cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12998741.v1
title: Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines

description: Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.
DOI: 10.6084/m9.figshare.12998741
title: Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines

description: Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.
DOI: 10.6084/m9.figshare.13602894
title: Computational identification of chemical compounds with potential anti-Chagas activity using a classification tree

description: Chagas disease is endemic to 21 Latin American countries and is a great public health problem in that region. Current chemotherapy remains unsatisfactory; consequently the need to search for new drugs persists. Here we present a new approach to identify novel compounds with potential anti-chagasic action. A large dataset of 584 compounds, obtained from the Drugs for Neglected Diseases initiative, was selected to develop the computational model. Dragon software was used to calculate the molecular descriptors and WEKA software to obtain the classification tree. The best model shows accuracy greater than 93.4% for the training set; the tree was also validated using a 10-fold cross-validation procedure and through a test set, achieving accuracy values over 90.5% and 92.2%, correspondingly. The values of sensitivity and specificity were around 90% in all series; also the false alarm rate values were under 10.5% for all sets. In addition, a simulated ligand-based virtual screening for several compounds recently reported as promising anti-chagasic agents was carried out, yielding good agreement between predictions and experimental results. Finally, the present work constitutes an example of how this rational computer-based method can help reduce the cost and increase the rate in which novel compounds are developed against Chagas disease.
DOI: 10.6084/m9.figshare.13602894.v1
title: Computational identification of chemical compounds with potential anti-Chagas activity using a classification tree

description: Chagas disease is endemic to 21 Latin American countries and is a great public health problem in that region. Current chemotherapy remains unsatisfactory; consequently the need to search for new drugs persists. Here we present a new approach to identify novel compounds with potential anti-chagasic action. A large dataset of 584 compounds, obtained from the Drugs for Neglected Diseases initiative, was selected to develop the computational model. Dragon software was used to calculate the molecular descriptors and WEKA software to obtain the classification tree. The best model shows accuracy greater than 93.4% for the training set; the tree was also validated using a 10-fold cross-validation procedure and through a test set, achieving accuracy values over 90.5% and 92.2%, correspondingly. The values of sensitivity and specificity were around 90% in all series; also the false alarm rate values were under 10.5% for all sets. In addition, a simulated ligand-based virtual screening for several compounds recently reported as promising anti-chagasic agents was carried out, yielding good agreement between predictions and experimental results. Finally, the present work constitutes an example of how this rational computer-based method can help reduce the cost and increase the rate in which novel compounds are developed against Chagas disease.
DOI: 10.6084/m9.figshare.12911245
title: Modelling quantitative structure activity–activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation

description: Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model for direct prediction of the toxicity of a larger set of compounds, combing the application of an already predicted model for another species, and molecular descriptors. We investigated a large acute toxicity data set of five aquatic organisms, fish, <i>Daphnia magna</i>, and algae from the VEGA-Hub, as well as <i>Tetrahymena pyriformis</i> and <i>Vibrio fischeri</i>. The statistical quality of the models encouraged us to consider this alternative for the prediction of toxicity using interspecies extrapolation QSAAR models without regard to the toxicity mechanism or chemical class. In the case of algae, the use of activity values from a second species did not improve the models. This can be attributed to the weak interspecies relationships, due to different aquatic toxicity mechanisms in species.
DOI: 10.6084/m9.figshare.12911245.v1
title: Modelling quantitative structure activity–activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation

description: Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model for direct prediction of the toxicity of a larger set of compounds, combing the application of an already predicted model for another species, and molecular descriptors. We investigated a large acute toxicity data set of five aquatic organisms, fish, <i>Daphnia magna</i>, and algae from the VEGA-Hub, as well as <i>Tetrahymena pyriformis</i> and <i>Vibrio fischeri</i>. The statistical quality of the models encouraged us to consider this alternative for the prediction of toxicity using interspecies extrapolation QSAAR models without regard to the toxicity mechanism or chemical class. In the case of algae, the use of activity values from a second species did not improve the models. This can be attributed to the weak interspecies relationships, due to different aquatic toxicity mechanisms in species.
DOI: 10.6084/m9.figshare.13674082
title: SAR and QSAR research on tyrosinase inhibitors using machine learning methods

description: Tyrosinase is a key rate-limiting enzyme in the process of melanin synthesis, which is closely related to human pigmentation disorders. Tyrosinase inhibitors can down-regulate tyrosinase to effectively reduce melanin synthesis. In this work, we conducted structure-activity relationship (SAR) study on 1097 diverse mushroom tyrosinase inhibitors. We applied five kinds of machine learning methods to develop 15 classification models. Model 5B built by fully connected neural networks and ECFP4 fingerprints achieved the highest prediction accuracy of 91.36% and Matthews correlation coefficient (MCC) of 0.81 on the test set. The applicability domains (AD) of classification models were defined by dSTD−PRO method. Moreover, we clustered the 1097 inhibitors into eight subsets by K-Means to figure out inhibitors’ structural features. In addition, 10 quantitative structure–activity relationship (QSAR) models were constructed by four machine learning methods based on 813 inhibitors. Model 6 J, the best QSAR model, was developed by fully connected neural networks with 50 RDKit descriptors. It resulted in a coefficient of determination (<i>r</i><sup>2</sup>) of 0.770 and a root mean squared error (RMSE) of 0.482 on the test set. The AD of Model 6 J was visualized by Williams plot. The models built in this study can be obtained from the authors.
DOI: 10.6084/m9.figshare.13674082.v1
title: SAR and QSAR research on tyrosinase inhibitors using machine learning methods

description: Tyrosinase is a key rate-limiting enzyme in the process of melanin synthesis, which is closely related to human pigmentation disorders. Tyrosinase inhibitors can down-regulate tyrosinase to effectively reduce melanin synthesis. In this work, we conducted structure-activity relationship (SAR) study on 1097 diverse mushroom tyrosinase inhibitors. We applied five kinds of machine learning methods to develop 15 classification models. Model 5B built by fully connected neural networks and ECFP4 fingerprints achieved the highest prediction accuracy of 91.36% and Matthews correlation coefficient (MCC) of 0.81 on the test set. The applicability domains (AD) of classification models were defined by dSTD−PRO method. Moreover, we clustered the 1097 inhibitors into eight subsets by K-Means to figure out inhibitors’ structural features. In addition, 10 quantitative structure–activity relationship (QSAR) models were constructed by four machine learning methods based on 813 inhibitors. Model 6 J, the best QSAR model, was developed by fully connected neural networks with 50 RDKit descriptors. It resulted in a coefficient of determination (<i>r</i><sup>2</sup>) of 0.770 and a root mean squared error (RMSE) of 0.482 on the test set. The AD of Model 6 J was visualized by Williams plot. The models built in this study can be obtained from the authors.
DOI: 10.6084/m9.figshare.12849518.v1
title: Effect of the structural factors of organic compounds on the acute toxicity toward <i>Daphnia magna</i>

description: The acute toxicity of organic compounds towards <i>Daphina magna</i> was subjected to QSAR analysis. The two-dimensional simplex representation of molecular structure (2D SiRMS) and the support vector machine (SVM), gradient boosting (GBM) methods were used to develop QSAR models. Adequate regression QSAR models were developed for incubation of 24 h. Their interpretation allowed us to quantitatively describe and rank the well-known toxicophores, to refine their molecular surroundings, and to distinguish the structural derivatives of the fragments that significantly contribute to the acute toxicity (LC<sub>50</sub>) of organic compounds towards <i>D. magna</i>. Based on the results of the interpretation of the regression models, a molecular design (modification) of highly toxic compounds was performed in order to reduce their hazard. In addition, acceptable classification QSAR models were developed to reliably predict the following mode of action (MOA): specific and non-specific toxicity of organic compounds towards <i>D. magna</i>. When interpreting these models, we were able to determine the structural fragments and the physicochemical characteristics of molecules that are responsible for the manifestation of one of the modes of action. The on-line version of the OCHEM expert system (https://ochem.eu), HYBOT descriptors, and the random forest and SVM methods were used for a comparative QSAR investigation.
DOI: 10.6084/m9.figshare.12849518
title: Effect of the structural factors of organic compounds on the acute toxicity toward <i>Daphnia magna</i>

description: The acute toxicity of organic compounds towards <i>Daphina magna</i> was subjected to QSAR analysis. The two-dimensional simplex representation of molecular structure (2D SiRMS) and the support vector machine (SVM), gradient boosting (GBM) methods were used to develop QSAR models. Adequate regression QSAR models were developed for incubation of 24 h. Their interpretation allowed us to quantitatively describe and rank the well-known toxicophores, to refine their molecular surroundings, and to distinguish the structural derivatives of the fragments that significantly contribute to the acute toxicity (LC<sub>50</sub>) of organic compounds towards <i>D. magna</i>. Based on the results of the interpretation of the regression models, a molecular design (modification) of highly toxic compounds was performed in order to reduce their hazard. In addition, acceptable classification QSAR models were developed to reliably predict the following mode of action (MOA): specific and non-specific toxicity of organic compounds towards <i>D. magna</i>. When interpreting these models, we were able to determine the structural fragments and the physicochemical characteristics of molecules that are responsible for the manifestation of one of the modes of action. The on-line version of the OCHEM expert system (https://ochem.eu), HYBOT descriptors, and the random forest and SVM methods were used for a comparative QSAR investigation.
DOI: 10.6084/m9.figshare.12849509
title: Quantitative structure–property relationship of distribution coefficients of organic compounds

description: The <i>n</i>-octanol/buffer solution distribution coefficient (or <i>n</i>-octanol/water partition coefficient) is of critical importance for measuring lipophilicity of drug candidates. After 4885 molecular descriptor generation, 15 molecular descriptors were selected to develop quantitative structure–property relationship (QSPR) models for distribution coefficients at pH 7.4 (log <i>D</i><sub>7.4</sub>) of a large data set consisting of 1043 organic compounds, which was divided into a training set (600 compounds) and a test set (443 compounds). Support vector machine (SVM) based on genetic algorithm was used to develop a model for log <i>D</i><sub>7.4</sub> that has coefficient of determination <i>r</i><sup>2</sup> of 0.919 for the training set and 0.893 for the test set. The results suggest that the SVM model is accurate in predicting log <i>D</i><sub>7.4</sub>.
DOI: 10.6084/m9.figshare.12849509.v1
title: Quantitative structure–property relationship of distribution coefficients of organic compounds

description: The <i>n</i>-octanol/buffer solution distribution coefficient (or <i>n</i>-octanol/water partition coefficient) is of critical importance for measuring lipophilicity of drug candidates. After 4885 molecular descriptor generation, 15 molecular descriptors were selected to develop quantitative structure–property relationship (QSPR) models for distribution coefficients at pH 7.4 (log <i>D</i><sub>7.4</sub>) of a large data set consisting of 1043 organic compounds, which was divided into a training set (600 compounds) and a test set (443 compounds). Support vector machine (SVM) based on genetic algorithm was used to develop a model for log <i>D</i><sub>7.4</sub> that has coefficient of determination <i>r</i><sup>2</sup> of 0.919 for the training set and 0.893 for the test set. The results suggest that the SVM model is accurate in predicting log <i>D</i><sub>7.4</sub>.
DOI: 10.6084/m9.figshare.12849494
title: Development of a simple, interpretable and easily transferable QSAR model for quick screening antiviral databases in search of novel 3C-like protease (3CLpro) enzyme inhibitors against SARS-CoV diseases

description: In the context of recently emerged pandemic of COVID-19, we have performed two-dimensional quantitative structure-activity relationship (2D-QSAR) modelling using SARS-CoV-3CLpro enzyme inhibitors for the development of a multiple linear regression (MLR) based model. We have used 2D descriptors with an aim to develop an easily interpretable, transferable and reproducible model which may be used for quick prediction of SAR-CoV-3CLpro inhibitory activity for query compounds in the screening process. Based on the insights obtained from the developed 2D-QSAR model, we have identified the structural features responsible for the enhancement of the inhibitory activity against 3CLpro enzyme. Moreover, we have performed the molecular docking analysis using the most and least active molecules from the dataset to understand the molecular interactions involved in binding, and the results were then correlated with the essential structural features obtained from the 2D-QSAR model. Additionally, we have performed in silico predictions of SARS-CoV 3CLpro enzyme inhibitory activity of a total of 50,437 compounds obtained from two anti-viral drug databases (CAS COVID-19 antiviral candidate compound database and another recently reported list of prioritized compounds from the ZINC15 database) using the developed model and provided prioritized compounds for experimental detection of their performance for SARS-CoV 3CLpro enzyme inhibition.
DOI: 10.6084/m9.figshare.12849494.v1
title: Development of a simple, interpretable and easily transferable QSAR model for quick screening antiviral databases in search of novel 3C-like protease (3CLpro) enzyme inhibitors against SARS-CoV diseases

description: In the context of recently emerged pandemic of COVID-19, we have performed two-dimensional quantitative structure-activity relationship (2D-QSAR) modelling using SARS-CoV-3CLpro enzyme inhibitors for the development of a multiple linear regression (MLR) based model. We have used 2D descriptors with an aim to develop an easily interpretable, transferable and reproducible model which may be used for quick prediction of SAR-CoV-3CLpro inhibitory activity for query compounds in the screening process. Based on the insights obtained from the developed 2D-QSAR model, we have identified the structural features responsible for the enhancement of the inhibitory activity against 3CLpro enzyme. Moreover, we have performed the molecular docking analysis using the most and least active molecules from the dataset to understand the molecular interactions involved in binding, and the results were then correlated with the essential structural features obtained from the 2D-QSAR model. Additionally, we have performed in silico predictions of SARS-CoV 3CLpro enzyme inhibitory activity of a total of 50,437 compounds obtained from two anti-viral drug databases (CAS COVID-19 antiviral candidate compound database and another recently reported list of prioritized compounds from the ZINC15 database) using the developed model and provided prioritized compounds for experimental detection of their performance for SARS-CoV 3CLpro enzyme inhibition.
DOI: 10.6084/m9.figshare.12971780.v1
title: Isomer-specific molecular interaction of cationic glucosamine with benzoate and phthalates: individual stabilities of unresolvable-isomer complexes detected by NMR titration

description: Some biogenic compounds undergo conformational isomerism, and the isomers may interact distinctively with other substances to form molecular complexes individually with different stabilities. In the present work, such molecular interaction specific to isomers was confirmed for the complexation of cationic glucosamine anomers with benzoate and phthalate anions. The individual stability constants of the anomer complexes were determined by tracing <sup>1</sup>H NMR shifts of each anomer in titration with the appropriate carboxylate. In the complexes of benzoate, phthalate and terephthalate, the binding forces in the β-anomer complexes are two to five times as strong as those of the corresponding α-anomer complexes. The formulae of titration curves for determining individual stabilities have been developed in a generalised form to be applicable to the complexation of other unresolvable isomers with different compositions.
DOI: 10.6084/m9.figshare.12849542.v1
title: Conformationally mobile acyclic cucurbit[n]uril-type receptors derived from an S-shaped methylene bridged glycoluril pentamer

description: We report the synthesis of the conformationally mobile S-shaped glycoluril pentamer building block <b>3a</b> and two new acyclic CB[n]-type receptors <b>P1</b> and <b>P2. P1</b> (9 mM) and <b>P2</b> (11 mM) have moderate aqueous solubility but their host•guest complexes are poorly soluble. Host <b>P1</b> does not undergo intermolecular self-association whereas <b>P2</b> does (K<sub>s</sub> = 189 ± 27 M<sup>−1</sup>). <sup>1</sup> H NMR titrations show that <b>P1</b> and <b>P2</b> are poor hosts towards hydrophobic (di)cations <b>6</b>–<b>11</b> (<b>P1</b>: K<sub>a</sub> = 375–1400 M<sup>−1</sup>; <b>P2</b>: K<sub>a</sub> = 1950–19,800 M<sup>−1</sup>) compared to <b>Tet1</b> and <b>Tet2</b> (<b>Tet1</b>: K<sub>a</sub> = 3.09 x 10<sup>6</sup> to 4.69 × 10<sup>8</sup> M<sup>−1</sup>; <b>Tet2</b>: K<sub>a</sub> = 4.59 x 10<sup>8</sup> to 1.30 × 10<sup>10</sup> M<sup>−1</sup>). Molecular modelling shows that <b>P1</b> and <b>P2</b> exist as a mixture of three different conformers due to the two S-shaped methylene bridged glycoluril dimer subunits that each possess two different conformations. The lowest energy conformers of <b>P1</b> and <b>P2</b> do not feature a well-defined central cavity. In the presence of guests, <b>P2</b> adapts its conformation to form 1:1 <b>P2</b>•guest complexes; the binding free energy pays the energetic price of conformer selection. This energetically unfavourable conformer selection results in significantly decreased K<sub>a</sub> values of <b>P1</b> and <b>P2</b> compared to <b>Tet1</b> and <b>Tet2</b>.