Building Machine Learning Small Molecule Melting Points and Solubility Models Using CCDC Melting Points Dataset.

Predicting solubility of small molecules is a very difficult undertaking due to the lack of reliable and consistent experimental solubility data. It is well known that for a molecule in a crystal lattice to be dissolved, it must, first, dissociate from the lattice and then, second, be solvated. The melting point of a compound is proportional to the lattice energy, and the octanol-water partition coefficient (log P) is a measure of the compound's solvation efficiency. The CCDC's melting point dataset of almost one hundred thousand compounds was utilized to create widely applicable machine learning models of small molecule melting points. Using the general solubility equation, the aqueous thermodynamic solubilities of the same compounds can be predicted. The global model could be easily localized by adding additional melting point measurements for a chemical series of interest.

Using Machine Learning To Predict Partition Coefficient (Log P) and Distribution Coefficient (Log D) with Molecular Descriptors and Liquid Chromatography Retention Time.

During preclinical evaluations of drug candidates, several physicochemical (p-chem) properties are measured and employed as metrics to estimate drug efficacy in vivo. Two such p-chem properties are the octanol-water partition coefficient, Log P, and distribution coefficient, Log D, which are useful in estimating the distribution of drugs within the body. Log P and Log D are traditionally measured using the shake-flask method and high-performance liquid chromatography. However, it is challenging to measure these properties for species that are very hydrophobic (or hydrophilic) owing to the very low equilibrium concentrations partitioned into octanol (or aqueous) phases. Moreover, the shake-flask method is relatively time-consuming and can require multistep dilutions as the range of analyte concentrations can differ by several orders of magnitude. Here, we circumvent these limitations by using machine learning (ML) to correlate Log P and Log D with liquid chromatography (LC) retention time (RT). Predictive models based on four ML algorithms, which used molecular descriptors and LC RTs as features, were extensively tested and compared. The inclusion of RT as an additional descriptor improves model performance (MAE = 0.366 and R2 = 0.89), and Shapley additive explanations analysis indicates that RT has the highest impact on model accuracy.

Octanol/Air Partition Coefficient─A General-Purpose Fragment Model to Predict Log Koa from Molecular Structure.

The octanol/air partition coefficient Koa is important for assessing the bioconcentration of airborne xenobiotics in foliage and in air-breathing organisms. Moreover, Koa informs about compound partitioning to aerosols and indoor dust, and complements the octanol/water partition coefficient Kow and the air/water partition coefficient Kaw for multimedia fate modeling. Experimental log Koa at 25 °C has been collected from literature for 2161 compounds with molecular weights from 16 to 959 Da. The curated data set covers 18.2 log units (from -1.0 to 17.2). A newly developed fragment model for predicting log Koa from molecular structure outperforms COSMOtherm, EPI-Suite KOAWIN, OPERA, and linear solvation energy relationships (LSERs) regarding the root-mean-squared error (rms) and the maximum negative and positive errors (mne and mpe) (rms: 0.57 vs 0.86 vs 1.09 vs 1.19 vs 1.05-1.53, mne: -2.55 vs -3.95 vs -7.51 vs -7.54 vs (-5.63) - (-7.34), mpe: 2.91 vs 5.97 vs 7.54 vs 4.24 vs 6.89-10.2 log units). The prediction capability, statistical robustness, and sound mechanistic basis are demonstrated through initial separation into a training and prediction set (80:20%), mutual leave-50%-out validation, and target value scrambling in terms of temporarily wrong compound-Koa allocations. The new general-purpose model is implemented in a fully automatized form in the ChemProp software available to the public. Regarding Koa indirectly determined through Kow and Kaw, a new approach is developed to convert from wet to dry octanol, enabling higher consistency in experimental (and thus also predicted) Koa.

Intermolecular Interactions, Solute Descriptors, and Partition Properties of Neutral Per- and Polyfluoroalkyl Substances (PFAS).

The environmental partition properties of perfluoroalkyl and polyfluoroalkyl substances (PFAS) must be understood for their transport and fate analysis. In this study, isothermal gas chromatographic (GC) retention times of 60 neutral PFAS were measured using four columns with different stationary phase polarities, which indicated varying polar interactions exerted by these substances. The GC data were combined with new octanol/water partition coefficient data from this study and existing partition coefficient data from the literature and used to determine the polyparameter linear free energy relationship (PP-LFER) solute descriptors. A complete set of the solute descriptors was obtained for 47 PFAS, demonstrating the characteristic intermolecular interaction properties, such as hydrogen bonding capabilities influenced by the electron-withdrawing perfluoroalkyl group. The partition coefficients between octanol and water, air and water, and octanol and air predicted by the PP-LFER models agreed with those predicted by the quantum chemically based model COSMOtherm, suggesting that both models are highly accurate for neutral PFAS and can fill the current large data gaps in partition property data. A chemical partitioning space plot was generated by using the PP-LFER-predicted partition coefficients, showing the primary importance of the air phase for the environmental distribution of nonpolar and weakly polar PFAS and the increasing significance of organic phases with increasing PFAS polarity.

Cross-Species Evaluation of Bioaccumulation Thresholds for Air-Breathing Animals.

In air-breathing organisms, an organic chemical's susceptibility to elimination via urinary excretion and respiratory exhalation can be judged on the basis of the octanol-water partition ratio (KOW) and the octanol-air partition ratio (KOA), respectively. Current regulations specify that chemicals with KOW values of <102 and KOA values of <105 may be screened as non-bioaccumulative in air breathers. Here we used a model-based approach to evaluate whether these thresholds are consistent with a biomagnification factor of 1 for 141 different mammals, birds, and reptiles. Animals with lower rates of respiration (e.g., manatees and sloths) and those ingesting high-lipid diets (e.g., polar bears and carnivorous birds) were predicted to be able to biomagnify persistent chemicals with KOA values of <105. This was also observed for several temperate reptiles due to their lower respiration rates and internal temperatures. Protective KOA thresholds were determined to be <104.85 for mammals, <104.60 for birds, <104.60 for reptiles at >25 °C, and <103.95 for reptiles at ≤25 °C. For all animals, urination alone was not efficient to prevent the biomagnification of any organic chemical. For chemicals with KOW values of <101, we found that biomagnification of persistent chemicals was constrained by the water-air partition ratio (KWA) rather than KOA. Differences in physiology may need to be considered in bioaccumulation assessments of air-breathing species.

Nα -Methylation of arginine: Implications for cell-penetrating peptides.

The field of cell-penetrating peptides is dominated by the use of oligomers of arginine residues. Octanol-water partitioning in the presence of an anionic lipid is a validated proxy for cell-penetrative efficacy. Here, we add one, two, or three N-methyl groups to Ac-Arg-NH2 and examine the effects on octanol-water partitioning. In the absence of an anionic lipid, none of these arginine derivatives can be detected in the octanol layer. In the presence of sodium dodecanoate, however, increasing N-methylation correlates with increasing partitioning into octanol, which is predictive of higher cell-penetrative ability. We then evaluated fully Nα -methylated oligoarginine peptides and observed an increase in their cellular penetration compared with canonical oligoarginine peptides in some contexts. These findings indicate that a simple modification, Nα -methylation, can enhance the performance of cell-penetrating peptides.

Lipophilicity Modulations by Fluorination Correlate with Membrane Partitioning.

Bioactive compounds generally need to cross membranes to arrive at their site of action. The octanol-water partition coefficient (lipophilicity, logPOW ) has proven to be an excellent proxy for membrane permeability. In modern drug discovery, logPOW and bioactivity are optimized simultaneously, for which fluorination is one of the relevant strategies. The question arises as to which extent the often subtle logP modifications resulting from different aliphatic fluorine-motif introductions also lead to concomitant membrane permeability changes, given the difference in molecular environment between octanol and (anisotropic) membranes. It was found that for a given compound class, there is excellent correlation between logPOW values with the corresponding membrane molar partitioning coefficients (logKp ); a study enabled by novel solid-state 19 F NMR MAS methodology using lipid vesicles. Our results show that the factors that cause modulation of octanol-water partition coefficients similarly affect membrane permeability.

Comparison of logP and logD correction models trained with public and proprietary data sets.

In drug discovery, partition and distribution coefficients, logP and logD for octanol/water, are widely used as metrics of the lipophilicity of molecules, which in turn have a strong influence on the bioactivity and bioavailability of potential drugs. There are a variety of established methods, mostly fragment or atom-based, to calculate logP while logD prediction generally relies on calculated logP and pKa for the estimation of neutral and ionized populations at a given pH. Algorithms such as ClogP have limitations generally leading to systematic errors for chemically related molecules while pKa estimation is generally more difficult due to the interplay of electronic, inductive and conjugation effects for ionizable moieties. We propose an integrated machine learning QSAR modeling approach to predict logD by training the model with experimental data while using ClogP and pKa predicted by commercial software as model descriptors. By optimizing the loss function for the ClogD calculated by the software, we build a correction model that incorporates both descriptors from the software and available experimental logD data. Additionally, we calculate logP from the logD model using the software predicted pKa's. Here, we have trained models using publicly or commercial available logD data to show that this approach can improve on commercial software predictions of lipophilicity. When applied to other logD data sets, this approach extends the domain of applicability of logD and logP predictions over commercial software. Performance of these models favorably compare with models built with a larger set of proprietary logD data.

Predicting octanol/water partition coefficients and pKa for the SAMPL7 challenge using the SM12, SM8 and SMD solvation models.

Blind predictions of octanol/water partition coefficients and pKa at 298.15 K for 22 drug-like compounds were made for the SAMPL7 challenge. Octanol/water partition coefficients were predicted from solvation free energies computed using electronic structure calculations with the SM12, SM8 and SMD solvation models. Within these calculations we compared the use of gas- and solution-phase optimized geometries of the solute. Based on these calculations we found that in general the use of solution phase-optimized geometries increases the affinity of the solutes for water as compared to octanol, with the use of gas-phase optimized geometries resulting in the better agreement with experiment. The pKa is computed using the direct approach, scaled solvent-accessible surface model, and the inclusion of an explicit water molecule, where the latter two methods have previously been shown to offer improved predictions as compared to the direct approach. We find that the use of an explicit water molecule provides superior predictions, and that the predicted macroscopic pKa is sensitive to the employed microstates.

Quantum Chemical Calculation and Evaluation of Partition Coefficients for Classical and Emerging Environmentally Relevant Organic Compounds.

Octanol/water (KOW), octanol/air (KOA), and hexadecane/air (KHdA) partition coefficients are calculated for 67 organic compounds of environmental concern using computational chemistry. The extended CRENSO workflow applied here includes the calculation of extensive conformer ensembles with semiempirical methods and refinement through density functional theory, taking into account solvation models, especially COSMO-RS, and thermostatistical contributions. This approach is particularly advantageous for describing large and nonrigid molecules. With regard to KOW and KHdA, one can refer to many experimental data from direct and indirect measurement methods, and very good matches with results from our quantum chemical workflow are evident. In the case of the KOA values, however, good matches are only obtained for the experimentally determined values. Larger systematic deviations between data computed here and available, nonexperimental quantitative structure-activity relationship literature data occur in particular for phthalic acid esters and organophosphate esters. From a critical analysis of the coefficients calculated in this work and comparison with available literature data, we conclude that the presented quantum chemical composite approach is the most powerful so far for calculating reliable partition coefficients because all physical contributions to the conformational free energy are considered and the structure ensembles for the two phases are generated independently and consistently.

Odor coding in the antenna of the tsetse fly Glossina morsitans.

Tsetse flies transmit trypanosomiasis to humans and livestock across much of sub-Saharan Africa. Tsetse are attracted by olfactory cues emanating from their hosts. However, remarkably little is known about the cellular basis of olfaction in tsetse. We have carried out a systematic physiological analysis of the Glossina morsitans antenna. We identify 7 functional classes of olfactory sensilla that respond to human or animal odorants, CO2, sex and alarm pheromones, or other odorants known to attract or repel tsetse. Sensilla differ in their response spectra, show both excitatory and inhibitory responses, and exhibit different response dynamics to different odor stimuli. We find striking differences between the functional organization of the tsetse fly antenna and that of the fruit fly Drosophila melanogaster One morphological type of sensilla has a different function in the 2 species: Trichoid sensilla respond to pheromones in Drosophila but respond to a wide diversity of compounds in G. morsitans. In contrast to Drosophila, all tested G. morsitans sensilla that show excitatory responses are excited by one odorant, 1-octen-3-ol, which is contained in host emanations. The response profiles of some classes of sensilla are distinct but strongly correlated, unlike the organization described in the Drosophila antenna. Taken together, this study defines elements that likely mediate the attraction of tsetse to its hosts and that might be manipulated as a means of controlling the fly and the diseases it transmits.

Relating Conformational Equilibria to Conformer-Specific Lipophilicities: New Opportunities in Drug Discovery.

Efficient drug discovery is based on a concerted effort in optimizing bioactivity and compound properties such as lipophilicity, and is guided by efficiency metrics that reflect both aspects. While conformation-activity relationships and ligand conformational control are known strategies to improve bioactivity, the use of conformer-specific lipophilicities (logp) is much less explored. Here we show how conformer-specific logp values can be obtained from knowledge of the macroscopic logP value, and of the equilibrium constants between the individual species in water and in octanol. This is illustrated with fluorinated amide rotamers, with integration of rotamer 19 F NMR signals as a facile, direct method to obtain logp values. The difference between logp and logP optimization is highlighted, giving rise to a novel avenue for lipophilicity control in drug discovery.

The Synthesis of Ipsenol and Ipsdienol: A Review (1968-2020).

Herein I present a review on the synthesis of ipsenol and ipsdienol, two aggregation pheromones of bark beetles, isolated from different species of genus Ips, and serious pests of conifer forests. I have covered the literature for around fifty years, since 1968 to 2020. This account has been divided in different sections and sub-sections, including a general and brief outlook on their isolation, structure and biological activity, to continue with the reported synthesis of racemic ipsenol and ipsdienol, including my own contribution to topic, and the presentation of reports describing the synthesis of enantiomerically pure ipsenol and ipsdienol. Particular attention has been devoted to identify and highlight racemic or enantiomerically pure "isoprene synthons", and isoprenylation methods employed in the synthesis of ipsenol and ipsdienol, of general interest for related terpene derivatives synthesis.

Predicting Leachables Solubilization in Polysorbate 80 Solutions by a Linear Solvation Energy Relationship (LSER).

PURPOSE: A linear solvation energy relationship (LSER) was developed to predict the partitioning of neutral chemicals from polysorbate 80 (PS 80) micelles to water. Predicted partition coefficients were converted to a concentration dependent solubilization strength of aqueous PS 80 solutions. This solubilization strength represents a key parameter to project equilibrium levels of leaching from pharmaceutical plastic materials. METHODS: To construct the LSER model equation, partition coefficients between PS 80 micelles and water were measured via a reference phase method or collected from the literature. Multiple linear regression of partition coefficients against five publicly available solute parameters was used to obtain the LSER system parameters. RESULTS: 112 chemically diverse compounds were incorporated for LSER model regression. The model equation shows a very good fit (R2 = 0.969, SD = 0.219) for the entire dataset. The accuracy of the multi-parameter LSER model was proven to be substantially better in comparison to a single-parameter log-linear model based on the octanol-water partition coefficient. CONCLUSION: PS 80 solubilization strength in water can expediently and accurately be calculated for neutral organic compounds with the proposed LSER model. LSER system parameters provide insightful chemical information with respect to solubilization in aqueous solutions of PS 80.

Precise force-field-based calculations of octanol-water partition coefficients for the SAMPL7 molecules.

We predicted water-octanol partition coefficients for the molecules in the SAMPL7 challenge with explicit solvent classical molecular dynamics (MD) simulations. Water hydration free energies and octanol solvation free energies were calculated with a windowed alchemical free energy approach. Three commonly used force fields (AMBER GAFF, CHARMM CGenFF, OPLS-AA) were tested. Special emphasis was placed on converging all simulations, using a criterion developed for the SAMPL6 challenge. In aggregate, over 1000 [Formula: see text]s of simulations were performed, with some free energy windows remaining not fully converged even after 1 [Formula: see text]s of simulation time. Nevertheless, the amount of sampling produced [Formula: see text] estimates with a precision of 0.1 log units or better for converged simulations. Despite being probably as fully sampled as can expected and is feasible, the agreement with experiment remained modest for all force fields, with no force field performing better than 1.6 in root mean squared error. Overall, our results indicate that a large amount of sampling is necessary to produce precise [Formula: see text] predictions for the SAMPL7 compounds and that high precision does not necessarily lead to high accuracy. Thus, fundamental problems remain to be solved for physics-based [Formula: see text] predictions.

Prediction of n-octanol/water partition coefficients and acidity constants (pKa) in the SAMPL7 blind challenge with the IEFPCM-MST model.

Within the scope of SAMPL7 challenge for predicting physical properties, the Integral Equation Formalism of the Miertus-Scrocco-Tomasi (IEFPCM/MST) continuum solvation model has been used for the blind prediction of n-octanol/water partition coefficients and acidity constants of a set of 22 and 20 sulfonamide-containing compounds, respectively. The log P and pKa were computed using the B3LPYP/6-31G(d) parametrized version of the IEFPCM/MST model. The performance of our method for partition coefficients yielded a root-mean square error of 1.03 (log P units), placing this method among the most accurate theoretical approaches in the comparison with both globally (rank 8th) and physical (rank 2nd) methods. On the other hand, the deviation between predicted and experimental pKa values was 1.32 log units, obtaining the second best-ranked submission. Though this highlights the reliability of the IEFPCM/MST model for predicting the partitioning and the acid dissociation constant of drug-like compounds compound, the results are discussed to identify potential weaknesses and improve the performance of the method.

Energy-entropy prediction of octanol-water logP of SAMPL7 N-acyl sulfonamide bioisosters.

Partition coefficients quantify a molecule's distribution between two immiscible liquid phases. While there are many methods to compute them, there is not yet a method based on the free energy of each system in terms of energy and entropy, where entropy depends on the probability distribution of all quantum states of the system. Here we test a method in this class called Energy Entropy Multiscale Cell Correlation (EE-MCC) for the calculation of octanol-water logP values for 22 N-acyl sulfonamides in the SAMPL7 Physical Properties Challenge (Statistical Assessment of the Modelling of Proteins and Ligands). EE-MCC logP values have a mean error of 1.8 logP units versus experiment and a standard error of the mean of 1.0 logP units for three separate calculations. These errors are primarily due to getting sufficiently converged energies to give accurate differences of large numbers, particularly for the large-molecule solvent octanol. However, this is also an issue for entropy, and approximations in the force field and MCC theory also contribute to the error. Unique to MCC is that it explains the entropy contributions over all the degrees of freedom of all molecules in the system. A gain in orientational entropy of water is the main favourable entropic contribution, supported by small gains in solute vibrational and orientational entropy but offset by unfavourable changes in the orientational entropy of octanol, the vibrational entropy of both solvents, and the positional and conformational entropy of the solute.

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge.

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 pKa 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 pKa 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 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa 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 pKa prediction methods.

Mosquito Attractants.

Vector control and personal protection against anthropophilic mosquitoes mainly rely on the use of insecticides and repellents. The search for mosquito-attractive semiochemicals has been the subject of intense studies for decades, and new compounds or odor blends are regularly proposed as lures for odor-baited traps. We present a comprehensive and up-to-date review of all the studies that have evaluated the attractiveness of volatiles to mosquitoes, including individual chemical compounds, synthetic blends of compounds, or natural host or plant odors. A total of 388 studies were analysed, and our survey highlights the existence of 105 attractants (77 volatile compounds, 17 organism odors, and 11 synthetic blends) that have been proved effective in attracting one or several mosquito species. The exhaustive list of these attractants is presented in various tables, while the most common mosquito attractants - for which effective attractiveness has been demonstrated in numerous studies - are discussed throughout the text. The increasing knowledge on compounds attractive to mosquitoes may now serve as the basis for complementary vector control strategies, such as those involving lure-and-kill traps, or the development of mass trapping. This review also points out the necessity of further improving the search for new volatile attractants, such as new compound blends in specific ratios, considering that mosquito attraction to odors may vary over the life of the mosquito or among species. Finally, the use of mosquito attractants will undoubtedly have an increasingly important role to play in future integrated vector management programs.

QSPR modeling of octanol-water partition coefficient and organic carbon normalized sorption coefficient of diverse organic chemicals using Extended Topochemical Atom (ETA) indices.

Octanol-water partition coefficient (logKow) and soil organic carbon content normalized sorption coefficient (logKoc) values are two important physicochemical properties in the context of bioaccumulation and environmental fate of organic compounds and their environmental risk assessment. Simple, interpretable and easy-to-derive extended topochemical atom (ETA) indices obtained from 2D structural representation of compounds were used for quantitative structure-property relationship (QSPR) modeling of these two endpoints. Linear regression based models developed using only ETA indices show encouraging statistical and validation results. Based on the information obtained from developed QSPR models, we may conclude that molecular volume, branching pattern, presence of hydrophobic Cl atoms, cyclicity/fusion, polar environment, electron density, unsaturation content, hydrogen bonding propensity or hydrogen bond donor atoms, local topology, presence of heteroatoms and aromaticity are crucial factors in controlling the logKow and logKoc values of the compounds. The suggested explanatory features for different classes of chemicals or the whole diverse set can help in safer designing of chemicals, which is one of the primary agenda of the "Green Chemistry" program.