Experimental Log Research Articles

Exploring the Influence of Ionic Liquid Anion Structure on Gas-Ionic Liquid Partition Coefficients of Organic Solutes Using Machine Learning.

This article presents an in-depth investigation into the influence of anionic structures of ionic liquids (ILs) on gas-ionic liquid partition coefficients (log K) of organic solutes in three ILs. While the primary objective was to examine whether there is a relationship between the molecular structure of the IL anion component and log K, additionally it was looked at whether the molecular descriptors of the anion in the relationships encode possible molecular interactions during the miscibility and partitioning in the IL. The research involves the compilation of data series of experimental log K values, where the cation component is constant. Such representative data series were obtained for three solutes─benzene, cyclohexane, and methanol─in three ILs with a uniform cationic component of methylimidazoliums. Using multiple linear regression models enhanced with machine learning techniques, the relationship between anionic structures and log K values was successfully quantified and modeled. Systematically selected molecular descriptors describing the anion structure show that in the case of methanol log K is strongly dependent on hydrogen bonds and Coulomb-dipolar interactions with the anion component, while in the case of benzene and cyclohexane the dispersion forces of the anion component are dominant. The outlier analysis and data interpretation highlight the need for extensive experimental data. The results confirm the initial hypothesis and provide valuable information on the role of the structure of the anionic component in determining the partitioning behavior of organic solutes. This knowledge is important for the design and optimization of ILs for specific applications, particularly as solvents in various industrial processes. The research also provides useful information about molecular interactions taking place in the interfaces of IL and organic additives in complex liquid media such as multicomponent electrolyte solutions, for example in energy storage applications.

Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set.

Aqueous solubility is one of the most important physicochemical properties of drug molecules and a major driving force for oral drug absorption. To date, the performance of in silico models for the estimation of solubility for novel chemical space is limited. To investigate possible reasons and remedies for this, the Johnson and Johnson in-house aqueous solubility data with over 40,000 compounds was leveraged. All data were generated through the same high-throughput assay, providing a unique opportunity to explore the relationship between data quality, quantity, and model estimations. Six intrinsic solubility data sets with different sizes and noise levels were generated by making use of three different approaches: (i) inclusion or exclusion of amorphous solid residue, (ii) measured or experimental log D to identify the intrinsic solubility, and (iii) adopting or omitting a quality check process in the data processing workflow. A random forest regressor was trained on the data sets with three different sets of descriptors calculated from RDKit, ADMET predictor, or Mordred, and the performances were evaluated with nested cross-validation as well as ten refined test sets. The models confirm, as expected, that with the same data set size, high-quality data leads to better model performance; however, also, models trained with larger data sets containing analytical variability can give equally accurate estimations compared to models trained with small, clean, and diverse data sets. However, noise introduced by including the presence of amorphous solid postsolubility measurement in the training data set cannot be overcome by increasing data size, as they are introducing a biased systematic positive error in the data set, confirming the importance of critical data review. Finally, two top-performing models were tested on the first test set from the second solubility challenge, achieving RMSE values of 0.74 and 0.72 and log S ± 0.5 of 46 and 48%, respectively. These results demonstrated improved performance compared to those reported in the findings of the competition, highlighting that a single-source curated data set can enhance the prediction of intrinsic solubility.

A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru.

Peru is one of the most biodiverse countries in the world, which is reflected in its wealth of knowledge about medicinal plants. However, there is a lack of information regarding intestinal absorption and the permeability of natural products. The human colon adenocarcinoma cell line (Caco-2) is an in vitro assay used to measure apparent permeability. This study aims to develop a quantitative structure-property relationship (QSPR) model using machine learning algorithms to predict the apparent permeability of the Caco-2 cell in natural products from Peru. A dataset of 1817 compounds, including experimental log Papp values and molecular descriptors, was utilized. Six QSPR models were constructed: a multiple linear regression (MLR) model, a partial least squares regression (PLS) model, a support vector machine regression (SVM) model, a random forest (RF) model, a gradient boosting machine (GBM) model, and an SVM-RF-GBM model. An evaluation of the testing set revealed that the MLR and PLS models exhibited an RMSE = 0.47 and R2 = 0.63. In contrast, the SVM, RF, and GBM models showcased an RMSE = 0.39-0.40 and R2 = 0.73-0.74. Notably, the SVM-RF-GBM model demonstrated superior performance, with an RMSE = 0.38 and R2 = 0.76. The model predicted log Papp values for 502 natural products falling within the applicability domain, with 68.9% (n = 346) showing high permeability, suggesting the potential for intestinal absorption. Additionally, we categorized the natural products into six metabolic pathways and assessed their drug-likeness. Our results provide insights into the potential intestinal absorption of natural products in Peru, thus facilitating drug development and pharmaceutical discovery efforts.

Evaluation of the EMA log kow trigger for fish BCF testing based on data for several human pharmaceuticals

In the European Medicines Agency (EMA) “Guideline for Environmental Risk Assessment of Medicinal Products for Human Use,” a fish bioconcentration factor (BCF) study is triggered in Phase I for pharmaceuticals having log Kow >4.5, to support Persistence, Bioaccumulation and Toxicity (PBT) screening, and in Phase II to assess secondary poisoning and bioaccumulation (‘B’) potential when log Kow ≥3. The standard sampling schedule outlined in OECD Test Guideline 305 (TG305) may require assessment of approximately 200 fish following exposure to low- and high-test concentrations and a negative control. We report experimental log Kow and BCF values for 64 human pharmaceuticals that were used to evaluate the current BCF testing trigger of log Kow ≥3, and whether a single BCF exposure concentration allows accurate classification of bioaccumulation potential. Our data support raising the BCF testing trigger to log Kow ≥4, and use of a single test concentration. The resulting reduction in the use of fish is consistent with the 3 R s principle and did not adversely affect classification accuracy. An assessment of potential risk of secondary poisoning was also conducted for three drugs classified as either B or vB, and no risks were identified.

Plugging of pipes by cohesive particles. Computed tomography investigation and theoretical analysis

Flow regimes leading to clogging of pipes exist for most multiphase flows with cohesive particles. Although plugging is critical in many industrial and medical applications, there are few records describing the details of the process. To address the problem, we conduct a computed tomography (CT) study of plugging in a cohesive ice slurry. We run experiments for ice concentration of 15%, Re∼3500, particle size 0.2-0.4 mm, and their surface energy ∼150 mJ/m2. The CT scans, combined with experimental logs of the main flow parameters, revealed the formation of deposits around the orifice inserted into the pipe, with a complete blockage formed downflow the orifice. The deposition efficiency of ice was relatively low ∼ 10−3. Reproducing the experimental deposition efficiency with the Lagrangian CFD model, we extracted the probability of particle capture at the orifice, which was about 0.5%. The simulation results illustrated how inter-particle interactions hindered the plugging.

Accurate and experimentally validated transition data for Si I and Si II

Aims. The aim of this study is to provide radiative data for neutral and singly ionised silicon, in particular for the first experimental oscillator strengths for near-infrared Si I lines. In addition, we aim to perform atomic structure calculations both for neutral and singly ionised silicon while including lines from highly excited levels. Methods. We performed large-scale atomic structure calculations with the relativistic multiconfiguration Dirac-Hartree-Fock method using the GRASP2K package to determine log(𝑔ƒ) values of Si I and Si II lines, taking into account valence-valence and core-valence electron correlation. In addition, we derived oscillator strengths of near-infrared Si I lines by combining the experimental branching fractions with radiative lifetimes from our calculations. The silicon plasma was obtained from a hollow cathode discharge lamp, and the intensity-calibrated high-resolution spectra between 1037 and 2655 nm were recorded by a Fourier transform spectrometer. Results. We provide an extensive set of accurate experimental and theoretical log(𝑔ƒ) values. For the first time, we derived 17 log(𝑔ƒ) values of Si I lines in the infrared from experimental measurements. We report data for 1500 Si I lines and 500 Si II lines. The experimental uncertainties of our ƒ-values vary between 5% for the strong lines and 25% for the weak lines. The theoretical log(𝑔ƒ) values for Si I lines in the range 161 nm to 6340 nm agree very well with the experimental values of this study and complete the missing transitions involving levels up to 3s23p7s (61 970 cm−1). In addition, we provide accurate calculated log(𝑔ƒ) values of Si II lines from the levels up to 3s27f (122 483 cm−1) in the range 81 nm to 7324 nm.

Few-Shot Continual Learning via Flat-to-Wide Approaches.

The existing approaches on continual learning (CL) call for a lot of samples in their training processes. Such approaches are impractical for many real-world problems having limited samples because of the overfitting problem. This article proposes a few-shot CL approach, termed flat-to-wide approach (FLOWER), where a flat-to-wide learning process finding the flat-wide minima is proposed to address the catastrophic forgetting (CF) problem. The issue of data scarcity is overcome with a data augmentation approach making use of a ball-generator concept to restrict the sampling space into the smallest enclosing ball. Our numerical studies demonstrate the advantage of FLOWER achieving significantly improved performances over prior arts notably in the small base tasks. For further study, source codes of FLOWER, competitor algorithms, and experimental logs are shared publicly in https://github.com/anwarmaxsum/FLOWER.

Meta-analysis reveals differential gene expression in tetralogy of Fallot versus controls.

Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart defect in the United States. We aimed to identify genetic variations associated with TOF using meta-analysis of publicly available digital samples to spotlight targets for prevention, screening, and treatment strategies. We used the Search Tag Analyze Resource for Gene Expression Omnibus (STARGEO) platform to identify 39 TOF and 19 non-TOF right ventricle tissue samples from microarray data and identified upregulated and downregulated genes. Associated gene expression data were analyzed using ingenuity pathway analysis and restricted to genes with a statistically significant (p < .05) difference and an absolute experimental log ratio >0.1 between disease and control samples. Our analysis identified 220 genes whose expression profiles were significantly altered in TOF vs. non-TOF samples. The most striking differences identified in gene expression included genes FBXO32, PTGES, MYL12a, and NR2F2. Some top associated canonical pathways included adrenergic signaling, estrogen receptor signaling, and the role of NFAT in cardiac hypertrophy. In general, genes involved in adaptive, defensive, and reparative cardiovascular responses showed altered expression in TOF vs. non-TOF samples. We introduced the interpretation of open "big data" using the STARGEO platform to define robust genomic signatures of congenital heart disease pathology of TOF. Overall, our meta-analysis results indicated increased metabolism, inflammation, and altered gene expression in TOF patients. Estrogen receptor signaling and the role of NFAT in cardiac hypertrophy represent unique pathways upregulated in TOF patients and are potential targets for future pharmacologic treatments.

Decomposition of bark beetle-attacked trees after mortality varies across forests

Bark beetle outbreaks result in massive amounts of deadwood which are a significant carbon reservoir that can impact forest ecosystems. Additionally, bark beetle-introduced symbiotic fungi in beetle-generated deadwood can increase termite presence and feeding. This could alter decomposition rates and saproxylic communities of bark beetle-generated deadwood. However, the fate of bark beetle-generated deadwood after tree mortality has received little attention despite the possibility bark beetles could alter decomposition processes and deadwood residence times at landscape scales following outbreaks. We tested the hypothesis that beetle-attacked trees decompose faster than unattacked trees. In Honduras, and Mississippi and Arizona, USA, we felled one recently bark beetle-attacked and one apparently healthy conspecific tree at each site that was cut into 120 experimental logs. Logs of each tree (attacked or unattacked) were assigned one of three metal mesh covering treatments: 1) fully covered to exclude all macroinvertebrates, 2) covered from above to exclude secondary bark beetle colonization, 3) no cover to allow all detrital food web organisms. Half of all logs at each site was collected after 1 and 2 years and the density loss, insect visual damage rating, and abundance of termites, ants, and beetles was measured. Bark beetle attack had the largest impacts at higher latitude and western forests: density loss between attacked and unattacked logs was consistently higher in Arizona (Flagstaff), initially slower (year 1) in Mississippi, but faster between years 1 and 2 resulting in similar density loss after 2 years, and did not differ in Honduras. Saproxylic insect wood damage rating accounted for 30% of the variation in decomposition across sites. Thus, decomposition rates of deadwood following bark beetle attack are highly variable across their geography and likely reflect important interactions among saproxylic organisms. This has implications for forest ecology and management including forest modeling with regard to carbon cycling and maintaining biodiversity of saproxylic organisms.

COMPARISON OF PARTITION COEFFICIENT (LOG P) OF DRUGS: COMPUTATIONAL AND EXPERIMENTAL DATA STUDY

Objective: The objective of this study was to determine the accuracy of the Log P calculation program (OSIRIS®, SCF bio®, Molinspiration®, ALOGPS 2.1®, Molsoft®, ACD/logP ®, PkCSM ®, and Swiss ADME ®) comparing it with the Log P value from the experimental results of the partition coefficient between n-octanol-water (Log P exp) taken from journals and databases.&#x0D; Methods: The predicted results of the computational Log P as the independent variable and the experimental Log P as the dependent variable then the data were analyzed statistically with the SPSS program to find the best correlation.&#x0D; Results: In this study, the result shows that the applications that have the best correlation with the experimental Log P are ACDlogP, MolLogP, and ALOGPS, with successive results of the R square are 0.928, 0.921, and 0.907, respectively. The results of this correlation are expressed by positive results and high-degree correlations are obtained.&#x0D; Conclusion: This result suggests that the Log P calculation program (ACDlogP, MolLogP, and ALOGPS) has a good correlation with the experimental Log P value in determining the lipophilicity of the compound.

Log D7.4 and plasma protein binding of synthetic cannabinoid receptor agonists and a comparison of experimental and predicted lipophilicity.

The emergence of new synthetic cannabinoid receptor agonists (SCRAs) onto the illicit drugs market continues to cause harm, and the overall availability of physicochemical and pharmacokinetic data for new psychoactive substances is lacking. The lipophilicity of 23 SCRAs and the plasma protein binding (PPB) of 11 SCRAs was determined. Lipophilicity was determined using a validated chromatographic hydrophobicity index (CHI) log D method; tested SCRAs showed moderate to high lipophilicity, with experimental log D7.4 ranging from 2.48 (AB-FUBINACA) to 4.95 (4F-ABUTINACA). These results were also compared to in silico predictions generated using seven commercially available software packages and online tools (Canvas; ChemDraw; Gastroplus; MoKa; PreADMET; SwissADME; and XlogP). Licenced, dedicated software packages provided more accurate lipophilicity predictions than those which were free or had prediction as a secondary function; however, the latter still provided competitive estimates in most cases. PPB of tested SCRAs, as determined by equilibrium dialysis, was in the upper range of the lipophilicity scale, ranging from 90.8% (ADB-BUTINACA) to 99.9% (BZO-HEXOXIZID). The high PPB of these drugs may contribute to reduced rate of clearance and extended durations of pharmacological effects compared to lesser-bound SCRAs. The presented data improve understanding of the behaviour of these drugs in the body. Ultimately, similar data and predictions may be used in the prediction of the structure and properties of drugs yet to emerge on the illicit market.

Quantitative Read-across structure-activity relationship (q-RASAR): A new approach methodology to model aquatic toxicity of organic pesticides against different fish species

We have developed quantitative toxicity prediction models for organic pesticides of agricultural importance considering different fish species using a novel quantitative Read-across structure-activity relationship (q-RASAR) approach. The current study uses experimental (Log 1/LC50) data of organic pesticides to various fish species, including Rainbow trout (RT: Oncorhynchus mykiss: 715 data points), Lepomis (LP: Lepomis macrochirus: 136 data points), and Miscellaneous (Pimephales promelas, Brachydanio rerio: 226 data points). This study has also discussed the validation of the developed models and the analysis of structural features that are important for aquatic toxicity towards fishes. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D-2D descriptors; the combined pool of RASAR and selected 0D-2D descriptors have been used to develop the final models by employing partial least squares algorithm. All the q-RASAR models are acceptable in terms of goodness of fit, robustness, and external predictivity, superseding the quality of the respective QSAR models, as seen from the computed validation metrics. The q-RASAR is an effective approach that has the potential to be used as a good alternative way to enhance external predictivity, interpretability, and transferability for aquatic toxicity prediction as well as ecotoxicity potential identification.

Collection of Partition Coefficients in Hexadecyltrimethylammonium Bromide, Sodium Cholate, and Lithium Perfluorooctanesulfonate Micellar Solutions: Experimental Determination and Computational Predictions.

This study focuses on determining the partition coefficients (logP) of a diverse set of 63 molecules in three distinct micellar systems: hexadecyltrimethylammonium bromide (HTAB), sodium cholate (SC), and lithium perfluorooctanesulfonate (LPFOS). The experimental log p values were obtained through micellar electrokinetic chromatography (MEKC) experiments, conducted under controlled pH conditions. Then, Quantum Mechanics (QM) and machine learning approaches are proposed for the prediction of the partition coefficients in these three micellar systems. In the applied QM approach, the experimentally obtained partition coefficients were correlated with the calculated values for the case of the 15 solvent mixtures. Using Density Function Theory (DFT) with the B3LYP functional, we calculated the solvation free energies of 63 molecules in these 16 solvents. The combined data from the experimental partition coefficients in the three micellar formulations showed that the 1-propanol/water combination demonstrated the best agreement with the experimental partition coefficients for the SC and HTAB micelles. Moreover, we employed the SVM approach and k-means clustering based on the generation of the chemical descriptor space. The analysis revealed distinct partitioning patterns associated with specific characteristic features within each identified class. These results indicate the utility of the combined techniques when we want an efficient and quicker model for predicting partition coefficients in diverse micelles.

Estimating the hydrophobicity extent of molecular fragments using reversed-phase liquid chromatography.

A fast HPLC method was developed to study the hydrophobicity extent of pharmaceutically relevant molecular fragments. By this strategy, the reduced amount of sample available for physico-chemical evaluations in early-phase drug discovery programs does not represent a limiting factor. The sixteen acid fragments investigated were previously synthesized also determining potentiometrically their experimental log D values. For four fragments it was not possible to determine such property since their values were outside of the instrumental working range (2<pKa <12). An RP-HPLC method was therefore optimized. For each scrutinized method, some derived chromatographic indices were calculated, and Pearson's correlation coefficient (r) allowed to select the so-called "φ0 index" as the best correlating with the log D. The was fixed at 3.5 and a modification of some variables [organic modifier (methanol vs. ACN), stationary phase (octyl vs. octadecyl), presence/absence of the additives n-octanol, n-butylamine, and n-octylamine], allowed to select the best correlation conditions, producing a r=0.94 (p<0.001). Importantly, the φ0 index enabled the estimation of log D values for four fragments which were unattainable by potentiometric titration. Moreover, a series of molecular descriptors were calculated to identify the chemical characteristics of the fragments explaining the obtained φ0 . The number of hydrogen bond donors and the index of cohesive interaction correlated with the experimental data.

Research Progress on the Electrical Properties of Gas Hydrate‐bearing Sediments

AbstractElectrical properties are important physical parameters of natural gas hydrate, and, specifically, resistivity has been widely used in the quantitative estimation of hydrate saturation. There are three main methods to study the electrical properties of gas hydrate‐bearing sediments: experimental laboratory measurements, numerical simulation, and resistivity logging. Experimental measurements can be divided into three categories: normal electrical measurement, complex resistivity measurement, and electrical resistivity tomography. Experimental measurements show that the resistivity of hydrate‐bearing sediment is affected by many factors, and its distribution as well as the hydrate saturation is not uniform; there is a distinct non‐Archie phenomenon. The numerical method can simulate the resistivity of sediments by changing the hydrate occurrence state, saturation, distribution, etc. However, it needs to be combined with X‐ray CT, nuclear magnetic resonance, and other imaging techniques to characterize the porous characteristics of the hydrate‐bearing sediments. Resistivity well logging can easily identify hydrate layers based on their significantly higher resistivity than the background, but the field data of the hydrate layer also has a serious non‐Archie phenomenon. Therefore, more experimental measurements and numerical simulation studies are needed to correct the parameters of Archie's formula.

Study of Micro Annulus Analysis of Cement Bond Evaluation by using Logging Tools

This paper outlines and measures the significance of the cement-bond-log (CBL) tool for cement evaluation programs. And these tool evaluations are often challenged, as is their ability to determine the association between the 􀁆􀁈􀁐􀁈􀁑􀁗􀂶􀁖 compressive strength and the existence of poor cementing bonding. Casing dimensions have an impact on the connection, but cement type and drying conditions have no bearing on them. Significance of certain geometrical parameters on the cement bond log's primary output, or the attenuation of a sonic wave traveling along an interface between cement and casing. For a variety of cement densities, sonic attenuation rates were connected to compressive strengths. There is a discussion of certain experimental logging samples and cementing operation details. Moreover, data connecting attenuation rates to cement densities and compressive strengths are shown.

Assessor-guided learning for continual environments

This paper proposes an assessor-guided learning strategy for continual learning where an assessor guides the learning process of a base learner by controlling the direction and pace of the learning process thus allowing an efficient learning of new environments while protecting against the catastrophic interference problem. The assessor is trained in a meta-learning manner with a meta-objective to boost the learning process of the base learner. It performs a soft-weighting mechanism of every sample accepting positive samples while rejecting negative samples. The training objective of a base learner is to minimize a meta-weighted combination of the cross entropy loss function, the dark experience replay (DER) loss function and the knowledge distillation loss function whose interactions are controlled in such a way to attain an improved performance. A compensated over-sampling (COS) strategy is developed to overcome the class imbalanced problem of the episodic memory due to limited memory budgets. Our approach, Assessor-Guided Learning Approach (AGLA), has been evaluated in the class-incremental and task-incremental learning problems. AGLA achieves improved performances compared to its competitors while the theoretical analysis of the COS strategy is offered. Source codes of AGLA, baseline algorithms and experimental logs are shared publicly in https://github.com/anwarmaxsum/AGLA for further study.

Immobilized Keratin HPLC Stationary Phase-A Forgotten Model of Transdermal Absorption: To What Molecular and Biological Properties Is It Relevant?

Chromatographic retention data collected on immobilized keratin (KER) or immobilized artificial membrane (IAM) stationary phases were used to predict skin permeability coefficient (log Kp) and bioconcentration factor (log BCF) of structurally unrelated compounds. Models of both properties contained, apart from chromatographic descriptors, calculated physico-chemical parameters. The log Kp model, containing keratin-based retention factor, has slightly better statistical parameters and is in a better agreement with experimental log Kp data than the model derived from IAM chromatography; both models are applicable primarily to non-ionized compounds.Based on the multiple linear regression (MLR) analyses conducted in this study, it was concluded that immobilized keratin chromatographic support is a moderately useful tool for skin permeability assessment.However, chromatography on immobilized keratin may also be of use for a different purpose-in studies of compounds' bioconcentration in aquatic organisms.

Improved GNNs for Log D7.4 Prediction by Transferring Knowledge from Low-Fidelity Data

The n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4) is an indicator of lipophilicity, and it influences a wide variety of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and druggability of compounds. In log D7.4 prediction, graph neural networks (GNNs) can uncover subtle structure-property relationships (SPRs) by automatically extracting features from molecular graphs that facilitate the learning of SPRs, but their performances are often limited by the small size of available datasets. Herein, we present a transfer learning strategy called pretraining on computational data and then fine-tuning on experimental data (PCFE) to fully exploit the predictive potential of GNNs. PCFE works by pretraining a GNN model on 1.71 million computational log D data (low-fidelity data) and then fine-tuning it on 19,155 experimental log D7.4 data (high-fidelity data). The experiments for three GNN architectures (graph convolutional network (GCN), graph attention network (GAT), and Attentive FP) demonstrated the effectiveness of PCFE in improving GNNs for log D7.4 predictions. Moreover, the optimal PCFE-trained GNN model (cx-Attentive FP, Rtest2 = 0.909) outperformed four excellent descriptor-based models (random forest (RF), gradient boosting (GB), support vector machine (SVM), and extreme gradient boosting (XGBoost)). The robustness of the cx-Attentive FP model was also confirmed by evaluating the models with different training data sizes and dataset splitting strategies. Therefore, we developed a webserver and defined the applicability domain for this model. The webserver (http://tools.scbdd.com/chemlogd/) provides free log D7.4 prediction services. In addition, the important descriptors for log D7.4 were detected by the Shapley additive explanations (SHAP) method, and the most relevant substructures of log D7.4 were identified by the attention mechanism. Finally, the matched molecular pair analysis (MMPA) was performed to summarize the contributions of common chemical substituents to log D7.4, including a variety of hydrocarbon groups, halogen groups, heteroatoms, and polar groups. In conclusion, we believe that the cx-Attentive FP model can serve as a reliable tool to predict log D7.4 and hope that pretraining on low-fidelity data can help GNNs make accurate predictions of other endpoints in drug discovery.

Determination of partition coefficients of phthalic acid esters between polydimethylsiloxane and water and its field application to surface waters

Phthalic acid esters (PAEs) or phthalates are endocrine-disrupting chemicals and among the most frequently detected hydrophobic organic pollutants, which can be gradually released from consumer products into the environment (e.g., water). This study measured the equilibrium partition coefficients for 10 selected PAEs, with a wide range of logarithms of the octanol-water partition coefficient (log Kow) from 1.60 to 9.37, between poly(dimethylsiloxane) (PDMS) and water (KPDMSw) using the kinetic permeation method. The desorption rate constant (kd) and KPDMSw for each PAEs were calculated from kinetic data. The experimental log KPDMSw for the PAEs ranges from 0.8 to 5.9, which is linearly correlated with log Kow values up to 8 from the literature (R2 > 0.94); however, it slightly deviated for the PAEs with log Kow values greater than 8. In addition, KPDMSw decreased with the temperature and enthalpy for PAEs partitioning in PDMS-water in an exothermic manner. Furthermore, the effects of dissolved organic matter and ionic strength on the partitioning of PAEs in PDMS were investigated. PDMS was used as a passive sampler to determine the aqueous concentration of plasticizers in river surface water. The results of this study can be used to evaluate the bioavailability and risk of phthalates in real environmental samples.