One of the most important physicochemical properties of a molecule is pKa. It is known that two parameters imperative in ADME profiling, solubility, and lipophilicity are governed by pKa, and receptor binding can be influenced by pKa. Because most drugs are ionized in physiological conditions, pKa is particularly relevant to medicinal chemistry. Despite the numerous advances in high-throughput measurements, in silico determination is still the fastest and cheapest way of obtaining pKa. This paper presents a new original computational method for pKa prediction of organic compounds. Descriptors were generated using the program GRID, and these descriptors are based on molecular interaction fields precomputed on a set of molecular fragments. The new method was developed, trained, and cross-validated by using a large and diverse data set of 24 617 pKa values. This paper presents the results for a class of 421 acidic nitrogen compounds (RMSE = 0.41, r2 = 0.97, q2 = 0.87) and for a class of 947 six-membered N-heterocyclic bases (RMSE = 0.60, r2 = 0.93, q2 = 0.85). For external validation 28 novel compounds were selected that covered nine different ionizable groups, and 39 pKa values could be experimentally determined by spectral gradient analysis (SGA). Comparison of experimental pKa with calculated pKa demonstrated that the predictive ability of the method is good (external set, r2 = 0.85, RMSE = 0.90).