A reliable and efficient first principles-based method for predicting pKa values. 4. Organic bases.

Area of Science:

• Computational Chemistry
• Medicinal Chemistry
• Physical Chemistry

Background:

• The ionization constant (pK(a)) is a critical molecular property for drug development, influencing absorption, distribution, metabolism, and excretion.
• Approximately 68% of ionized drugs are weak bases, making accurate pK(a) prediction crucial for drug candidates.
• Experimental pK(a) determination is costly and time-consuming, necessitating reliable computational prediction methods.

Purpose of the Study:

• To develop and validate linear fitting equations for predicting pK(a) values of organic bases in aqueous solution.
• To establish a cost-effective computational protocol for early-stage drug discovery.
• To differentiate prediction models for saturated and unsaturated organic bases.

Main Methods:

• Utilized a previously established computational protocol (OLYP/6-311+G**//3-21G(d)) with a conductor-like screening model for solvation in water.
• Derived two linear regression equations based on the energy difference between neutral and cationic forms of organic bases.
• Employed a training/test set comprising nearly 500 organic compounds.

Main Results:

• Achieved mean absolute deviations of 0.45 pK(a) units for saturated bases and 0.52 pK(a) units for unsaturated bases.
• Demonstrated that over 60% and 86% of predicted pK(a) values fall within ±0.5 and ±1.0 pK(a) units of experimental values, respectively.
• Validated the reliability and accuracy of the computational protocol for pK(a) prediction.

Conclusions:

• The derived linear equations provide accurate pK(a) predictions for diverse organic bases.
• The computational protocol is a valuable tool for accelerating drug discovery by enabling rapid pK(a) estimation.
• The study confirms the utility of computational chemistry in predicting key physicochemical properties of drug molecules.