Predictive QSAR Models Research Articles

A QSAR and Pharmacophore Survey on Tyrosine Kinase Inhibitors with Effect on Epidermal Growth Factor Receptor

Background:: Tyrosine kinases are of great importance nowadays in cancer treatment. As designing new inhibitors with more potency is an optimal goal of pharmaceutical companies, using previous improvements in this area would be beneficial. One of the most popular and widely used methods is creating a QSAR model. Another useful way is to build a pharmacophoric map to address important features of inhibitors. Methods:: Upon this, a large dataset of molecules was applied to create a QSAR model for the prediction of the inhibitory activity of molecules against the epidermal growth factor receptor. Using MOE software, molecular descriptors were calculated in 3d, and a model was built. Results:: 9 descriptors were selected, which describe the energy, shape, and hydrophobicity of the molecules. A pharmacophoric map was also created, and 3 important features were selected: Hydrophobic areas, H-bond acceptor regions, and Aromatic moieties. Conclusion:: These findings proved the results obtained result from the QSAR model.

Integrated QSAR Models for Prediction of Serotonergic Activity: Machine Learning Unveiling Activity and Selectivity Patterns of Molecular Descriptors.

Understanding the features of compounds that determine their high serotonergic activity and selectivity for specific receptor subtypes represents a pivotal challenge in drug discovery, directly impacting the ability to minimize adverse events while maximizing therapeutic efficacy. Up to now, this process has been a puzzle and limited to a few serotonergic targets. One approach represented in the literature focuses on receptor structure whereas in this study, we followed another strategy by creating AI-based models capable of predicting serotonergic activity and selectivity based on ligands' representation by molecular descriptors. Predictive models were developed using Automated Machine Learning provided by Mljar and later analyzed through the SHAP importance analysis, which allowed us to clarify the relationship between descriptors and the effect on activity and what features determine selective affinity for serotonin receptors. Through the experiments, it was possible to highlight the most important features of ligands based on highly efficient models. These features are discussed in this manuscript. The models are available in the additional modules of the SerotoninAI application called "Serotonergic activity" and "Selectivity".

Carbozone Compound Corrosion Inhibition Efficiency in Relation to Quantum Chemical Parameters: A QSAR Study for Predictive Models

This article discusses the use of carbozones compounds as corrosion inhibitors and the correlation between their quantum chemical parameters and molecular dynamic parameters with their corrosion inhibition efficiency. The results show that the use of a composite index of quantum parameters can accurately describe the corrosion inhibitory performance of carbozone molecules. A predictive QSAR model can be developed using the study's statistical methodologies to predict corrosion inhibitory behavior of organic compounds and generate new corrosion inhibitors. The inclusion of adsorption energy in the nonlinear equation assist decrease the number of descriptors utilized in the model. Overall, Researchers should focus more on these criteria in future research since they have the potential to enhance results.

2,4-disubstituted 6-fluoroquinolines as potent antiplasmodial agents: QSAR, homology modeling, molecular docking and ADMET studies

ObjectiveThis work was designed to study 2,4-disubstituted 6-fluoroquinolines as antiplasmodial agents by using in silico techniques, to aid in the design of novel analogs with high potency against malaria and high inhibition of Plasmodium falciparum translation elongation factor 2 (PfeEF2), a novel drug target. MethodsQuantitative structure-activity relationships (QSAR) of 2,4-disubstituted 6-fluoroquinolines were studied with the genetic function approximation technique in Material Studio software. The 3D structure of PfeEF2 was modeled in the SWISS-MODEL workspace through homology modeling. A molecular docking study of the modeled PfeEF2 and 2,4-disubstituted 6-fluoroquinolines was conducted with Autodock Vina in Pyrx software. Furthermore, the in silico pharmacokinetic properties of selected compounds were investigated. ResultsA robust, reliable and predictive QSAR model was developed that related the chemical structures of 2,4-disubstituted 6-fluoroquinolines to their antiplasmodium activities. The model had an internal squared correlation coefficient R2 of 0.921, adjusted squared correlation coefficient R2adj of 0.878, leave-one-out cross-validation coefficient Q2cv of 0.801 and predictive squared correlation coefficient R2pred of 0.901. The antiplasmodium activity of 6-fluoroquinolines was found to depend on the n5Ring, GGI9, TDB7u, TDB8u and RDF75i physicochemical properties: n5Ring, TDB8u and RDF75i were positively associated, whereas GGI9 and TDB7u were negatively associated, with the antiplasmodium activity of the compounds. Stable complexes formed between the compounds and modeled PfeEF2, with binding affinity ranging from −8.200 to −10.700 kcal/mol. Compounds 5, 11, 16, 22 and 24 had better binding affinities than quinoline-4-carboxamide (DDD107498), as well as good pharmacokinetic properties, and therefore may be better inhibitors of this novel target. ConclusionQSAR and docking studies provided insight into designing novel 2,4-disubstituted 6-fluoroquinolines with high antiplasmodial activity and good structural properties for inhibiting a novel antimalarial drug target.

In Silico, in Vitro and in Vivo Study of Substituted Imidazolidinone Sulfonamides as Antibacterial Agents.

New substituted imidazolidinone sulfonamides have been developed using a rational drug design strategy. Predictive QSAR models for the search of new antibacterials were created using the OCHEM platform. Regression models were applied to verify a virtual chemical library of new imidazolidinone derivatives designed to have antibacterial activity. A number of substituted imidazolidinone sulfonamides as effective antibacterial agents were identified by QSAR prediction, synthesized and characterized by spectral and elemental, and tested in vitro. Six studied compounds have shown the highest in vitro antibacterial activity against Gram-negative E. coli and Gram-positive S. aureus multidrug-resistant strains. The in vivo acute toxicity of these imidazolidinone sulfonamides based on the LC50 value ranged from 16.01 to 44.35 mg/L (slightly toxic compounds class). The results of molecular docking suggest that the antibacterial mechanism of the compounds can be associated with the inhibition of post-translational modification processes of bacterial peptides and proteins.

Correlation intensity index-index of ideality of correlation: A hyphenated target function for furtherance of MAO-B inhibitory activity assessment

Monoamine oxidases are the enzymes involved in the management of brain homeostasis through oxidative deamination of monoamines such as neurotransmitters, tyramine etc. The excessive production of monoamine oxidase-B specifically results in numerous neurodegenerative disorders like Alzheimer’s and Parkinson's diseases. Inhibitors of monoamine oxidase-B are applied in the management of these disorders. Here in this article we have developed robust hybrid descriptor based QSAR models related to 123 monoamine oxidase-B inhibitors through CORAL software by means of Monte Carlo optimization method. Three target functions were applied to prepare QSAR models and three splits were made for each target function. The most reliable, robust and better predictive QSAR models were developed with TF3 (correlation intensity index -index of ideality of correlation). Correlation intensity index showed positive effect on QSAR models. The structural features obtained from the QSAR modeling were incorporated in newly designed molecules and exhibited positive effect on their endpoint. Significant binding interactions were represented by these molecules in docking studies. Molecule B5 displayed prominent pIC50 (8.3) and binding affinity (−11.5 kcal mol−1) towards monoamine oxidase-B.

First report on chemometric modeling of tilapia fish aquatic toxicity to organic chemicals: Toxicity data gap filling

The Toxic Substances Control Act (TSCA) mandates the Environmental Protection Agency (EPA) to document chemicals entering the US. Due to the vast range of toxicity endpoints, experimental toxicological study for all chemicals is impossible to conduct. To address this, in silico methods like QSAR and read-across are strategically used to prioritize testing for chemicals lacking ecotoxicity data. Aquatic toxicity is one of the most critical endpoints directly related to aquatic species, mainly fish, followed by direct to indirect effects on humans through drinking water and fish as food, respectively. Therefore, we have employed the ToxValDB database to curate acute LC50 toxicity data for three Tilapia species covering two different genera, an ideal species for aquatic toxicity testing. Employing the curated dataset, we have developed multiple robust and predictive QSAR and quantitative read-across structure-activity relationship (q-RASAR) models for Tilapia zillii, Oreochromis niloticus, and Oreochromis mossambicus which helped to understand the toxicological mode of action (MoA) of the modeled chemicals and predict the aquatic toxicity of new untested chemicals followed by toxicity data gap filling. The best three QSAR models showed encouraging statistical quality in terms of determination coefficient R2 (0.94, 0.74, and 0.77), cross-validated leave-one-out Q2 (0.90, 0.67 and 0.70), and predictive capability in terms of R2pred (0.95, 0.77, and 0.74) for T. zillii, O. niloticus, and O. mossambicus datasets, respectively. The developed best mathematical models were used for the prediction of aquatic toxicity in terms of pLC50 for 297 untested organic chemicals across three major Tilapia species ranging from 1.841 to 8.561 M in terms of environmental risk assessment.

Hybridization of molecular docking studies with machine learning based QSAR model for prediction of xanthine oxidase activity

Xanthine Oxidase inhibitors treat hyperuricemia, but associated side effects have led researchers to discover new inhibitors with fewer side effects. Our study employed a hybrid approach, i.e., combining ML-based QSAR modeling with molecular docking, to elucidate a novel inhibitor. On robust training, our ML model achieved an r2 value of 0.84 and an RMSE value of 0.39, with MACCS 41 and 62 keys influencing its performance. After validation by using various metrics, this model was deployed as a web application (https://bioactivity.streamlit.app/) and was used to predict pIC50 values of Alkannin/Shikonin derivatives sorted by molecular docking studies. Final results suggested that compounds NK-1 and NK-2 were having pIC50 value equivalent to allopurinol, and requires further evaluation for confirmation of their potential. Hence, our study will help researchers to understand the relation of molecular structure with their bioactivity.

Assessment of Structural Basis for Thiazolopyridine Derivatives as DNA Gyrase-B Inhibitors.

Tuberculosis (TB) is one of the leading causes of death in the post-COVID- 19 era. It has been observed that there is a devastating condition with a 25-30% increase in TB patients. DNA gyrase B isoform has proved its high potential to be a therapeutically effective target for developing newer and safer anti-TB agents. This study aims to identify minimum structural requirements for the optimization of thiazolopyridine derivatives having DNA gyrase inhibitory activities. Moreover, developed QSAR models could be used to design new thiazolopyridine derivatives and predict their DNA gyrase B inhibitory activity before synthesis. 3D-QSAR and Group-based QSAR (G-QSAR) methodologies were adopted to develop accurate, reliable, and predictive QSAR models. Statistical methods such as kNN-MFA SW-FB and MLR SW-FB were used to correlate dependent parameters with descriptors. Both models were thoroughly validated for internal and external predictive abilities. The 3D-QSAR model significantly correlated steric and electrostatic descriptors with q2 0.7491 and predicted r2 0.7792. The G-QSAR model showed that parameters such as SsOHE-index, slogP, ChiV5chain, and T_C_C_3 were crucial for optimizing thiazolopyridine derivatives as DNA gyrase inhibitors. The 3D-QSAR model was interpreted extensively with respect to 3D field points, and the pattern of fragmentation was studied in the G-QSAR model. The 3D-QSAR and G-QSAR models were found to be highly predictive. These models could be useful for designing potent DNA gyrase B inhibitors before their synthesis.

6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling

The application of QSAR analysis dates back a half-century ago and is currently continuously employed in any rational drug design. The multi-dimensional QSAR modeling can be a promising tool for researchers to develop reliable predictive QSAR models for designing novel compounds. In the present work, we studied inhibitors of human aldose reductase (AR) to generate multi-dimensional QSAR models using 3D- and 6D-QSAR methods. For this purpose, Pentacle and Quasar’s programs were used to produce the QSAR models using corresponding dissociation constant (Kd) values. By inspecting the performance metrics of the generated models, we achieved similar results with comparable internal validation statistics. However, considering the externally validated values, 6D-QSAR models provide significantly better prediction of endpoint values. The obtained results suggest that the higher the dimension of the QSAR model, the higher the performance of the generated model. However, more studies are required to verify these outcomes.

Exploring QSAR models for activity-cliff prediction

Introduction and methodologyPairs of similar compounds that only differ by a small structural modification but exhibit a large difference in their binding affinity for a given target are known as activity cliffs (ACs). It has been hypothesised that QSAR models struggle to predict ACs and that ACs thus form a major source of prediction error. However, the AC-prediction power of modern QSAR methods and its quantitative relationship to general QSAR-prediction performance is still underexplored. We systematically construct nine distinct QSAR models by combining three molecular representation methods (extended-connectivity fingerprints, physicochemical-descriptor vectors and graph isomorphism networks) with three regression techniques (random forests, k-nearest neighbours and multilayer perceptrons); we then use each resulting model to classify pairs of similar compounds as ACs or non-ACs and to predict the activities of individual molecules in three case studies: dopamine receptor D2, factor Xa, and SARS-CoV-2 main protease.Results and conclusionsOur results provide strong support for the hypothesis that indeed QSAR models frequently fail to predict ACs. We observe low AC-sensitivity amongst the evaluated models when the activities of both compounds are unknown, but a substantial increase in AC-sensitivity when the actual activity of one of the compounds is given. Graph isomorphism features are found to be competitive with or superior to classical molecular representations for AC-classification and can thus be employed as baseline AC-prediction models or simple compound-optimisation tools. For general QSAR-prediction, however, extended-connectivity fingerprints still consistently deliver the best performance amongs the tested input representations. A potential future pathway to improve QSAR-modelling performance might be the development of techniques to increase AC-sensitivity.Graphical

A consensual machine-learning-assisted QSAR model for effective bioactivity prediction of xanthine oxidase inhibitors using molecular fingerprints.

Xanthine oxidase inhibitors (XOIs) have been widely studied due to the promising potential as safe and effective therapeutics in hyperuricemia and gout. Currently, available XOI molecules have been developed from different experiments but they are with the wide structure diversity and significant varying bioactivities. So it is of great practical significance to present a consensual QSAR model for effective bioactivity prediction of XOIs based on a systematic compiling of these XOIs across different experiments. In this work, 249 XOIs belonging to 16 scaffolds were collected and were integrated into a consensual dataset by introducing the concept of IC50 values relative to allopurinol (RIC50). Here, extended connectivity fingerprints (ECFPs) were employed to represent XOI molecules. By performing effective feature selection by machine-learning method, 54 crucial fingerprints were indicated to be valuable for predicting the inhibitory potency (IP) of XOIs. The optimal predictor yields the promising performance by different cross-validation tests. Besides, an external validation of 43 XOIs and a case study on febuxostat also provide satisfactory results, indicating the powerful generalization of our predictor. Here, the predictor was interpreted by shapely additive explanation (SHAP) method which revealed several important substructures by mapping the featured fingerprints to molecular structures. Then, 15 new molecules were designed and predicted by our predictor to show superior IP than febuxostat. Finally, molecular docking simulation was performed to gain a deep insight into molecular binding mode with xanthine oxidase (XO) enzyme, showing that molecules with selenazole moiety, cyano group and isopropyl group tended to yield higher IP. The absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction results further enhanced the potential of these novel XOIs as drug candidates. Overall, this work presents a QSAR model for accurate prediction of IP of XOIs, and is expected to provide new insights for further structure-guided design of novel XOIs.

Computer-Aided drug design of new 2-amino-thiophene derivatives as anti-leishmanial agents

The leishmaniasis is a neglected disease caused by a group of protozoan parasites from the genus Leishmania whose treatment is limited, obsolete, toxic, and ineffective in certain cases. These characteristics motivate researchers worldwide to plan new therapeutic alternatives for the treatment of leishmaniasis, where the use of cheminformatics tools applied to computer-assisted drug design has allowed research to make great advances in the search for new drugs candidates. In this study, a series of 2-amino-thiophene (2-AT) derivatives was screened virtually using QSAR tools, ADMET filters and prediction models, allowing direct the synthesis of compounds, which were evaluated in vitro against promastigotes and axenic amastigotes of Leishmania amazonensis. The combination of different descriptors and machine learning methods led to obtaining robust and predictive QSAR models, which was obtained from a dataset composed of 1862 compounds extracted from the ChEMBL database, with correct classification rates ranging from 0.53 (for amastigotes) to 0.91 (for promastigotes), allowing to select eleven 2-AT derivatives, which do not violate Lipinski's rules, exhibit good druglikeness, and with probability ≤70% of potential activity against the two evolutionary forms of the parasite. All compounds were properly synthesized and 8 of them were shown to be active at least against one of the evolutionary forms of the parasite with IC50 values lower than 10 μM, being more active than the reference drug meglumine antimoniate, and showing low or no citotoxicity against macrophage J774.A1 for the most part. Compounds 8CN and DCN-83, respectively, are the most active against promastigote and amastigote forms, with IC50 values of 1.20 and 0.71 μM, and selectivity indexes (SI) of 36.58 and 119.33. Structure Activity Relationship (SAR) study was carried out and allowed to identify some favorable and/or essential substitution patterns for the leishmanial activity of 2-AT derivatives. Taken together, these findings demonstrate that the use of ligand-based virtual screening proved to be quite effective and saved time, effort, and money in the selection of potential anti-leishmanial agents, and confirm, once again that 2-AT derivatives are promising hit compounds for the development of new anti-leishmanial agents.

In Silico Design and Experimental Validation of Novel Oxazole Derivatives Against Varicella zoster virus.

Varicella zoster virus (VZV) infection causes severe disease such as chickenpox, shingles, and postherpetic neuralgia, often leading to disability. Reactivation of latent VZV is associated with a decrease in specific cellular immunity in the elderly and in patients with immunodeficiency. However, due to the limited efficacy of existing therapy and the emergence of antiviral resistance, it has become necessary to develop new and effective antiviral drugs for the treatment of diseases caused by VZV, particularly in the setting of opportunistic infections. The goal of this work is to identify potent oxazole derivatives as anti-VZV agents by machine learning, followed by their synthesis and experimental validation. Predictive QSAR models were developed using the Online Chemical Modeling Environment (OCHEM). Data on compounds exhibiting antiviral activity were collected from the ChEMBL and uploaded in the OCHEM database. The predictive ability of the models was tested by cross-validation, giving coefficient of determination q2 = 0.87-0.9. The validation of the models using an external test set proves that the models can be used to predict the antiviral activity of newly designed and known compounds with reasonable accuracy within the applicability domain (q2 = 0.83-0.84). The models were applied to screen a virtual chemical library with expected activity of compounds against VZV. The 7 most promising oxazole derivatives were identified, synthesized, and tested. Two of them showed activity against the VZV Ellen strain upon primary in vitro antiviral screening. The synthesized compounds may represent an interesting starting point for further development of the oxazole derivatives against VZV. The developed models are available online at OCHEM http://ochem.eu/article/145978 and can be used to virtually screen for potential compounds with anti-VZV activity.

Cheminformatics analysis of chemicals that increase estrogen and progesterone synthesis for a breast cancer hazard assessment

Factors that increase estrogen or progesterone (P4) action are well-established as increasing breast cancer risk, and many first-line treatments to prevent breast cancer recurrence work by blocking estrogen synthesis or action. In previous work, using data from an in vitro steroidogenesis assay developed for the U.S. Environmental Protection Agency (EPA) ToxCast program, we identified 182 chemicals that increased estradiol (E2up) and 185 that increased progesterone (P4up) in human H295R adrenocortical carcinoma cells, an OECD validated assay for steroidogenesis. Chemicals known to induce mammary effects in vivo were very likely to increase E2 or P4 synthesis, further supporting the importance of these pathways for breast cancer. To identify additional chemical exposures that may increase breast cancer risk through E2 or P4 steroidogenesis, we developed a cheminformatics approach to identify structural features associated with these activities and to predict other E2 or P4 steroidogens from their chemical structures. First, we used molecular descriptors and physicochemical properties to cluster the 2,012 chemicals screened in the steroidogenesis assay using a self-organizing map (SOM). Structural features such as triazine, phenol, or more broadly benzene ramified with halide, amine or alcohol, are enriched for E2 or P4up chemicals. Among E2up chemicals, phenol and benzenone are found as significant substructures, along with nitrogen-containing biphenyls. For P4up chemicals, phenol and complex aromatic systems ramified with oxygen-based groups such as flavone or phenolphthalein are significant substructures. Chemicals that are active for both E2up and P4up are enriched with substructures such as dihydroxy phosphanedithione or are small chemicals that contain one benzene ramified with chlorine, alcohol, methyl or primary amine. These results are confirmed with a chemotype ToxPrint analysis. Then, we used machine learning and artificial intelligence algorithms to develop and validate predictive classification QSAR models for E2up and P4up chemicals. These models gave reasonable external prediction performances (balanced accuracy ~ 0.8 and Matthews Coefficient Correlation ~ 0.5) on an external validation. The QSAR models were enriched by adding a confidence score that considers the chemical applicability domain and a ToxPrint assessment of the chemical. This profiling and these models may be useful to direct future testing and risk assessments for chemicals related to breast cancer and other hormonally-mediated outcomes.

Predictive QSAR models for the toxicity of Phenols

Toxicity data for the 50% growth inhibitory concentration against Tetrahymena pyriformis pCIC50 = -logCIC50 for 85 phenols substituted were obtained experimentally. Log (CIC50)-1 along with the hydrophobicity, the logarithm of the 1-octanol/water partition coefficient (log Kow), and R2u (GETAWAY descriptors). The entire data set was randomly split into a training set (60chemicals) used to establish the QSAR model, and a test set (25 chemicals) for statistical external validation The descriptors models were selected from an extensive set of several descriptors (topological, geometrical and quantum). Quantitative structure-activity/property (QSAR / The values of the statistical parameters obtained from the multiple linear regression analysis (R²=95.5%, Q²=95.01%, S=0.157, F=604.34, P=0, SDEC=0.153, SDEP=0.161, Q²ext=95.96%, SDEPext=0.153) testify to the good fit of the model.

Two dimensional-QSAR and molecular dynamics studies of a selected class of aldoxime- and hydroxy-functionalized chalcones as monoamine oxidase-B inhibitors

Candidates generated from unsaturated ketone (chalcone) demonstrated as strong, reversible and specific monoamine oxidase-B (MAO-B) inhibitory activity. For the research on MAO-B inhibition, our team has synthesized and evaluated a panel of aldoxime-chalcone ethers (ACE) and hydroxylchalcones (HC). The MAO-B inhibitory activity of several candidates is in the micro- to nanomolar range in these series. The purpose of this research was to develop predictive QSAR models and look into the relation between MAO-B inhibition by aldoxime and hydroxyl-functionalized chalcones. It was shown that the molecular descriptors ETA Shape P, MDEO-12, ETA dBetaP, SpMax1 Bhi and ETA EtaP B are significant in the inhibitory action of the MAO-B target. Using the current 2D QSAR models, potential chalcone-based MAO-B inhibitors might be created. The lead molecules were further analyzed by the detailed molecular dynamics study to establish the stability of the ligand–enzyme complex. Communicated by Ramaswamy H. Sarma

Exploring the Gallic and Cinnamic Acids Chimeric Derivatives as Anticancer Agents over HeLa Cell Line: An in silico and in vitro Study.

Cervical cancer is one of the most aggressive and important cancer types in the female population, due to its low survival rate. Actually, the search for new bioactive compounds, like gallic and cinnamic acid, is one of the most employed options to finding a treatment. In the present study, 134 phenolic compounds with cytotoxic activity over HeLa cell line were used to generate a descriptive ( =0.76) and predictive ( =0.69 and =0.62) QSAR model. Structural, electronic, steric, and hydrophobic features are represented as different molecular descriptors in our QSAR model. From this model, nine gallate-cinnamate ester derivatives (N1-N9) were designed and synthesized. Furthermore, in vitro cytotoxic activity was evaluated against HeLa and non-tumorigenic cells. Derivatives N6, N5, N1, and N9 were the most active molecules with IC50ExpHeLa values from 7.26 to 11.95 μM. Finally, the binding of the synthesized compounds to the colchicine binding site on tubulin was evaluated by molecular docking as a possible action mechanism. N1, N5 and N6 can be considered as templates for the design of new cervical anticancer compounds.

Automated QSPR modeling and data curation of physicochemical properties using KNIME platform: Prediction of partition coefficients

In the last two decades, the volumes of chemical and biological data are constantly increasing. The problem of converting data sets into knowledge is both expensive and time-consuming, as a result a workflow technology with platforms such as KNIME, was built up to facilitate searching through multiple heterogeneous data sources and filtering for specific criteria then extracting hidden information from these large data. Before any QSAR modeling, a manual data curation is extremely recommended. However, this can be done, for small datasets, but for the extensive data accumulated recently in public databases a manual process of big data will be hardly feasible. In this work, we suggest using KNIME as an automated solution for workflow in data curation, development, and validation of predictive QSAR models from a huge dataset.In this study, we used 250250 structures from NCI database, only 3520 compounds could successfully pass through our workflow safely with their corresponding experimental log P, this property was investigated as a case study, to improve some existing log P calculation algorithms.

QSAR analysis of the partitioning of terpenes and terpenoids into human milk

AbstractStudies have shown that olfactory experience during breastfeeding plays an important role in the later development of certain food preferences in life. Thus, the aim of this study was to predict partitioning of odorous terpenes and terpenoids into breast milk from a predictive QSAR model for drug transfer. A large heterogenous data set based on drugs and their active metabolites that were used to build a QSAR was collected from the literature. Due to the vast structural diversity of these compounds and possibly different mechanisms involved in M/P partitioning, a non‐linear artificial neural network (ANN) model was used to develop a predictive QSAR model. The value of the correlation coefficient of predicted versus experimentally measured M/P values for the final model (14‐2‐1) was high (R = .82). The descriptors selected in the final model (14‐2‐1) belong to 3 main categories: (a) solubility/permeability descriptors (dipole moment, polar surface area, aromatic ring count and hydroxyl group count), (b) reactivity descriptors (i.e. HOMO energy) and (c) shape descriptors (different ring size counts, counts of methyl groups and molecular depth). Results of this study predict that many volatile terpenes from the essential oils are transferred into breast milk selectively. The highest M/P values (>3.5) were predicted for β‐caryophyllene, aromadendrene, alloaromadendrene, and 1,4‐ and 1,8‐cineole, high values for carvacrol (M/P = 3.2), eugenol (M/P = 3.0) and thymol (M/P = 3.6), and moderate values for α‐pinene (M/P = 2.3) and low values (M/P = 0.4) for phellandrene and limonene. Our model helps to explain and expand on the current knowledge of volatile compounds in breast milk by predicting that a variety of volatile terpenoids can be found in breast milk.