Tanimoto Similarity Index Research Articles

Abstract 4937: Potential therapeutic targets with anti-inflammatory activity spectrum(COX-2/PGE2) of compounds associated with anti-cancer activityagainst oral and colorectal cancers

Abstract Introduction: COX-2 and PGE2 are prevalent inflammatory cytokines that exert influence on several cancer-related pathways throughout the gastrointestinal tract, spanning from oral cancers to colorectal cancers. One of the tactics being developed for the treatment of oral and colorectal malignancies is the identification of drugs that can selectively target receptors associated with COX-2 and PGE2. Materials and Methods: The objective was to identify potential therapeutic targets for oral cancer and colorectal cancer, specifically focusing on drugs that act as inhibitors or antagonists of the COX2/PGE2 pathway and its related receptor. A method for systematic data mining was created with a total of 32 targets associated with the COX-2/PGE2 pathway which were identified using the KEGG database. These targets were shown to be linked to both the oral cancer pathway and the colorectal cancer pathway. The compounds of interest were obtained from the Chembl25 database and their similarity was assessed using the Tanimoto similarity index, which was generated based on the Morgan fingerprints. The study employed molecular docking techniques to examine the interaction between the chosen drugs and the anticipated targets. A systematic data mining approach and computational methodology were devised utilizing the KNIME platform. Results: A total of 16,900 compounds were extracted for a set of 32 targets. Following the implementation of rule-based filters, the number of compounds was reduced to 13,800. The compounds that have a similarity index greater than 0.7 were chosen for inclusion in final analysis. Among various targets examined, empirical evidence was found to support the inhibition of expected target for PI-3 kinase-mTOR, GSK-3CD1, GSK3-AKT and PI3kinase-AKT pair. However, no evidence was found to support the inhibition of GSK3-Ikappa B-kinase. In order to examine the likelihood of the compound's interaction with anticipated target, molecular docking was employed to explore the interaction of one inhibitor compound for each pair. This investigation was conducted in contrast to the existing inhibitor compound and the subsequent findings were analyzed and discussed. Conclusion: The implemented data mining technique has demonstrated a notable decrease in the duration needed for comparable investigations, enabling the execution of experiments including numerous targets and a substantial number of ligands within a cumulative timeframe of 30 minutes. The findings of this study have the potential to be utilized in the identification of targets and the discovery of lead compounds for the purpose of managing oral as well as colorectal malignancies. Citation Format: Saravanan Sampoornam Pape. Potential therapeutic targets with anti-inflammatory activity spectrum(COX-2/PGE2) of compounds associated with anti-cancer activityagainst oral and colorectal cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4937.

In Silico Prediction of ADMET/Drug-likeness Properties of Bioactive Phloroglucinols from Hypericum genus.

Dimeric acylphloroglucinols occurring in species from sections Brathys and Trigynobrathys of the genus Hypericum exhibit acylfilicinic acid and acylphloroglucinol moieties linked by a methylene bridge. However, this chemical feature differs from hyperforin, from H. perforatum (Hypericum section), some dimeric acylphloroglucinols, such as uliginosin B, display similar pharmacological activities, namely antidepressant and antinociceptive. However, there is no knowledge about the pharmacokinetic profile and no toxicity studies of these compounds in intact mammals. to perform an in silico evaluation of the similarity, pharmacokinetics and toxicity (ADMET) properties of dimeric acylphloroglucinols from species native to Central and South America. ADMET prediction of eleven elected phloroglucinols followed by the chemical space evaluation of thirty-five dimeric acylphloroglucinols derivatives labeled according to their prenylation/geranylation pattern through principal component analysis (PCA). The similarity analysis was performed using the Tanimoto similarity index. ADMET properties were predicted with the open-source software SwissADME and pkCSM-pharmacokinetics. Several compounds showed good human intestinal absorption. However, they may present difficulties in crossing the blood-brain barrier, probably due to the high tPSA values. The predicted toxicity parameters indicated that most compounds have low toxicity. Most non-prenylated phloroglucinols were disposed into Lipinski's rule limits. Uliginosin B, isouliginosin B and japonica seem to be druglike compounds. The PCA model explained 77.49% of the total variance, and molecular similarity analyses revealed some expected similarities between isomers and different compounds. dimeric acylphloroglucinols may be promising drug candidates and deserve further pharmacological and medicinal chemistry studies.

Discovery of inhibitors of protein tyrosine phosphatase 1B contained in a natural products library from Mexican medicinal plants and fungi using a combination of enzymatic and in silico methods*.

This work aimed to discover protein tyrosine phosphatase 1B (PTP1B) inhibitors from a small molecule library of natural products (NPs) derived from selected Mexican medicinal plants and fungi to find new hits for developing antidiabetic drugs. The products showing similar IC50 values to ursolic acid (UA) (positive control, IC50 = 26.5) were considered hits. These compounds were canophyllol (1), 5-O-(β-D-glucopyranosyl)-7-methoxy-3',4'-dihydroxy-4-phenylcoumarin (2), 3,4-dimethoxy-2,5-phenanthrenediol (3), masticadienonic acid (4), 4',5,6-trihydroxy-3',7-dimethoxyflavone (5), E/Z vermelhotin (6), tajixanthone hydrate (7), quercetin-3-O-(6″-benzoyl)-β-D-galactoside (8), lichexanthone (9), melianodiol (10), and confusarin (11). According to the double-reciprocal plots, 1 was a non-competitive inhibitor, 3 a mixed-type, and 6 competitive. The chemical space analysis of the hits (IC50 < 100μM) and compounds possessing activity (IC50 in the range of 100-1,000μM) with the BIOFACQUIM library indicated that the active molecules are chemically diverse, covering most of the known Mexican NPs' chemical space. Finally, a structure-activity similarity (SAS) map was built using the Tanimoto similarity index and PTP1B absolute inhibitory activity, which allows the identification of seven scaffold hops, namely, compounds 3, 5, 6, 7, 8, 9, and 11. Canophyllol (1), on the other hand, is a true analog of UA since it is an SAR continuous zone of the SAS map.

Feature Sampling based on Multilayer Perceptive Neural Network for image quality assessment

Image quality assessment (IQA) has a vital issue in image processing to measure the perceptual quality of the image. This aims at the human visual system (HVS) by viewing for distorted images from reference images. HVS is the ultimate receiver of visual data in major applications. But, quality assessment is generally a time-consuming and luxurious method. In our work, the novel Bilateral Smoothing Filtered Feature Sampling-Based Tanimoto Index’ve Multilayer Perceptive Neural Network (BSFFS-TIMPNN) technique is developed. The main contribution of the designed BSFFS-TIMPNN technique is for enhancing the quality of the assessment performance with full reference and no reference images with multiple layers. At first, the original and reference image is taken to the input layer and sent to the first hidden layer. Followed by, image preprocessing is performed in the first hidden layer by using the novelty of a nonlinear bilateral smoothing filtering technique for eradicating noisy pixels with a higher-quality image. Next, patch extraction is executed in the second hidden layer with the novelty of the nearest neighbor sampling approach to split images into various patches. Then, the different features of the image such as shape, color, and texture are extracted in the third hidden layer. Finally, classification is carried out output layer by applying the innovation of the Tanimoto similarity index to examine the extracted features for IQA. We estimate the proposed BSFFS-TIMPNN technique on the Tampere Image Database (TID2013) dataset with qualitative and quantitative results analysis. The proposed BSFFS-TIMPNN technique is employed to compute image quality with maximum accuracy and minimum time and memory consumption when compared to other related methods by MATLAB simulator. The observed quantitative result demonstrates the better performance of the proposed BSFFS-TIMPNN technique by full reference and no reference with higher PSNR by 8%, accuracy by 15%, 17%, lesser memory consumption by 15%, 17, and faster prediction time by 9%, 9% than the state of art methods respectively.

Substrate space analysis of the bacterial proton-coupled oligopeptide transporter YdgR by cheminformatics.

Proton-dependent oligopeptide transporters (POTs) are recognized for their substrate promiscuity due to their ability to transport a wide range of substrates. POTs are conserved in all forms of life ranging from bacteria to humans. A dipeptide-fluorophore conjugate, H-(β-Ala)-Lys(AMCA)-OH, is a well-known substrate of the transporter YdgR that is commonly used as a fluorescent reporter. In order to understand the substrate space of YdgR, we used this dipeptide as a bait reference, when screening an ensemble of compounds (previously tested in PEPT/PTR/NPF space) via a cheminformatic analysis based on the Tanimoto similarity index. Eight compounds (sinalbin, abscisic acid, carnosine, jasmonic acid, N-acetyl-aspartate, N-acetyl-lysine, aspartame, and N-acetyl-aspartylglutamate), covering a wide range on the Tanimoto scale, were tested for YdgR-mediated transport. Carnosine was the only compound observed to be a YdgR substrate based on cell-based transport assays and molecular docking. The other compounds tested were neither inhibitors nor substrates. Thus, we found that neither the Tanimoto similarity index nor ADME (absorption, distribution, metabolism, and excretion) properties appear useful for the identification of substrates (e.g., dipeptides) in YdgR-mediated drug transport.

Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is the major cause of clinical trial failure and postmarketing withdrawals of approved drugs. It is very expensive and time-consuming to evaluate hepatotoxicity using animal or cell-based experiments in the early stage of drug development. In this study, an in silico model based on the joint decision-making strategy was developed for DILI assessment using a relatively large dataset of 2608 compounds. Five consensus models were developed with PaDEL descriptors and PubChem, Substructure, Estate, and Klekota–Roth fingerprints, respectively. Submodels for each consensus model were obtained through joint optimization. The parameters and features of each submodel were optimized jointly based on the hybrid quantum particle swarm optimization (HQPSO) algorithm. The application domain (AD) based on the frequency-weighted and distance (FWD)-based method and Tanimoto similarity index showed the wide AD of the qualified consensus models. A joint decision-making model was integrated by the qualified consensus models, and the overwhelming majority principle was used to improve the performance of consensus models. The application scope narrowing caused by the overwhelming majority principle was successfully solved by joint decision-making. The proposed model successfully predicted 99.2% of the compounds in the test set, with an accuracy of 80.0%, a sensitivity of 83.9, and a specificity of 73.3%. For an external validation set containing 390 compounds collected from DILIrank, 98.2% of the compounds were successfully predicted with an accuracy of 79.9%, a sensitivity of 97.1%, and a specificity of 66.0%. Furthermore, 25 privileged substructures responsible for DILI were identified from Substructure, PubChem, and Klekota–Roth fingerprints. These privileged substructures can be regarded as structural alerts in hepatotoxicity evaluation. Compared with the main published studies, our method exhibits certain advantage in data size, transparency, and standardization of the modeling process and accuracy and credibility of prediction results. It is a promising tool for virtual screening in the early stage of drug development.

STOUT: SMILES to IUPAC names using neural machine translation

Chemical compounds can be identified through a graphical depiction, a suitable string representation, or a chemical name. A universally accepted naming scheme for chemistry was established by the International Union of Pure and Applied Chemistry (IUPAC) based on a set of rules. Due to the complexity of this ruleset a correct chemical name assignment remains challenging for human beings and there are only a few rule-based cheminformatics toolkits available that support this task in an automated manner. Here we present STOUT (SMILES-TO-IUPAC-name translator), a deep-learning neural machine translation approach to generate the IUPAC name for a given molecule from its SMILES string as well as the reverse translation, i.e. predicting the SMILES string from the IUPAC name. In both cases, the system is able to predict with an average BLEU score of about 90% and a Tanimoto similarity index of more than 0.9. Also incorrect predictions show a remarkable similarity between true and predicted compounds.

Clustering of Red/White Wine and Allergen/Non-Allergen Data Sets by Using Descriptor Fingerprints

An application of the descriptor fingerprints in relation to a clustering procedure of red and white wines and food allergen/non-allergens has been studied. The clustering method is based on a procedure using the Tanimoto similarity index. Different threshold values have been employed in order a best set of clusters to be obtained. It was shown that this procedure produces quite good discrimination between the red and white wines, but any correlation with the human sensation tastes (represented by a scale from 1 to 10) is more difficult to be obtained. The results from 4212 food allergens/non-allergens show a much better discrimination of those two groups starting from threshold value=0.5.

Antiplasmodial profile of selected compounds from Malaria Box: in vitro evaluation, speed of action and drug combination studies

BackgroundArtemisinin-based combination therapy (ACT) is used as the first-line treatment of uncomplicated malaria caused by the Plasmodium falciparum parasite and chloroquine-resistant Plasmodium vivax parasites. Evidence of resistance to ACT has been reported in Cambodia, and without new and effective anti-malarial agents, malaria burden and mortality will rise.MethodsThe used MolPrint 2D fingerprints and the Tanimoto similarity index were used to perform a structural similarity search within the Malaria Box collection to select diverse molecular scaffolds that are different from artesunate. Next, the inhibitory potency against the P. falciparum 3D7 strain (SYBR Green I inhibition assay) and the cytotoxicity against HepG2 cells (MTT and neutral red assays) were evaluated. Then, the speed of action, the combination profile of selected inhibitors with artesunate, and the P. berghei in vivo activity of the best compounds were assessed.ResultsA set of 11 structurally diverse compounds from the Malaria Box with a similarity threshold of less than 0.05 was selected and compared with artesunate. The in vitro inhibitory activity of each compound confirmed the reported potencies (IC50 values ranging from 0.005 to 1 µM). The cytotoxicity of each selected compound was evaluated and used to calculate the selectivity index (SI values ranging from 15.1 to 6100). Next, both the speed of action and the combination profile of each compound with artesunate was assessed. Acridine, thiazolopyrimidine, quinoxaline, benzimidazole, thiophene, benzodiazepine, isoxazole and pyrimidoindole derivatives showed fast in vitro inhibitory activity of parasite growth, whereas hydrazinobenzimidazole, indenopyridazinone and naphthalenone derivatives were slow-acting in vitro inhibitors. Combinatory profile evaluation indicated that thiazolopyrimidinone and benzodiazepine derivatives have an additive profile, suggesting that the combination of these inhibitors with artesunate is favourable for in vitro inhibitory activity. The remaining compounds showed an antagonistic combinatory profile with artesunate. The collected data indicated that the indenopyridazinone derivative, a bc1 complex inhibitor, had a similar association profile in combination with proguanil when compared to atovaquone combined with proguanil, thereby corroborating the correlation between the molecular target and the combination profile. Lastly, the in vivo activity of the thiazolopyrimidinone and benzodiazepine derivatives were assessed. Both compounds showed oral efficacy at 50 mg/kg in a mouse model of Plasmodium berghei malaria (64% and 40% reduction in parasitaemia on day 5 post-infection, respectively).ConclusionsThe findings in this paper shed light on the relationship among the speed of action, molecular target and combinatory profile and identified new hits with in vivo activity as candidates for anti-malarial combination therapy.

In search of the representative pharmacophore hypotheses of the enzymatic proteome of Plasmodium falciparum: a multicomplex-based approach.

Drug resistance has made malaria an untreatable disease and therefore intensified the need for the development of new drugs and the identification of potential drug targets. In this pursuit, in silico efforts made in the past have not shown significant responses. Therefore, in the present work, the multicomplex-based pharmacophore modeling approach was employed to construct the pharmacophores of the 16 selected Plasmodium falciparum (Pf) targets. All the constructed hypotheses (153) were screened against a focused dataset made up of experimental actives of the chosen targets (3705 inhibitors). The rationale was to check the affinity of the inhibitors for the off-targets. Subsequently, the constructed hypotheses from each target were pooled based on the feature types and the pooled-hypotheses were then clustered to offer an insight about the pharmacophore similarity. Tanimoto similarity index was also calculated to look for the similarity among the inhibitors belonging to different Pf targets. Overall, the work was accomplished to bid healthier perceptive of the pharmacophore-based virtual screening and abet in providing guiding principles for the construction of stringent pharmacophores that can be employed for the screening.

DISCOVERY OF NOVEL AND SELECTIVE C-JUN NH2-TERMINAL KINASES 2 INHIBITORS BY TWO-DIMENSIONAL QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP MODEL DEVELOPMENT, MOLECULAR DOCKING AND ABSORPTION, DISTRIBUTION, METABOLISM, ELIMINATION PREDICTION STUDIES: AN IN SILICO APPROACH

Objective: Incited by the dearth of selective c-Jun NH2-terminal kinases (JNK) inhibitors, efforts have been made to design novel JNK2 inhibitors with good selectivity profile by utilizing a set of in silico tools.Methods: The present study involved 2D QSAR model development through multiple linear regression and partial least square methods. Further, the information unveiled through the above model was meticulously utilized to design novel JNK2 inhibitors (compound a and compound b). The selectivity of the novel molecules was ascertained through the molecular docking experiments. Determination of Tanimoto similarity index and absorption, distribution, metabolism, and elimination (ADME) properties was performed to ascertain the novelty and drug-like properties of the designed molecules, respectively.Results: Four explanatory variables or descriptors, moment of inertia 2 length (subst.1), moment of inertia 3 length (subst.3), Kier Chi4 (path/ cluster) index (whole molecule), and vamp highest occupied molecular orbital (whole molecule), were found to possess profound influence on the biological activity with the values of standard parameters, s: 0.28, r: 0.96, r2: 0.92, r2CV: 0.82, and f=59.69. Tanimoto index values were found to be 0.095 and 0.075 for compound a and compound b, respectively.Conclusion: The values of the statistical parameters proved the developed model to be of excellent quality. The results obtained for ADME prediction studies proved both the compounds to be better than the most potent compound (compound 45) of the set of JNK2 inhibitors selected for model development. In addition, extremely low Tanimoto similarity index values for both the compounds provided sufficient evidence for the novelty of the designed molecules.Objective: Incited by the dearth of selective c-Jun NH2-terminal kinases (JNK) inhibitors, efforts have been made to design novel JNK2 inhibitors with good selectivity profile by utilizing a set of in silico tools.Methods: The present study involved 2D QSAR model development through multiple linear regression and partial least square methods. Further, the information unveiled through the above model was meticulously utilized to design novel JNK2 inhibitors (compound a and compound b). The selectivity of the novel molecules was ascertained through the molecular docking experiments. Determination of Tanimoto similarity index and absorption, distribution, metabolism, and elimination (ADME) properties was performed to ascertain the novelty and drug-like properties of the designed molecules, respectively.Results: Four explanatory variables or descriptors, moment of inertia 2 length (subst.1), moment of inertia 3 length (subst.3), Kier Chi4 (path/ cluster) index (whole molecule), and vamp highest occupied molecular orbital (whole molecule), were found to possess profound influence on the biological activity with the values of standard parameters, s: 0.28, r: 0.96, r2: 0.92, r2CV: 0.82, and f=59.69. Tanimoto index values were found to be 0.095 and 0.075 for compound a and compound b, respectively.Conclusion: The values of the statistical parameters proved the developed model to be of excellent quality. The results obtained for ADME prediction studies proved both the compounds to be better than the most potent compound (compound 45) of the set of JNK2 inhibitors selected for model development. In addition, extremely low Tanimoto similarity index values for both the compounds provided sufficient evidence for the novelty of the designed molecules.

Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model

Quantitative Structure-Retention Relationships (QSRR) methodology combined with the Hydrophobic Subtraction Model (HSM) have been utilized to accurately predict retention times for a selection of analytes on several different reversed phase liquid chromatography (RPLC) columns. This approach is designed to facilitate early prediction of co-elution of analytes, for example in pharmaceutical drug discovery applications where it is advantageous to predict whether impurities might be co-eluted with the active drug component. The QSRR model utilized VolSurf+ descriptors and a Partial Least Squares regression combined with a Genetic Algorithm (GA-PLS) to predict the solute coefficients in the HSM. It was found that only the hydrophobicity (η'H) term in the HSM was required to give the accuracy necessary to predict potential co-elution of analytes. Global QSRR models derived from all 148 compounds in the dataset were compared to QSRR models derived using a range of local modelling techniques based on clustering of compounds in the dataset by the structural similarity of compounds (as represented by the Tanimoto similarity index), physico-chemical similarity of compounds (represented by log D), the neutral, acidic, or basic nature of the compound, and the second dominant interaction between analyte and stationary phase after hydrophobicity. The global model showed reasonable prediction accuracy for retention time with errors of 30 s and less for up to 50% of modeled compounds. The local models for Tanimoto, nature of the compound and second dominant interaction approaches all exhibited prediction errors less than 30 s in retention time for nearly 70% of compounds for which models could be derived. Predicted retention times of five representative compounds on nine reversed-phase columns were compared with known experimental retention data for these columns and this comparison showed that the accuracy of the proposed modelling approach is sufficient to reliably predict the retention times of analytes based only on their chemical structures.

Modelling the water–plant cuticular polymer matrix membrane partitioning of diverse chemicals in multiple plant species using the support vector machine-based QSAR approach

In this study, a support vector machine (SVM) based multi-species QSAR (quantitative structure–activity relationship) model was developed for predicting the water–plant cuticular polymer matrix membrane (MX) partition coefficient, KMXw of diverse chemicals using two simple molecular descriptors derived from the chemical structures and following the OECD guidelines. Accordingly, the Lycopersicon esculentum Mill. data were used to construct the QSAR model that was externally validated using three other plant species data. The diversity in chemical structures and end-points were verified using the Tanimoto similarity index and Kruskal–Wallis statistics. The predictive power of the developed QSAR model was tested through rigorous validation, deriving a wide series of statistical checks. The MLOGP was the most influential descriptor identified by the model. The model yielded a correlation (r2) of 0.966 and 0.965 in the training and test data arrays. The developed QSAR model also performed well in another three plant species (r2 > 0.955). The results suggest the appropriateness of the developed model to reliably predict the plant chemical interactions in multiple plant species and it can be a useful tool in screening the new chemical for environmental risk assessment.

Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. III Combination of Tanimoto similarity index, logP, and retention factor ratio to identify optimal analyte training sets for ion chromatography

Retention prediction for unknown compounds based on Quantitative Structure-Retention Relationships (QSRR) can lead to rapid “scoping” method development in chromatography by simplifying the selection of chromatographic parameters. The use of retention factor ratio (or k-ratio) as a chromatographic similarity index can be a potent method to cluster similar compounds into a training set to generate an accurate predictive QSRR model provided that its limitation – that the method is impractical for retention prediction for unknown compounds – is successfully addressed. In this work, we propose a localised QSRR modelling approach with the aim of compensating the critical limitation in the otherwise successful k-ratio filter-based QSRR modelling. The approach is to combine a k-ratio filter with both Tanimoto similarity (TS) and a ΔlogP index (i.e., logP-Dual filter). QSRR models for two retention parameters (a and b) in the linear solvent strength (LSS) model in ion chromatography (IC), logk=a − blog[eluent], were generated for larger organic cations (molecular mass up to 506) on a Thermo Fisher Scientific CS17 column. The application of the developed logP-Dual filter resulted in the production of successful QSRR models for 50 organic cations out of 87 in the dataset. The predicted a- and b-values of the models were then applied to the LSS model to predict the corresponding retention times. External validation showed that QSRR models for a-, b- and tR- values with excellent accuracy and predictability (Qext(F2)2 of 0.96, 0.95, and 0.96, RMSEP of 0.06, 0.02, and 0.38min) were created successfully, and these models can be employed to speed up the “scoping” phase of method development in IC.

Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography

Quantitative Structure-Retention Relationships (QSRR) are used to predict retention times of compounds based only on their chemical structures encoded by molecular descriptors. The main concern in QSRR modelling is to build models with high predictive power, allowing reliable retention prediction for the unknown compounds across the chromatographic space. With the aim of enhancing the prediction power of the models, in this work, our previously proposed QSRR modelling approach called “federation of local models” is extended in ion chromatography to predict retention times of unknown ions, where a local model for each target ion (unknown) is created using only structurally similar ions from the dataset. A Tanimoto similarity (TS) score was utilised as a measure of structural similarity and training sets were developed by including ions that were similar to the target ion, as defined by a threshold value. The prediction of retention parameters (a- and b-values) in the linear solvent strength (LSS) model in ion chromatography, log k=a – blog[eluent], allows the prediction of retention times under all eluent concentrations. The QSRR models for a- and b-values were developed by a genetic algorithm-partial least squares method using the retention data of inorganic and small organic anions and larger organic cations (molecular mass up to 507) on four Thermo Fisher Scientific columns (AS20, AS19, AS11HC and CS17). The corresponding predicted retention times were calculated by fitting the predicted a- and b-values of the models into the LSS model equation. The predicted retention times were also plotted against the experimental values to evaluate the goodness of fit and the predictive power of the models. The application of a TS threshold of 0.6 was found to successfully produce predictive and reliable QSRR models (Qext(F2)2>0.8 and Mean Absolute Error<0.1), and hence accurate retention time predictions with an average Mean Absolute Error of 0.2min.

Towards a chromatographic similarity index to establish localized quantitative structure-retention models for retention prediction: Use of retention factor ratio

Quantitative Structure-Retention Relationships (QSRR) have the potential to speed up the screening phase of chromatographic method development as the initial exploratory experiments are replaced by prediction of analyte retention based solely on the structure of the molecule. The present study offers further proof-of-concept of localized QSRR modelling, in which the retention of any given compound is predicted using only the most chromatographically similar compounds in the available dataset. To this end, each compound in the dataset was sequentially removed from the database and individually utilized as a test analyte. In this study, we propose the retention factor k as the most relevant chromatographic similarity measure and compare it with the Tanimoto index, the most popular similarity measure based on chemical structure. Prediction error was reduced by up to 8 fold when QSRR was based only on chromatographically similar compounds rather than using the entire dataset. The study therefore shows that the design of a practically useful structural similarity index should select the same compounds in the dataset as does the k-similarity filter in order to establish accurate predictive localized QSRR models. While low average prediction errors (Mean Absolute Error (MAE)<0.5min) and slopes of the regression lines through the origin close to 1.00 were obtained using k-similarity searching, the use of the structural Tanimoto similarity index, considered as the gold standard in Quantitative Structure-Activity Relationships (QSAR) studies, generally resulted in much higher prediction errors (MAE>1min) and significant deviations from the reference slope of 1.0. The Tanomoto similarity index therefore appears to have limited general utility in QSRR studies. Future studies therefore aim at designing a more appropriate chromatographic similarity index that can then be applied for unknown compounds (that is, compounds which have not been tested previously on the chromatographic system used, but for which the chemical structures are known).

Modeling the reactivities of hydroxyl radical and ozone towards atmospheric organic chemicals using quantitative structure-reactivity relationship approaches.

The persistence and the removal of organic chemicals from the atmosphere are largely determined by their reactions with the OH radical and O3. Experimental determinations of the kinetic rate constants of OH and O3 with a large number of chemicals are tedious and resource intensive and development of computational approaches has widely been advocated. Recently, ensemble machine learning (EML) methods have emerged as unbiased tools to establish relationship between independent and dependent variables having a nonlinear dependence. In this study, EML-based, temperature-dependent quantitative structure-reactivity relationship (QSRR) models have been developed for predicting the kinetic rate constants for OH (kOH) and O3 (kO3) reactions with diverse chemicals. Structural diversity of chemicals was evaluated using a Tanimoto similarity index. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation performed employing statistical checks. In test data, the EML QSRR models yielded correlation (R (2)) of ≥0.91 between the measured and the predicted reactivities. The applicability domains of the constructed models were determined using methods based on descriptors range, Euclidean distance, leverage, and standardization approaches. The prediction accuracies for the higher reactivity compounds were relatively better than those of the low reactivity compounds. Proposed EML QSRR models performed well and outperformed the previous reports. The proposed QSRR models can make predictions of rate constants at different temperatures. The proposed models can be useful tools in predicting the reactivities of chemicals towards OH radical and O3 in the atmosphere.

Predicting binding affinities of diverse pharmaceutical chemicals to human serum plasma proteins using QSPR modelling approaches

The prediction of the plasma protein binding (PPB) affinity of chemicals is of paramount significance in the drug development process. In this study, ensemble machine learning-based QSPR models have been established for a four-category classification and PPB affinity prediction of diverse compounds using a large PPB dataset of 930 compounds and in accordance with the OECD guidelines. The structural diversity of the chemicals was tested by the Tanimoto similarity index. The external predictive power of the developed QSPR models was evaluated through internal and external validations. In the QSPR models, XLogP was the most important descriptor. In the test data, the classification QSPR models rendered an accuracy of >93%, while the regression QSPR models yielded r2 of >0.920 between the measured and predicted PPB affinities, with the root mean squared error <9.77. Values of statistical coefficients derived for the test data were above their threshold limits, thus put a high confidence in this analysis. The QSPR models in this study performed better than any of the previous studies. The results suggest that the developed QSPR models are reliable for predicting the PPB affinity of structurally diverse chemicals. They can be useful for initial screening of candidate molecules in the drug development process.

Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.

Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and development of computational approaches has widely been advocated. In this study, artificial intelligence (AI)-based ten different qualitative and quantitative structure-property relationship (QSPR) models (MLPN, RBFN, PNN/GRNN, CCN, SVM, GEP, GMDH, SDT, DTF, DTB) were established for the prediction of the adsorption capacity of structurally diverse chemicals to activated carbon following the OECD guidelines. Structural diversity of the chemicals and nonlinear dependence in the data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation procedures performed employing a wide series of statistical checks. In complete dataset, the qualitative models rendered classification accuracies between 97.04 and 99.93%, while the quantitative models yielded correlation (R(2)) values of 0.877-0.977 between the measured and the predicted endpoint values. The quantitative prediction accuracies for the higher molecular weight (MW) compounds (class 4) were relatively better than those for the low MW compounds. Both in the qualitative and quantitative models, the Polarizability was the most influential descriptor. Structural alerts responsible for the extreme adsorption behavior of the compounds were identified. Higher number of carbon and presence of higher halogens in a molecule rendered higher binding affinity. Proposed QSPR models performed well and outperformed the previous reports. A relatively better performance of the ensemble learning models (DTF, DTB) may be attributed to the strengths of the bagging and boosting algorithms which enhance the predictive accuracies. The proposed AI models can be useful tools in screening the chemicals for their binding affinities toward carbon for their safe management.

Predicting toxicities of ionic liquids in multiple test species – an aid in designing green chemicals

Ionic liquids (ILs) due to their unique characteristics have attained much importance for future applications, although they may pose environmental risks to aquatic ecosystems that have to be assessed. This paper presents a novel computational approach for estimating the toxicity of ILs in multiple test species of different trophic levels in accordance with the OECD guidelines. Here, ensemble learning based global structure–activity relationship (SAR) models were established for qualitative (two- and four-category) and quantitative toxicity predictions of ILs in Vibrio fischeri and successfully applied to the algae and daphnia species. Diversity and nonlinearity of the considered datasets were evaluated using the Tanimoto similarity index, Kruskal–Wallis and Brock–Dechert–Scheinkman statistics. The gradient boosted tree (GBT) and bagged decision tree (BDT) SAR models were constructed using simple descriptors and validated by stringent statistical tests. In V. fischeri, algae and daphnia data, the classification accuracies rendered by GBT and BDT models were 84.44–100% (two-category) and 92.23–98.74% (four-category), while the two models yielded correlations (R2) of 0.857–0.982 between the measured and predicted toxicity values with mean squared errors of 0.21–0.04. The SARs also identified structural elements of ILs responsible for their toxicities. The successful results obtained in three test species of different trophic levels reveal that the proposed approach can be useful as a screening tool to easily aid, from the early stages of the processes, the design of aquatic environmentally friendly ILs.