SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction.

A novel crystallographic fragment screening data set was generated and used in the SAMPL7 challenge for protein-ligands. The SAMPL challenges prospectively assess the predictive power of methods involved in computer-aided drug design. Application of various methods to fragment molecules are now widely used in the search for new drugs.

Automated high throughput pK and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge.

The goal of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) challenge is to improve the accuracy of current computational models to estimate free energy of binding, deprotonation, distribution and other associated physical properties that are useful for the design of new pharmaceutical products. New experimental datasets of physicochemical properties provide opportunities for prospective evaluation of computational prediction methods. Here, aqueous pK and a range of bi-phasic logD values for a variety of pharmaceutical compounds were determined through a streamlined automated process to be utilized in the SAMPL8 physical property challenge.

Overview of the SAMPL6 pK challenge: evaluating small molecule microscopic and macroscopic pK predictions.

The prediction of acid dissociation constants (pK) is a prerequisite for predicting many other properties of a small molecule, such as its protein-ligand binding affinity, distribution coefficient (log D), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 pK Challenge was the first time that a separate challenge was conducted for evaluating pK predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises.

Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge.

The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the octanol-water partition coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects.

Standard state free energies, not pKs, are ideal for describing small molecule protonation and tautomeric states.

The pK is the standard measure used to describe the aqueous proton affinity of a compound, indicating the proton concentration (pH) at which two protonation states (e.g. A and AH) have equal free energy.

The role of shadow economies in ecological footprint quality: empirical evidence from Turkey.

The purpose of this research study is to investigate the role of shadow economies in the levels of ecological footprint for the case of Turkey. Annual data set ranging from 1961 to 2014 was used with this respect. The results of this study verify the long-term and positively elastic impact of shadow economic activities on the level of ecological footprint in Turkey; that is, 1 % change in shadow economic activities results in a 1.

Octanol-water partition coefficient measurements for the SAMPL6 blind prediction challenge.

Partition coefficients describe the equilibrium partitioning of a single, defined charge state of a solute between two liquid phases in contact, typically a neutral solute. Octanol-water partition coefficients ([Formula: see text]), or their logarithms (log P), are frequently used as a measure of lipophilicity in drug discovery. The partition coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based computational models that predict properties of pharmaceutical relevance such as protein-ligand binding affinities or hydration/solvation free energies.

pK measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments.

Determining the net charge and protonation states populated by a small molecule in an environment of interest or the cost of altering those protonation states upon transfer to another environment is a prerequisite for predicting its physicochemical and pharmaceutical properties. The environment of interest can be aqueous, an organic solvent, a protein binding site, or a lipid bilayer. Predicting the protonation state of a small molecule is essential to predicting its interactions with biological macromolecules using computational models.

An Open Library of Human Kinase Domain Constructs for Automated Bacterial Expression.

Kinases play a critical role in cellular signaling and are dysregulated in a number of diseases, such as cancer, diabetes, and neurodegeneration. Therapeutics targeting kinases currently account for roughly 50% of cancer drug discovery efforts. The ability to explore human kinase biochemistry and biophysics in the laboratory is essential to designing selective inhibitors and studying drug resistance.

Quantitative self-assembly prediction yields targeted nanomedicines.

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves.