Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models.

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge" in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported.

Water envelope has a critical impact on the design of protein-protein interaction inhibitors.

We show that a water envelope network plays a critical role in protein-protein interactions (PPI). The potency of a PPI inhibitor is modulated by orders of magnitude on manipulation of the solvent envelope alone. The structure-activity relationship of PEX14 inhibitors was analyzed as an example using in silico and X-ray data.

On a relationship between molecular polarizability and partial molar volume in water.

We reveal a universal relationship between molecular polarizability (a single-molecule property) and partial molar volume in water that is an ensemble property characterizing solute-solvent systems. Since both of these quantities are of the key importance to describe solvation behavior of dissolved molecular species in aqueous solutions, the obtained relationship should have a high impact in chemistry, pharmaceutical, and life sciences as well as in environments. We demonstrated that the obtained relationship between the partial molar volume in water and the molecular polarizability has in general a non-homogeneous character.

In silico screening of bioactive and biomimetic solutes using Integral Equation Theory.

The Integral Equation Theory (IET) of Molecular Liquids is a theoretical framework for modelling solution phase behaviour that has recently found new applications in computational drug design. IET allows calculation of solvation thermodynamic parameters at significantly lower computational expense than explicit solvent simulations, but also provides information about the microscopic solvent structure that is not accessible by implicit continuum models. In this review we focus on recent advances in two fields of research using these methods: (i) calculation of the hydration free energies of bioactive molecules; (ii) modelling the aggregation of biomimetic molecules.

Toward a universal model to calculate the solvation thermodynamics of druglike molecules: the importance of new experimental databases.

We demonstrate that a new free energy functional in the integral equation theory of molecular liquids gives accurate calculations of hydration thermodynamics for druglike molecules. The functional provides an improved description of excluded volume effects by incorporating two free coefficients. When the values of these coefficients are obtained from experimental data for simple organic molecules, the hydration free energies of an external test set of druglike molecules can be calculated with an accuracy of about 1 kcal/mol.

Combination of RISM and Cheminformatics for Efficient Predictions of Hydration Free Energy of Polyfragment Molecules: Application to a Set of Organic Pollutants.

Here, we discuss a new method for predicting the hydration free energy (HFE) of organic pollutants and illustrate the efficiency of the method on a set of 220 chlorinated aromatic hydrocarbons. The new model is computationally inexpensive, with one HFE calculation taking less than a minute on a PC. The method is based on a combination of a molecular integral equations theory, one-dimensional reference interaction site model (1D RISM), with the cheminformatics approach.

Hydration thermodynamics using the reference interaction site model: speed or accuracy?

We report a method to dramatically improve the accuracy of hydration free energies (HFE) calculated by the 1D and 3D reference interaction site models (RISM) of molecular integral equation theory. It is shown that the errors in HFEs calculated by RISM approaches using the Gaussian fluctuations (GF) free energy functional are not random, but can be decomposed into linear combination of contributions from different structural elements of molecules (number of double bonds, number of OH groups, etc.).

Towards a universal method for calculating hydration free energies: a 3D reference interaction site model with partial molar volume correction.

We report a simple universal method to systematically improve the accuracy of hydration free energies calculated using an integral equation theory of molecular liquids, the 3D reference interaction site model. A strong linear correlation is observed between the difference of the experimental and (uncorrected) calculated hydration free energies and the calculated partial molar volume for a data set of 185 neutral organic molecules from different chemical classes. By using the partial molar volume as a linear empirical correction to the calculated hydration free energy, we obtain predictions of hydration free energies in excellent agreement with experiment (R = 0.

An accurate prediction of hydration free energies by combination of molecular integral equations theory with structural descriptors.

In this work, we report a novel method for the estimation of the hydration free energy of organic molecules, the structural descriptors correction (SDC) model. The method is based on a combination of the reference interaction site model (RISM) with several empirical corrections. The model requires only a small number of chemical descriptors associated with the main features of the chemical structure of solutes: excluded volume, branch, double bond, benzene ring, hydroxyl group, halogen atom, aldehyde group, ketone group, ether group, and phenol fragment.