In silico pKa prediction and ADME profiling.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Accurate prediction of drug candidate physicochemical properties, such as pKa, is vital for lead optimization.
• pKa influences key ADME properties including lipophilicity, solubility, and metabolism.
• Empirical pKa prediction methods face challenges with novel chemical entities due to complex determinant factors and limited experimental data.

Purpose of the Study:

• To introduce MoKa, a computational package designed to accurately predict pKa values for organic compounds.
• To address the limitations of existing empirical methods for pKa prediction in drug discovery.
• To provide medicinal and computational chemists with a tool to guide the design of new drug candidates.

Main Methods:

• Development of the MoKa computational package integrating graphical and command-line tools.
• Consideration of accurate training data selection and fundamental prediction methodology.
• Extension of the methodology for protein pKa prediction and handling of multiprotic compounds and tautomers.

Main Results:

• MoKa provides accurate pKa predictions, aiding in the understanding of ionization states.
• The tool facilitates the prediction of ADME properties like solubility, lipophilicity, and metabolism based on pKa.
• Demonstration of MoKa's application in specific drug design scenarios.

Conclusions:

• MoKa is a valuable computational tool for predicting pKa values of organic compounds.
• Accurate pKa prediction using MoKa can significantly improve the optimization of drug candidates' ADME profiles.
• The tool supports rational drug design by providing insights into compound ionization and its impact on drug properties.