (CADD) in addressing current drug discovery limitations.
• To review the application of topological descriptors and various modeling techniques in rational drug development.

Main Methods:

• Utilized topological descriptors (conformation-independent and 3D) in conjunction with modeling techniques like Linear Discriminant Analysis and the Replacement Method.
• Applied computational modeling for integrated prediction of key molecular properties relevant to drug discovery.

Main Results:

• Demonstrated the effectiveness of conformation-independent descriptors for exploring large chemical libraries.
• Highlighted the utility of CADD in predicting pharmacological activity, aqueous solubility, human intestinal permeability, and P-glycoprotein (ABCB1, MDR1) affinity.
• Showcased opportunities for in silico guided drug repurposing.

Conclusions:

• CADD, particularly using topological descriptors, presents significant opportunities to overcome challenges in modern drug discovery.
• In silico methods can accelerate the identification of viable drug candidates and facilitate drug repurposing efforts.