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Context: Extensive studies using a trial-and-error approach have been conducted on low-rank coal flotation collectors. However, screening efficient collectors remains a considerable challenge due to the lack of suitable screening principles. It has proven that polar compounds such as carboxylic acids and esters are effective collectors for low-rank coal flotation. In this work, the effects of carboxylic acid, alcohol, and methyl ester on the floatability of low-rank coal were compared, the flotation performance of the polar collector was evaluated with theoretical calculations, a suitable evaluation parameter was determined and a screening principle based on this parameter was determined. The results show that the enhancement effects of polar collectors on low-rank coal floatability follow the order of methyl decanoate > methyl laurate > methyl octanoate > sec-octanol > methyl oleate (or methyl oleate > sec-octanol) > n-octanoic acid. Compared with the molecular polarity index, the hydrophobicity indices log P and dipole moment cannot be used to accurately evaluate different types of collectors and the same type of collectors, respectively. At room temperature, liquid polar compounds with molecular polarity indices in the range of 6.0 ~ 8.0 kcal/mol effectively enhance the floatability of low-rank coal. The molecular polarity index of the collector is used for the first time to screen effective collectors of low-rank coal in this work. This parameter is anticipated to be highly important for the development and research of low-rank coal and other mineral collectors., Methods: To obtain reasonable and accurate molecular structure, geometry optimization and frequency calculations of the studied collectors were conducted via the Gaussian 09 software package based on density functional theory at the B3LYP/6-311 + G (d, p) level. The integral equation formalism for the polarizable continuum model (IEF-PCM) was utilized with water as the solvent (dielectric constant = 78.36, T = 298 K) for all the calculations. Then, the atomic charge distributions (MPA and NPA) and electrostatic potential maps, the dipole moment and molecular polarity index, and the log P and water solubilities of studied collectors were shown or calculated by Gauss View 5.0, Mutiwfn program and website ( https://www.molsoft.com/mprop/mprop.cgi ), respectively., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Inclusion complexes require higher concentration of Beta cyclodextrins (βCD) resulting in increased formulation bulk, toxicity, and production costs. This systematic review offers a comprehensive analysis using Quality by design (QbD) as a tool to predict potential applications of Polyvinylpyrrolidone (PVP) as a ternary substance to address issues of inclusion complexes. We reviewed 623 documents from 2013 to 2023 and Eighteen (18) research papers were selected for statistical and meta-analysis using the QbD concept to identify the most critical factors for selecting drugs and effect of PVP on inclusion complexes. The QbD analysis revealed that Molecular weight (MW), Partition coefficient (Log P), and the auxiliary substance ratio directly affected complexation efficiency (CE), thermodynamic stability in terms of Gibbs free energy (ΔG), and percent drug release. However, Stability constant (K s ) remained unaffected by any of these parameters. The results showed that low MW (250), median Log P (6), and a βCD: PVP ratio of 2:3 would result in higher CE, lower G, and improved drug release. PVP improves drug solubility, enhances delivery and therapeutic outcomes, and counteracts increased drug ionization due to decreased pH. In certain cases, its bulky nature and hydrogen bonding with CD molecules can form non-inclusion complexes. The findings of the study shows that there is potential molecular interaction between PVP and β-cyclodextrins, which possibly enhances the stability of inclusion complexes for drug with low MW and log P values less than 9. The systematic review shows a comprehensive methodology based on QbD offers a replicable template for future investigations into drug formulation research., (© 2024. The Author(s), under exclusive licence to American Association of Pharmaceutical Scientists.)

Blind predictions of octanol/water partition coefficients at 298.15 K for 22 drug-like compounds were made for the SAMPL7 challenge. The octanol/water partition coefficients were predicted using solvation free energies computed using molecular dynamics simulations, wherein we considered the use of both pure and water-saturated 1-octanol to model the octanol-rich phase. Water and 1-octanol were modeled using TIP4P and TrAPPE-UA, respectively, which have been shown to well reproduce the experimental mutual solubility, and the solutes were modeled using GAFF. After the close of the SAMPL7 challenge, we additionally made predictions using TIP4P/2005 water. We found that the predictions were sensitive to the choice of water force field. However, the effect of water in the octanol-rich phase was found to be even more significant and non-negligible. The effect of inclusion of water was additionally sensitive to the chemical structure of the solute., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pK a for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pK a challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pK a challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pK a values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pK a prediction methods., (© 2021. The Author(s).)

The prediction of [Formula: see text] values is one part of the statistical assessment of the modeling of proteins and ligands (SAMPL) blind challenges. Here, we use a molecular graph representation method called Geometric Scattering for Graphs (GSG) to transform atomic attributes to molecular features. The atomic attributes used here are parameters from classical molecular force fields including partial charges and Lennard-Jones interaction parameters. The molecular features from GSG are used as inputs to neural networks that are trained using a "master" dataset comprised of over 41,000 unique [Formula: see text] values. The specific molecular targets in the SAMPL7 [Formula: see text] prediction challenge were unique in that they all contained a sulfonyl moeity. This motivated a set of ClassicalGSG submissions where predictors were trained on different subsets of the master dataset that are filtered according to chemical types and/or the presence of the sulfonyl moeity. We find that our ranked prediction obtained 5th place with an RMSE of 0.77 [Formula: see text] units and an MAE of 0.62, while one of our non-ranked predictions achieved first place among all submissions with an RMSE of 0.55 and an MAE of 0.44. After the conclusion of the challenge we also examined the performance of open-source force field parameters that allow for an end-to-end [Formula: see text] predictor model: General AMBER Force Field (GAFF), Universal Force Field (UFF), Merck Molecular Force Field 94 (MMFF94) and Ghemical. We find that ClassicalGSG models trained with atomic attributes from MMFF94 can yield more accurate predictions compared to those trained with CGenFF atomic attributes., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Blind predictions of octanol/water partition coefficients at 298 K for 11 kinase inhibitor fragment like compounds were made for the SAMPL6 challenge. We used the conventional, "untrained", free energy based approach wherein the octanol/water partition coefficient was computed directly as the difference in solvation free energy in water and 1-octanol. We additionally proposed and used two different forms of a "trained" approach. Physically, the goal of the trained approach is to relate the partition coefficient computed using pure 1-octanol to that using water-saturated 1-octanol. In the first case, we assumed the partition coefficient using water-saturated 1-octanol and pure 1-octanol are linearly correlated. In the second approach, we assume the solvation free energy in water-saturated 1-octanol can be written as a linear combination of the solvation free energy in pure water and 1-octanol. In all cases here, the solvation free energies were computed using electronic structure calculations in the SM12, SM8, and SMD universal solvent models. In the context of the present study, our results in general do not support the additional effort of the trained approach.

Results are reported for octanol-water partition coefficients (log P) of the neutral states of drug-like molecules provided during the SAMPL6 (Statistical Assessment of Modeling of Proteins and Ligands) blind prediction challenge from applying the "embedded cluster reference interaction site model" (EC-RISM) as a solvation model for quantum-chemical calculations. Following the strategy outlined during earlier SAMPL challenges we first train 1- and 2-parameter water-free ("dry") and water-saturated ("wet") models for n-octanol solvation Gibbs energies with respect to experimental values from the "Minnesota Solvation Database" (MNSOL), yielding a root mean square error (RMSE) of 1.5 kcal mol -1 for the best-performing 2-parameter wet model, while the optimal water model developed for the pK a part of the SAMPL6 challenge is kept unchanged (RMSE 1.6 kcal mol -1 for neutral compounds from a model trained on both neutral and ionic species). Applying these models to the blind prediction set yields a log P RMSE of less than 0.5 for our best model (2-parameters, wet). Further analysis of our results reveals that a single compound is responsible for most of the error, SM15, without which the RMSE drops to 0.2. Since this is the only compound in the challenge dataset with a hydroxyl group we investigate other alcohols for which Gibbs energy of solvation data for both water and n-octanol are available in the MNSOL database to demonstrate a systematic cause of error and to discuss strategies for improvement.

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. This challenge builds on the SAMPL6 p[Formula: see text] Challenge, which asked participants to predict p[Formula: see text] values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of octanol-water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental octanol-water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 ± 0.13, 0.48 ± 0.06, 0.47 ± 0.05, and 0.50 ± 0.06, respectively.

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. The SAMPL6 Part II octanol-water partition coefficient prediction challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 [Formula: see text] prediction challenge in a blind experimental benchmark. Following experimental data collection, the partition coefficient dataset was kept blinded until all predictions were collected from participating computational chemistry groups. A total of 91 submissions were received from 27 participating research groups. This paper presents the octanol-water log P dataset for this SAMPL6 Part II partition coefficient challenge, which consisted of 11 compounds (six 4-aminoquinazolines, two benzimidazole, one pyrazolo[3,4-d]pyrimidine, one pyridine, one 2-oxoquinoline substructure containing compounds) with log P values in the range of 1.95-4.09. We describe the potentiometric log P measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future log P data collection efforts for the evaluation of computational methods.

Although log P is now recognized to be a key factor that determines the bioactivity of a molecule, the focus of medicinal chemists on hydrophobicity and log P started with the quantitative structure-activity relationships (QSAR) publications of Hansch and Fujita. Their original publication represents a dramatic change of focus to incorporate consideration of log P after a decade of work unsuccessfully attempting to use the Hammett equation to explain the structure-activity relationships of plant growth regulators. QSAR allows one to explore the quantitative relationship between log P and biological activity even when other factors also influence potency. In particular, Hansch's publications of thousands of QSAR equations demonstrate that a relationship of biological activity with log P is indeed a general phenomenon. Hansch's group also provided data and tools that enable others to explore the relationship between log P and the biological activity of compounds of interest.

A large and chemically diverse hERG inhibition data set comprised of 6690 compounds was constructed on the basis of ChEMBL bioactivity database and original publications dealing with experimental determination of hERG activities using patch-clamp and competitive displacement assays. The collected data were converted to binary format at 10 µM activity threshold and subjected to gradient boosting machine classification analysis using a minimal set of physicochemical and topological descriptors. The tested parameters involved lipophilicity (log P), ionization (pK a ), polar surface area, aromaticity, molecular size and flexibility. The employed approach allowed classifying the compounds with an overall 75-80 % accuracy, even though it only accounted for non-specific interactions between hERG and ligand molecules. The observed descriptor-response profiles were consistent with common knowledge about hERG ligand binding site, but also revealed several important quantitative trends, as well as slight inter-assay variability in hERG inhibition data. The results suggest that even weakly basic groups (pK a  < 6) might substantially contribute to hERG inhibition potential, whereas the role of lipophilicity depends on the compound's ionization state, and the influence of log P decreases in the order of bases > zwitterions > neutrals > acids. Given its robust performance and clear physicochemical interpretation, the proposed model may provide valuable information to direct drug discovery efforts towards compounds with reduced risk of hERG-related cardiotoxicity.

Target-oriented drug discovery is the main research paradigm of contemporary drug discovery. In target-oriented approaches, we attempt to maximize in vitro drug potency by finding the optimal fit to the target. This can result in a higher molecular complexity, in particular, the higher molecular weight (MW) of the drugs. However, a comparison of the successful developments of pharmaceuticals with the general trends that can be observed in medicinal chemistry resulted in the conclusion that the so-called molecular obesity is an important reason for the attrition rate of drugs. When analyzing the list of top 100 drug bestsellers versus all of the FDA approvals, we discovered that on average lower-complexity (MW, ADMET score) drugs are winners of the top 100 list in terms of numbers but that, especially, up to some optimal MW value, a higher molecular complexity can pay off with higher incomes. This indicates that slim drugs are doing better but that fat drugs are bigger fishes to catch.

Myricetin (MYR) is a natural compound that has been investigated as a chemopreventative agent. MYR has been shown to suppresses ultraviolet B (UVB)-induced cyclooxygenase-2 (COX-2) protein expression and reduce the incidence of UVB-induced skin tumors in mice. Despite MYR's promise as a therapeutic agent, minimal information is available to guide the progression of formulations designed for future drug development. Here, data is presented describing the solid-state and solution characterization of MYR. Investigation into the solid-state properties of MYR identified four different crystal forms, two hydrates (MYR I and MYR II) and two metastable forms (MYR IA and MYR IIA). From solubility studies, it was evident that all forms are very insoluble (<5 μg/ml) in pure water. MYR I was found to be the most stable form at 23, 35, and 56°C. Stability determination indicated that MYR undergoes rapid apparent first-order degradation under basic pH conditions, and that degradation was influenced by buffer species. Apparent first-order degradation was also seen when MYR was introduced to an oxidizing solution. Improved stability was achieved after introducing 0.1% antioxidants to the solution. MYR was found to have good stability following exposure to ultraviolet radiation (UVR), which is a consideration for topical applications. Finally, a partitioning study indicated that MYR possess a log P of 2.94 which, along with its solid-state properties, contributes to its poor aqueous solubility. Both the solid-state properties and solution stability of MYR are important to consider when developing future formulations.