Application Of Quantitative Structure-activity Relationships Research Articles

Application of Quantitative Structure-Activity Relationships in the Prediction of New Compounds with Anti-Leukemic Activity.

Leukemia invades the bone marrow progressively and, through unknown mechanisms, outcompetes healthy hematopoiesis. Protein arginine methyltransferases 1 (PRMT1) are found in prokaryotes and eukaryotes cells. They are necessary for a number of biological processes and have been linked to several human diseases, including cancer. Small compounds that target PRMT1 have a significant impact on both functional research and clinical disease treatment. In fact, numerous PRMT1 inhibitors targeting the S-adenosyl-L-methionine binding region have been studied. Through topographical descriptors, quantitative structure-activity relationships (QSAR) were developed in order to identify the most effective PRMT1 inhibitors among 17 compounds. The model built using linear discriminant analysis allows us to accurately classify over 90% of the investigated active substances. Antileukemic activity is predicted using a multilinear regression analysis, and it can account for more than 56% of the variation. Both analyses are validated using an internal "leave some out" test. The developed model could be utilized in future preclinical experiments with novel drugs.

A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability

IntroductionThe current effort towards the digital transformation across multiple scientific domains requires data that is Findable, Accessible, Interoperable and Reusable (FAIR). In addition to the FAIR data, what is required for the application of computational tools, such as Quantitative Structure Activity Relationships (QSARs), is a sufficient data volume and the ability to merge sources into homogeneous digital assets. In the nanosafety domain there is a lack of FAIR available metadata. MethodologyTo address this challenge, we utilized 34 datasets from the nanosafety domain by exploiting the NanoSafety Data Reusability Assessment (NSDRA) framework, which allowed the annotation and assessment of dataset's reusability. From the framework's application results, eight datasets targeting the same endpoint (i.e. numerical cellular viability) were selected, processed and merged to test several hypothesis including universal versus nanogroup-specific QSAR models (metal oxide and nanotubes), and regression versus classification Machine Learning (ML) algorithms. ResultsUniversal regression and classification QSARs reached an 0.86 R2 and 0.92 accuracy, respectively, for the test set. Nanogroup-specific regression models reached 0.88 R2 for nanotubes test set followed by metal oxide (0.78). Nanogroup-specific classification models reached 0.99 accuracy for nanotubes test set, followed by metal oxide (0.91). Feature importance revealed different patterns depending on the dataset with common influential features including core size, exposure conditions and toxicological assay.Even in the case where the available experimental knowledge was merged, the models still failed to correctly predict the outputs of an unseen dataset, revealing the cumbersome conundrum of scientific reproducibility in realistic applications of QSAR for nanosafety. To harness the full potential of computational tools and ensure their long-term applications, embracing FAIR data practices is imperative in driving the development of responsible QSAR models. ConclusionsThis study reveals that the digitalization of nanosafety knowledge in a reproducible manner has a long way towards its successful pragmatic implementation. The workflow carried out in the study shows a promising approach to increase the FAIRness across all the elements of computational studies, from dataset's annotation, selection, merging to FAIR modeling reporting. This has significant implications for future research as it provides an example of how to utilize and report different tools available in the nanosafety knowledge system, while increasing the transparency of the results. One of the main benefits of this workflow is that it promotes data sharing and reuse, which is essential for advancing scientific knowledge by making data and metadata FAIR compliant. In addition, the increased transparency and reproducibility of the results can enhance the trustworthiness of the computational findings.

Deep Learning in Environmental Toxicology: Current Progress and Open Challenges

Ubiquitous chemicals in the environment may pose a threat to human health and the ecosystem, so comprehensive toxicity information must be obtained. Due to the inability of traditional experimental methods to meet the needs of toxicity testing of a large number of chemicals, in vivo and in vitro assays have been shifted to a new paradigm, computer-assisted virtual screening. However, the commonly used virtual screening techniques, including read-across and machine learning-based quantitative structure–activity relationship (QSAR), have limitations in assessing complicated, high-dimensional, and multimodal bioactivity data. In these cases, deep learning (DL) has emerged as a desirable solution for the application of QSARs in toxicity prediction. Therefore, this paper introduces and discusses (a) architectures of six commonly used DL algorithms (fully connected neural network, convolutional neural network, recurrent neural network, long short-term memory, graph neural network, and generative adversarial network), (b) the application scenarios of six DL algorithms, e.g., toxicity prediction and data generation, and (c) challenges and future trends of DLs in toxicity prediction. We believe that by consolidating toxicological mechanisms and DL algorithms, this survey can help readers to build prediction models with excellent performance while promoting further discussions of the fusion of environmental toxicology and DL.

Application of QSAR for investigation on coagulation mechanisms of textile wastewater.

Coagulation is an effective preliminary treatment process for textile wastewater. In order to evaluate the effectiveness of the coagulation process, we performed quantitative structure activity relationship (QSAR) analyses with total organic carbon (TOC) removal rates (Rexp) as an index by three different coagulants (AlCl3, FeCl3, and MgCl2). The experimental results showed that the average Rexp of the three coagulants was 39.12%±2.60%, 51.60%±2.88%, and 49.95%±3.17%. Subsequently, 42 molecular descriptors of dye molecules were calculated by quantitative calculation softwares Gaussian 09, Material Studio 7.0, and Multiwfn 3.7, and then QSAR models were constructed by a multiple linear regression (MLR) method for the three coagulation systems. The developed QSAR models demonstrated excellent stability, robustness, and predictability with values of R2 =0.7677, 0.8015, and 0.7035, Q2INT =0.6067, 0.7026, and 0.5898, Q2EXT =0.5505, 0.5076, and 0.5697, respectively. Based on the analysis of quantum parameters, the coagulation mechanism for AlCl3, FeCl3 is primarily electrostatic adsorption as well as hydrogen bonding, while MgCl2 coagulates dyes mainly by electrostatic adsorption. This study provides an assessment of the removal rules and a feasible method for predicting dye removal rates in AlCl3, FeCl3, and MgCl2 coagulation process.

International Consortium to Advance Cross-Species Extrapolation of the Effects of Chemicals in Regulatory Toxicology.

International Consortium to Advance Cross-Species Extrapolation of the Effects of Chemicals in Regulatory Toxicology.

Application of QSAR for the identification of key molecular fragments and reliable predictions of effects of textile dyes on growth rate and biomass values of Raphidocelis subcapitata.

The current quantitative structure-activity relationship (QSAR) study seeks to explore the underlying causes of fluctuations in growth rate and biomass of microalgae mainly due to textile dyes. The derived QSAR models cover two endpoints: ErC50 (growth rate) and EbC50 (biomass) of Raphidocelis subcapitata. In order to extract the structural features involved, multiple PLS (partial least squares) models have been developed with easy to interpret and uncomplicated 2D descriptors having proper physico-chemical meaning. These descriptors were calculated from Dragon, SiRMS, and PaDEL-descriptor software. Then, the models were developed initially using stepwise regression followed by partial least squares (PLS) regression, and the model development procedure for both the endpoints (ErC50 and EbC50) followed the stringent Organization for Economic Cooperation and Development (OECD) rules. Later on, the model validation was carried out with statistically significant and internationally accepted metrics (both internally and externally) in both the cases. Next, we have used the "Intelligent Consensus Predictor" tool (available from http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) to test the prediction quality with an "intelligent" approach to select multiple models. The estimated prediction quality for the appropriate test sets reveals that the consensus models (CM) surpass the quality shown by individual models (IM) for both the endpoints (ErC50 and EbC50). Finally, the developed models were able to identify the major contributing features (hydrophobic units, unsaturation, saturation, electronegativity, branched atoms and charged fragments) related to aquatic toxicity of textile dyes.

Occurrence and Health Risks of Organic Micro-Pollutants and Metals in Groundwater of Chinese Rural Areas.

Background:Groundwater is a main drinking-water source for Chinese rural residents. The overall pollution status of organic micropollutants (OMPs) and metals in the groundwater and corresponding health risks are unknown.Objectives:Our objective was to comprehensively screen for and assess the health risks of OMPs and metals in groundwater of rural areas in China where groundwater is used for drinking so as to provide a benchmark for monitoring and improving groundwater quality in future developments.Methods:One hundred sixty-six groundwater samples were collected in the rural areas of China, and 1,300 OMPs and 25 metals were screened by GC-MS, LC-QTOF/MS, and ICP-MS analysis. To assess the noncarcinogenic and carcinogenic risks of the detected pollutants, missing toxicity threshold values were extrapolated from existing databases or predicted by quantitative structure–activity relationship (QSAR) models. Monte Carlo simulation was performed to account for uncertainties in the exposure parameters and toxicity thresholds.Results:Two hundred thirty-three OMPs and 25 metals were detected from the 166 samples. The concentration summation for the detected OMPs ranged from 2.9 to among the different sampling sites. Cumulative noncarcinogenic risks for the OMPs were estimated to be negligible. However, high metal risks were calculated in 23% of the sites. Forty-two carcinogens (including 38 OMPs) were identified and the cumulative carcinogenic risks in 34% of the sites were calculated to be (i.e., one excess cancer case in a population of 10 thousand people). The carcinogenic risks were estimated to be mainly associated with exposures to the metals, which were calculated to contribute 79% (0–100%) of the cumulative carcinogenic risks.Discussion:The overall status of OMPs and metals pollution in the groundwater and the corresponding health risks were determined preliminarily, which may provide a benchmark for future efforts in China to ensure the safety of drinking water for the local residents in rural areas. The joint application of QSARs and Monte Carlo simulation provided a feasible way to comprehensively assess the health risks of the large and ever-increasing number of pollutants detected in the aquatic environment. https://doi.org/10.1289/EHP6483

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review.

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as 'dark chemical space' or 'dark chemistry.' Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: 'genomics', proteomics and 'in-silico simulation' will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Application of QSARs in identification of mutagenicity mechanisms of nitro and amino aromatic compounds against Salmonella typhimurium species

In an attempt to describe the underlying causes of mutagenicity mainly due to organic chemicals, quantitative structure-activity relationship (QSAR) models have been developed using two different Salmonella typhimurium mutagenicity endpoints with or without presence of liver metabolic microsomal enzymes (S9) namely TA98-S9 and TA98 + S9. The models were developed using simple 2D variables having definite physicochemical meaning calculated from Dragon, SiRMS, and PaDEL-descriptor software tools. Stepwise regression followed by partial least squares (PLS) regression was used in model development following the strict OECD guidelines for QSAR model development and validation. The models were validated using coefficient of determination R2, cross-validation coefficient Q2LOO (leave one out) while the test set predictions were analyzed using Q2F1 (coefficient of determination for the test set). Several other internationally accepted validation metrics like MAE95%train, average rm(LOO)2 and Δrm(LOO)2 (for the training set) were used to check model robustness while predictive efficiency was evaluated using MAE95%test, average rm2 and Δrm2 (for the test set). The scope of predictions was defined by applicability domain analysis using the DModX approach, a recommended tool for PLS models. The major contributing features related to mutagenicity include lipophilicity, electronegativity, branching and unsaturation, etc. The present manuscript is the first attempt to undertake modeling of two different endpoints (TA98-S9 and TA98 + S9) in order to explore major contributing molecular features linked directly or indirectly to mutagenicity.

Application of Interspecies Correlation Estimation (ICE) models and QSAR in estimating species sensitivity to pesticides

ABSTRACTEcological risk assessment is challenged by the need to assess hazard to the diverse communities of organisms inhabiting aquatic and terrestrial systems. Computational approaches, such as Quantitative Structure Activity Relationships (QSAR) and Interspecies Correlation Estimation (ICE) models, are useful tools that provide estimates of acute toxicity where data are lacking or limited for ecological risk assessments (ERA). This review describes the technical basis of ICE models for use in pesticide ERA that may be used in conjunction with QSAR model estimates or surrogate species toxicity data and demonstrates the potential for improving hazard assessment. Validation and uncertainty analysis of ICE model predictions are summarized and used as guidance for selecting ICE models and evaluating toxicity predictions. A user-friendly web-based ICE modelling platform (Web-ICE) is described and demonstrated through case studies. Case studies include the development of Species Sensitivity Distributions generated from QSAR and ICE estimates, comparative sensitivity for a pesticide and its degradate, and application of ICE-estimated toxicity values for listed species assessments.

Application of quantitative structure-activity relationship (QSAR) model in comprehensive human health risk assessment of PAHs, and alkyl-, nitro-, carbonyl-, and hydroxyl-PAHs laden in urban road dust

The carcinogenic human health risks (CHHR) posed by the exposure to PAHs and transformed PAH products (TPPs) are currently inconclusive due to the lack of toxicity equivalency factors (TEFs) for most TPPs although some of these pollutants are more potent carcinogens. The applicability of quantitative structure-activity relationship (QSAR) model in predicting TEF of PAHs and TPPs to holistically evaluate the CHHR posed by the exposure to these pollutants in road dust from Gold Coast, Australia was examined. Statistical evaluation via ten metrics shows that partial least-squares regression (PLSR1) model has more statistical power in predicting TEF than multiple linear regression (MLR) within relevant applicability domain. For instance, the predicted residual sum of squares (PRESS) and standard deviation of error of prediction (SDEP) for PLSR is closer to zero than that of MLR. The total cancer risk estimated using the QSAR model derived TEFs and original TEFs for outliers gives a more holistic incremental lifetime cancer risk in relation to children and adults. Potential cancer risk exists for adults with this approach whereas reliance on only the originally available TEFs lead to a negligible risk diagnosis. The application of QSAR model in assessing CHHR due to PAHs and TPPs exposures is very viable.

Integrated QSAR Models to Predict Acute Oral Systemic Toxicity.

The ICCVAM Acute Toxicity Workgroup (U.S. Department of Health and Human Services), in collaboration with the U.S. Environmental Protection Agency (U.S. EPA, National Center for Computational Toxicology), coordinated the "Predictive Models for Acute Oral Systemic Toxicity" collaborative project to develop in silico models to predict acute oral systemic toxicity for filling regulatory needs. In this framework, new Quantitative Structure-Activity Relationship (QSAR) models for the prediction of very toxic (LD50 lower than 50 mg/kg) and nontoxic (LD50 greater than or equal to 2,000 mg/kg) endpoints were developed, as described in this study. Models were developed on a large set of chemicals (8992), provided by the project coordinators, considering the five OCED principles for QSAR applicability to regulatory endpoints. A Bayesian consensus approach integrating three different classification QSAR algorithms was applied as modelling method. For both the considered endpoints, the proposed approach demonstrated to be robust and predictive, as determined by a blind validation on a set of external molecules provided in a later stage by the coordinators of the collaborative project. Finally, the integration of predictions obtained for the very toxic and nontoxic endpoints allowed the identification of compounds associated to medium toxicity, as well as the analysis of consistency between the predictions obtained for the two endpoints on the same molecules. Predictions of the proposed consensus approach will be integrated with those originated from models proposed by the participants of the collaborative project to facilitate the regulatory acceptance of in-silico predictions and thus reduce or replace experimental tests for acute toxicity.

Computational chemistry study of toxicity of some m-tolyl acetate derivatives insecticides and molecular design of structurally related products.

Molecular descriptors (including quantum chemical, topological and physicochemical descriptors) were calculated for five m-tolyl derivatives insecticides [namely, carbosulfan (CBS), carbofuran (CBF), isoprocab (IFP), methiocarb (MTC) and isocarbophos (ICP)]. Calculated quantum chemical parameters included the total energy, the electronic energy, the binding energy, the core-core repulsion energy, the heat of formation, the dipole moment and the frontier molecular orbital energies. All the calculated quantum chemical parameters (except dipole moment) exhibited strong correlation with the experimental LD50 values of the studied insecticides (at various Hamiltonians). Calculated topological parameters included the molecular topological index (MTI), polar surface area (PSA), total connectivity (TC), total valence connectivity (TVC), Wiener index (WI), topological diameter (TD) and Balaban index (BI). However, only MTI, PSA, WI and BI exhibited excellent correlation with the toxicological activity of the insecticides. Also among all the calculated physicochemical parameters [logP, surface area (SA), surface volume (SV), hydration energy (EHydr), polarizability (PLZ) and refractivity (RFT)], only SV, EHydr, PLZ and RFT were useful in establishing quantitative structure activity relationship (QSAR). Application of QSAR indicated that the calculated theoretical LD50 values for the studied insecticides displayed excellent correlation with experimentally derived LD50 values. However, best results were obtained from quantum chemical descriptors under modified neglect of atomic overlap (MNDO). The toxicity profile of the insecticides also correlated strongly with ionization energy, electron affinity, global softness and global harness. Reactive sites of each of the insecticides were established using Fukui function, Huckel charges and HOMO/LUMO diagrams. Six new molecules were proposed and their theoretical activities were estimated. The proposed molecules included 2-methyl-2-(methylthio)-2,3-dihydrobenzofuran-7-yl methylcarbamate, O-methyl O-2-((methylaminooxy)carbonyl)phenyl phosphoramidothioate, 2-((methylaminooxy)carbonyl)phenyl methylcarbamate, 2-(1-(methylthio)ethyl)phenyl methylcarbamate, N-methyl-O-(2-(methylthiooxy) benzoyl) hydroxyl amine and 4-methyl naphthalen-2-yl methylcarbamate. Some of the proposed molecules exhibited negative values of LD50 (indicating extreme toxicity) while two of them exhibited values that are comparable to existing insecticides.

Design (Docking and QSAR Studies) and synthesis of histone deacetylase 2 (HDAC2) inhibitors series

In recent years, it has been shown that histone deacetylase 2 (HDAC2) inhibitors increase histone acetylation and enhance memory processes, probably due to an increase in the gene transcription rate that emerges during memory formation. Histone acetylation generally favors long-term memory, whereas histone deacetylation impinges on it. However, until today there is no specific drug that can target the HDAC2 active site. In this work we applied the method of rational drug design, through enzyme-structural-chemical properties to generate new molecules as HDAC inhibitors. By the application of Quantitative Structure-Activity Relationship (QSAR) and molecular modeling methodologies our aim is to predict more potent HDAC inhibitors. 76 small molecules with potential activity were analyzed using QSAR methodology. The best model was constructed by merging the properties of electronegativity, atomic mass, polarizability, van der Waals forces and some conformational aspects, with the following statistical parameters: r 2 = 0.8935, q 2 LOO-CV = 0.8498, and q 2 LGO-CV = 0.7598. The molecular docking of the ligands on the template was performed by blind docking. The results showed intermolecular interactions between small molecules and some amino acids, such as His145, His146, Asp179, Asp186, and internal-H2O and Zn2+ of which IN01, IN04, and IN14 showed theoretically better biological activity compared with that of TSA and SAHA. Mainly, the IN14 synthesized molecule is a theoretical inhibitor of HDAC class I.

Quantitative Structure–activity Relationship Studies of Flavonoids Substituted as Anticancer Agents Activity against the Growth of the Hepatic Cancer Cell lines HepG2

Quantitative Structure–Activity Relationship (QSAR) models, based on molecular descriptors, derived from molecular structures, have been used for the prediction for computed the Hepatic Cancer Cell lines HepG2 of flavonoids substituted. QSAR model including some molecular descriptors, regression quality indicates that these descriptors provide valuable information and have significant role in the assessment of the cytotoxicity of compounds under study. Four QSAR equations, for the prediction of cytotoxicity, have been drawn up by using the multiple regression technique, (Eqs 1-4) with the values of R2 ranged from 0.767-0.797, Q2 ranged from 0.765-0.796 and the values of S ranged from 7.051-7.391, while the values of F ranged from 9.328-10.354. The results have shown excellent model by Eq 4. with high R2,F and minimum S by using eight parameters [Gm, nO, nH, nCIC, nBM, nAB, D.M and Ku], and have found and indicated that these parameters have significant role in determining the properties of cytotoxicity. This result encourages the application of QSAR to a wider selection of compounds properties.

In Silico Studies Most Employed in the Discovery of New Antimicrobial Agents.

The present review summarizes the methods most used in drug search and design, which may help to keep pace with the growing antibiotic resistance among pathogens. The rate of reduction in the effectiveness of many antimicrobial medications, caused by this resistance, is faster than new drug development, thereby creating a worldwide public health threat. Among the scientific community, the urgency of finding new drugs is peaking interest in the use of in silico studies to explore the interaction of compounds with target receptors. With this approach, small molecules (designed or retrieved from data bases) are tested with computer-aided molecular simulation to explore their efficacy. That is, ligand-protein complexes are constructed and evaluated via virtual screening (VS), molecular dynamics (MD), and docking simulations with the data from the physical, chemical and pharmacological properties of such molecules. Additionally, the application of quantitative structure-activity relationship (QSAR), multi-target quantitative structure-activity relationship (mt- QSAR), and multi-tasking quantitative structure-biological effect (mtk-QSBER) can be enhanced by principal component analysis and systematic workflows. These types of studies aid in selecting a group of promising molecules with high potency and selectivity as well as low toxicity, thus making in vitro and in vivo (animal model) testing more efficient. Since knowledge of the receptor topography and receptor-ligand interactions has yielded promising compounds and effective drugs, there is now no doubt that the use of in silico tools can lead to more rapid validation of new potential drugs for preclinical studies and clinical trials.

Flavonoids as CDK1 Inhibitors: Insights in Their Binding Orientations and Structure-Activity Relationship.

In the last years, the interactions of flavonoids with protein kinases (PKs) have been described by using crystallographic experiments. Interestingly, different orientations have been found for one flavonoid inside different PKs and different chemical substitutions lead to different orientations of the flavonoid scaffold inside one PK. Accordingly, orientation predictions of novel analogues could help to the design of flavonoids with high PK inhibitory activities. With this in mind, we studied the binding modes of 37 flavonoids (flavones and chalcones) inside the cyclin-dependent PK CDK1 using docking experiments. We found that the compounds under study adopted two different orientations into the active site of CDK1 (orientations I and II in the manuscript). In addition, quantitative structure–activity relationship (QSAR) models using CoMFA and CoMSIA methodologies were constructed to explain the trend of the CDK1 inhibitory activities for the studied flavonoids. Template-based and docking-based alignments were used. Models developed starting from docking-based alignment were applied for describing the whole dataset and compounds with orientation I. Adequate R2 and Q2 values were obtained by each method; interestingly, only hydrophobic and hydrogen bond donor fields describe the differential potency of the flavonoids as CDK1 inhibitors for both defined alignments and subsets. Our current application of docking and QSAR together reveals important elements to be drawn for the design of novel flavonoids with increased PK inhibitory activities.

Identification of Highly Potent Protein Kinase C‐Related Kinase 1 Inhibitors by Virtual Screening, Binding Free Energy Rescoring, and in vitro Testing

Despite the considerable interest in protein kinase C-related kinase 1 (PRK1) as a target in cancer research, there is still a lack of PRK1 inhibitors with suitable selectivity profiles and physicochemical properties. To identify new PRK1 inhibitors we applied a virtual screening approach, which combines ensemble docking, minimization of the protein-ligand complex, binding free energy calculations, and application of quantitative structure-activity relationship (QSAR) models for predicting in vitro activity. The developed approach was then applied in a prospective manner to screen available libraries of kinase inhibitors from Selleck and GlaxoSmithKline (GSK). Compounds that showed favorable prediction were then tested in vitro for PRK1 inhibition. Some of the hits were found to inhibit PRK1 in the low-nanomolar range. Three in vitro hits were additionally tested in a mass-spectrometry-based cellular kinase profiling assay to examine selectivity. Our findings show that nanomolar and drug-like inhibitors can be identified by the virtual screening approach presented herein. The identified inhibitors are valuable tools for gaining a better understanding of PRK1 inhibition, and the identified hits can serve as starting points for further chemical optimization.

Comparison of prediction methods for octanol-air partition coefficients of diverse organic compounds

The octanol-air partition coefficient (KOA) is needed for assessing multimedia transport and bioaccumulability of organic chemicals in the environment. As experimental determination of KOA for various chemicals is costly and laborious, development of KOA estimation methods is necessary. We investigated three methods for KOA prediction, conventional quantitative structure–activity relationship (QSAR) models based on molecular structural descriptors, group contribution models based on atom-centered fragments, and a novel model that predicts KOA via solvation free energy from air to octanol phase (ΔGO0), with a collection of 939 experimental KOA values for 379 compounds at different temperatures (263.15–323.15 K) as validation or training sets. The developed models were evaluated with the OECD guidelines on QSAR models validation and applicability domain (AD) description. Results showed that although the ΔGO0 model is theoretically sound and has a broad AD, the prediction accuracy of the model is the poorest. The QSAR models perform better than the group contribution models, and have similar predictability and accuracy with the conventional method that estimates KOA from the octanol–water partition coefficient and Henry's law constant. One QSAR model, which can predict KOA at different temperatures, was recommended for application as to assess the long-range transport potential of chemicals.

A Multi-Objective Genetic Algorithm for Outlier Removal.

Quantitative structure activity relationship (QSAR) or quantitative structure property relationship (QSPR) models are developed to correlate activities for sets of compounds with their structure-derived descriptors by means of mathematical models. The presence of outliers, namely, compounds that differ in some respect from the rest of the data set, compromise the ability of statistical methods to derive QSAR models with good prediction statistics. Hence, outliers should be removed from data sets prior to model derivation. Here we present a new multi-objective genetic algorithm for the identification and removal of outliers based on the k nearest neighbors (kNN) method. The algorithm was used to remove outliers from three different data sets of pharmaceutical interest (logBBB, factor 7 inhibitors, and dihydrofolate reductase inhibitors), and its performances were compared with those of five other methods for outlier removal. The results suggest that the new algorithm provides filtered data sets that (1) better maintain the internal diversity of the parent data sets and (2) give rise to QSAR models with much better prediction statistics. Equally good filtered data sets in terms of these metrics were obtained when another objective function was added to the algorithm (termed "preservation"), forcing it to remove certain compounds with low probability only. This option is highly useful when specific compounds should be preferably kept in the final data set either because they have favorable activities or because they represent interesting molecular scaffolds. We expect this new algorithm to be useful in future QSAR applications.