Epik version 7 is a software program that uses machine learning for predicting the pK a values and protonation state distribution of complex, druglike molecules. Using an ensemble of atomic graph convolutional neural networks (GCNNs) trained on over 42,000 pK a values across broad chemical space from both experimental and computed origins, the model predicts pK a values with 0.42 and 0.72 pK a unit median absolute and root mean square errors, respectively, across seven test sets. Epik version 7 also generates protonation states and recovers 95% of the most populated protonation states compared to previous versions. Requiring on average only 47 ms per ligand, Epik version 7 is rapid and accurate enough to evaluate protonation states for crucial molecules and prepare ultra-large libraries of compounds to explore vast regions of chemical space. The simplicity and time required for the training allow for the generation of highly accurate models customized to a program's specific chemistry.

Epik version 7 is a software program that uses machine learning for predicting the pKa values and protonation state distribution of complex, drug-like molecules. Using an ensemble of atomic graph convolutional neural networks (GCNNs) trained on over 42,000 pKa values across broad chemical space from both experimental and computed origins, the model predicts pKa values with 0.42 and 0.72 log unit median absolute and RMS errors, respectively, across seven test sets. Epik version 7 also generates protonation states and recovers 95% of the most populated protonation states compared to previous versions. Requiring on average only 47 ms per ligand, Epik version 7 is rapid and accurate enough to evaluate protonation states for crucial molecules and prepare ultra-large libraries of compounds to explore vast regions of chemical space. The simplicity of and time required for the training allows for the generation of highly accurate models customized to a program’s specific chemistry.

Agonists can elicit pathway-specific conformational changes in a G protein–coupled receptor.

Molecular insight into the role of β-cyclodextrin (βCD) as a phase transfer catalyst at the liquid/liquid interface is obtained by molecular dynamics simulations of the structure and dynamics of βCD adsorbed at the interface between water and 1-bromobutane. In particular, we consider the structure and dynamics of the water and bromobutane molecules inside the βCD cavity and compare them with the behavior when βCD is dissolved in bulk water. βCD is preferentially oriented at the interface, with the cavity opening along the interface normal. While in bulk water the cavity includes 6-8 water molecules that are relatively mobile with short residence time, at the interface the cavity is mostly dehydrated and includes a single bromobutane molecule. This inclusion complex is stable in bulk water. The implication of this behavior for reverse phase transfer catalysis is discussed.

The blood−brain barrier (BBB) plays a critical role in preventing harmful endogenous and exogenous substances from penetrating the brain. Optimal brain penetration of small-molecule central nervous system (CNS) drugs is characterized by a high unbound brain/plasma ratio (K p,uu ). While various medicinal chemistry strategies and in silico models have been reported to improve BBB penetration, they have limited application in predicting K p,uu directly. We describe a physics-based computational approach, a quantum mechanics (QM)-based energy of solvation (E-sol), to predict K p,uu . Prospective application of this method in internal CNS drug discovery programs highlights the utility and accuracy of this new method, which showed a categorical accuracy of 79% and an R 2 of 0.61 from a linear regression model.