Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction

Partition coefficients describe how a solute is distributed between two immiscible solvents. They are used in drug design as a measure of a solute’s hydrophobicity and a proxy for its membrane permeability. We calculate partition coefficients from transfer free energies using molecular dynamics simulations in explicit solvent. Setup is done by our new Solvation Toolkit which automates the process of creating input files for any combination of solutes and solvents for many popular molecular dynamics software packages. We calculate partition coefficients between octanol/water and cyclohexane/water with the Generalized AMBER Force Field (GAFF) and the Dielectric Corrected GAFF (GAFF-DC). With similar methods in the past we found a root-mean-squared error (RMSE) of 6.3 kJ/mol in hydration free energies which would correspond to an error of around 1.6 log units in partition coefficients if solvation free energies in both solvents were estimated with comparable accuracy. Here we find an overall RMSE of about 1....

/pdf/calculating-partition-coefficients-of-small-molecules-in-18dr8b18d3.pdf

Calculating Partition Coefficients of Small Molecules in Octanol/Water and Cyclohexane/Water

Drug design is a process which is driven by technological breakthroughs implying advanced experimental and computational methods. Nowadays, the techniques or the drug design methods are of paramount importance for prediction of biological profile, identification of hits, generation of leads, and moreover to accelerate the optimization of leads into drug candidates. Quantitative structure–activity relationship (QSAR) has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. From decades to recent research, QSAR methods have been applied in the development of relationship between properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. Classical QSAR studies include ligands with their binding sites, inhibition constants, rate constants, and other biological end points, in addition molecular to properties such as lipophilicity, polarizability, electronic, and steric properties or with certain structural features. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free–Wilson approaches, which exploit the three-dimensional properties of the ligands to predict their biological activities using robust chemometric techniques such as PLS, G/PLS, and ANN. This paper provides an overview of 1-6 dimension-based developed QSAR methods and their approaches. In particular, we present various dimensional QSAR approaches, such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis, Topomer CoMFA, self-organizing molecular field analysis, comparative molecule/pseudo receptor interaction analysis, comparative molecular active site analysis, and FLUFF-BALL, 4D-QSAR, and G-QSAR approaches.

Quantitative structure–activity relationship (QSAR) studies as strategic approach in drug discovery

The logarithm of the partition coefficient between n-octanol and water (logP) is an important parameter for drug discovery. Based upon the comparison of several prediction logP models, i.e. Support Vector Machines (SVM), Partial Least Squares (PLS) and Multiple Linear Regression (MLR), the authors reported SVM model is the best one in this paper.

SVM approach for predicting LogP.

Melanocytes are unique cells that produce specific melanin-containing intracellular organelles called melanosomes. Melanosomes are transported from the perinuclear area of melanocytes toward the plasma membrane as they become more melanized in order to increase skin pigmentation. In this vesicular trafficking of melanosomes, Rab27a, melanophilin, and myosin Va play crucial roles in linking melanosomes to actin-based motors. To identify novel compounds to inhibit binding interface between Rab27a and melanophilin, a pharmacophore model was built based on a modeled 3D structure of the protein complex that describes the essential binding residues in the intermolecular interaction. A pharmacophore model was employed to screen a chemical library database. Finally, 25 virtual hits were selected for biological evaluations. The biological activities of 11 analogues were evaluated in a second assay. Two compounds were identified as having concentration-dependent inhibitory activity. By analyzing structure-activity relationships of derivatives of BMD-20, two hydroxyl functional groups were found to be critical for blocking the intermolecular binding between Rab27a and melanophilin.

Identification of Novel Rab27a/Melanophilin Blockers by Pharmacophore-Based Virtual Screening

The Solvation Free Energy Density (SFED) model, a solvation model proposed by No et al. was modified to give better solvation free energies of the molecules having high polarizable groups. The SFED at a point around the molecule was represented by a linear combination of four basis functions, the contribution from the cavitation free energy of a solvent, and a constant. As an application of the SFED model, the linear expansion coefficients of the Hydration Free Energy Density (HFED) and the 1-Octanol Free Energy Density (1-OFED) were determined. Both calculated hydration free energy and 1-octanol solvation free energy of selected 95 organic molecules agreed well with experimental values. The standard errors were 0.47 and 0.39 kcal/mol, respectively. 1-Octanol/water partition coefficients (P) of the molecules were calculated from the difference of the HFE and 1-OFE of the molecules. At the same time, the logP density (LPD) of a molecule was represented by the same basis functional form with the SFED model....

A partition coefficient calculation method with the SFED model

A Partition Coefficient Calculation Method with the SFED Model.

The physics-based molecular force field (PMFF) was developed by integrating a set of potential energy functions in which each term in an intermolecular potential energy function is derived based on experimental values, such as the dipole moments, lattice energy, proton transfer energy, and X-ray crystal structures. The term "physics-based" is used to emphasize the idea that the experimental observables that are considered to be the most relevant to each term are used for the parameterization rather than parameterizing all observables together against the target value. PMFF uses MM3 intramolecular potential energy terms to describe intramolecular interactions and includes an implicit solvation model specifically developed for the PMFF. We evaluated the PMFF in three ways. We concluded that the PMFF provides reliable information based on the structure in a biological system and interprets the biological phenomena accurately by providing more accurate evidence of the biological phenomena.

/pdf/pmff-development-of-a-physics-based-molecular-force-field-7p98h1j6vu.pdf

PMFF: Development of a Physics-Based Molecular Force Field for Protein Simulation and Ligand Docking.

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A partition coefficient calculation method with the SFED model

A Partition Coefficient Calculation Method with the SFED Model.

PMFF: Development of a Physics-Based Molecular Force Field for Protein Simulation and Ligand Docking.