RESUMEN
Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Experiments show that the QM/ML models outperform the linear regression models and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors.
Asunto(s)
Cisteína , Diseño de Fármacos , Aprendizaje Automático , Teoría Cuántica , Cisteína/química , Acrilamida/química , Humanos , Modelos Moleculares , Relación Estructura-Actividad Cuantitativa , Modelos Lineales , Estructura MolecularRESUMEN
The ionization of bioactive molecules impacts many ADME-relevant physicochemical properties, in particular, solubility, lipophilicity, and permeability. Ampholytes contain both acidic and basic groups and are distinguished as ordinary ampholytes and zwitterions. An influential review states that zwitterions only exist if the acidic pKa is significantly lower than the basic pKa. Through concordance of measured and calculated pKa and log P, we show that the zwitterionic behavior of several marketed drugs and natural products occurs despite a low or negative ΔpKa. These nonclassical zwitterions are characterized by a weak acidic and basic pKa and conjugation through an extended aromatic system, often including pseudorings via intramolecular hydrogen bonds. In contrast to most classical zwitterions, nonclassical zwitterions can exhibit excellent permeability. As permeability and lipophilicity are typically correlated, the combination of low lipophilicity and high permeability makes nonclassical zwitterions an attractive design principle in medicinal chemistry.