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Fulltext Organotin (IV) Dithiocarbamate Compounds as Anticancer Agents: A Review of Syntheses and Cytotoxicity Studies

Abd Aziz NA, Awang N, Chan KM, Kamaludin NF, Mohamad Anuar NN

Molecules, 2023 Aug 03;28(15).
PMID: 37570810 DOI: 10.3390/molecules28155841

Organotin (IV) dithiocarbamate has recently received attention as a therapeutic agent among organotin (IV) compounds. The individual properties of the organotin (IV) and dithiocarbamate moieties in the hybrid complex form a synergy of action that stimulates increased biological activity. Organotin (IV) components have been shown to play a crucial role in cytotoxicity. The biological effects of organotin compounds are believed to be influenced by the number of Sn-C bonds and the number and nature of alkyl or aryl substituents within the organotin structure. Ligands target and react with molecules while preventing unwanted changes in the biomolecules. Organotin (IV) dithiocarbamate compounds have also been shown to have a broad range of cellular, biochemical, and molecular effects, with their toxicity largely determined by their structure. Continuing the investigation of the cytotoxicity of organotin (IV) dithiocarbamates, this mini-review delves into the appropriate method for synthesis and discusses the elemental and spectroscopic analyses and potential cytotoxic effects of these compounds from articles published since 2010.

Matched MeSH terms: Structure-Activity Relationship
Fulltext Correction to: Ceramicines M-P from Chisocheton ceramicus: isolation and structure-activity relationship study

Nugroho AE, Hashimoto A, Wong CP, Yokoe H, Tsubuki M, Kaneda T, et al.

J Nat Med, 2019 Jun;73(3):682.
PMID: 30945063 DOI: 10.1007/s11418-019-01301-y

The article Ceramicines M-P from Chisocheton ceramicus: isolation and structure-activity relationship study.

Matched MeSH terms: Structure-Activity Relationship
Fulltext Validation of quantitative structure-activity relationship (QSAR) model for photosensitizer activity prediction

Frimayanti N, Yam ML, Lee HB, Othman R, Zain SM, Rahman NA

Int J Mol Sci, 2011;12(12):8626-44.
PMID: 22272096 DOI: 10.3390/ijms12128626

Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r(2) value, r(2) (CV) value and r(2) prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC(50) values ranging from 0.39 μM to 7.04 μM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r(2) prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set.

Matched MeSH terms: Quantitative Structure-Activity Relationship*
High-dimensional QSAR modelling using penalized linear regression model with L1/2-norm

Algamal ZY, Lee MH, Al-Fakih AM, Aziz M

SAR QSAR Environ Res, 2016 Sep;27(9):703-19.
PMID: 27628959 DOI: 10.1080/1062936X.2016.1228696

In high-dimensional quantitative structure-activity relationship (QSAR) modelling, penalization methods have been a popular choice to simultaneously address molecular descriptor selection and QSAR model estimation. In this study, a penalized linear regression model with L1/2-norm is proposed. Furthermore, the local linear approximation algorithm is utilized to avoid the non-convexity of the proposed method. The potential applicability of the proposed method is tested on several benchmark data sets. Compared with other commonly used penalized methods, the proposed method can not only obtain the best predictive ability, but also provide an easily interpretable QSAR model. In addition, it is noteworthy that the results obtained in terms of applicability domain and Y-randomization test provide an efficient and a robust QSAR model. It is evident from the results that the proposed method may possibly be a promising penalized method in the field of computational chemistry research, especially when the number of molecular descriptors exceeds the number of compounds.

Matched MeSH terms: Quantitative Structure-Activity Relationship*
A sparse QSRR model for predicting retention indices of essential oils based on robust screening approach

Al-Fakih AM, Algamal ZY, Lee MH, Aziz M

SAR QSAR Environ Res, 2017 Aug;28(8):691-703.
PMID: 28976224 DOI: 10.1080/1062936X.2017.1375010

A robust screening approach and a sparse quantitative structure-retention relationship (QSRR) model for predicting retention indices (RIs) of 169 constituents of essential oils is proposed. The proposed approach is represented in two steps. First, dimension reduction was performed using the proposed modified robust sure independence screening (MR-SIS) method. Second, prediction of RIs was made using the proposed robust sparse QSRR with smoothly clipped absolute deviation (SCAD) penalty (RSQSRR). The RSQSRR model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], Y-randomization test, [Formula: see text], [Formula: see text], and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of the RSQSRR for training dataset outperform the other two used modelling methods. The RSQSRR shows the highest [Formula: see text], [Formula: see text], and [Formula: see text], and the lowest [Formula: see text]. For the test dataset, the RSQSRR shows a high external validation value ([Formula: see text]), and a low value of [Formula: see text] compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed RSQSRR is an efficient approach for modelling high dimensional QSRRs and the method is useful for the estimation of RIs of essential oils that have not been experimentally tested.

Matched MeSH terms: Quantitative Structure-Activity Relationship*
Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm

Alharthi AM, Kadir DH, Al-Fakih AM, Algamal ZY, Al-Thanoon NA, Qasim MK

SAR QSAR Environ Res, 2023;34(10):831-846.
PMID: 37885432 DOI: 10.1080/1062936X.2023.2261855

The horse herd optimization algorithm (HOA), one of the more contemporary metaheuristic algorithms, has demonstrated superior performance in a number of challenging optimization tasks. In the present work, the descriptor selection issue is resolved by classifying different essential oil retention indices using the binary form, BHOA. Based on internal and external prediction criteria, Z-shape transfer functions (ZTF) were tested to verify their efficiency in improving BHOA performance in QSPR modelling for predicting retention indices of essential oils. The evaluation criteria involved the mean-squared error of the training and testing datasets (MSE), and leave-one-out internal and external validation (Q2). The degree of convergence of the proposed Z-shaped transfer functions was compared. In addition, K-fold cross validation with k = 5 was applied. The results show that ZTF, especially ZTF1, greatly improves the performance of the original BHOA. Comparatively speaking, ZTF, especially ZTF1, exhibits the fastest convergence behaviour of the binary algorithms. It chooses the fewest descriptors and requires the fewest iterations to achieve excellent prediction performance.

Matched MeSH terms: Quantitative Structure-Activity Relationship*
QSPR prediction of the hydroxyl radical rate constant of water contaminants

Borhani TN, Saniedanesh M, Bagheri M, Lim JS

Water Res, 2016 07 01;98:344-53.
PMID: 27124124 DOI: 10.1016/j.watres.2016.04.038

In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.

Matched MeSH terms: Quantitative Structure-Activity Relationship*
QSAR modeling approaches to identify a novel ACE2 inhibitor that selectively bind with the C and N terminals of the ectodomain

Jawarkar RD, Zaki MEA, Al-Hussain SA, Al-Mutairi AA, Samad A, Mukerjee N, et al.

J Biomol Struct Dyn, 2024 Mar;42(5):2550-2569.
PMID: 37144753 DOI: 10.1080/07391102.2023.2205948

Due to the high rates of drug development failure and the massive expenses associated with drug discovery, repurposing existing drugs has become more popular. As a result, we have used QSAR modelling on a large and varied dataset of 657 compounds in an effort to discover both explicit and subtle structural features requisite for ACE2 inhibitory activity, with the goal of identifying novel hit molecules. The QSAR modelling yielded a statistically robust QSAR model with high predictivity (R2tr=0.84, R2ex=0.79), previously undisclosed features, and novel mechanistic interpretations. The developed QSAR model predicted the ACE2 inhibitory activity (PIC50) of 1615 ZINC FDA compounds. This led to the detection of a PIC50 of 8.604 M for the hit molecule (ZINC000027990463). The hit molecule's docking score is -9.67 kcal/mol (RMSD 1.4). The hit molecule revealed 25 interactions with the residue ASP40, which defines the N and C termini of the ectodomain of ACE2. The HIT molecule conducted more than thirty contacts with water molecules and exhibited polar interaction with the ARG522 residue coupled with the second chloride ion, which is 10.4 nm away from the zinc ion. Both molecular docking and QSAR produced comparable findings. Moreover, MD simulation and MMGBSA studies verified docking analysis. The MD simulation showed that the hit molecule-ACE2 receptor complex is stable for 400 ns, suggesting that repurposed hit molecule 3 is a viable ACE2 inhibitor.

Matched MeSH terms: Quantitative Structure-Activity Relationship*
Fulltext Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors

Husin MN, Khan AR, Awan NUH, Campena FJH, Tchier F, Hussain S

PLoS One, 2024;19(5):e0302276.
PMID: 38713692 DOI: 10.1371/journal.pone.0302276

Based on topological descriptors, QSPR analysis is an incredibly helpful statistical method for examining many physical and chemical properties of compounds without demanding costly and time-consuming laboratory tests. Firstly, we discuss and provide research on kidney cancer drugs using topological indices and done partition of the edges of kidney cancer drugs which are based on the degree. Secondly, we examine the attributes of nineteen drugs casodex, eligard, mitoxanrone, rubraca, and zoladex, etc and among others, using linear QSPR model. The study in the article not only demonstrates a good correlation between TIs and physical characteristics with the QSPR model being the most suitable for predicting complexity, enthalpy, molar refractivity, and other factors and a best-fit model is attained in this study. This theoretical approach might benefit chemists and professionals in the pharmaceutical industry to forecast the characteristics of kidney cancer therapies. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient and suitable treatment options in therapeutic targeting. We also employed multicriteria decision making techniques like COPRAS and PROMETHEE-II for ranking of said disease treatment drugs and physicochemical characteristics.

Matched MeSH terms: Quantitative Structure-Activity Relationship*
The Wiener and Zagreb indices of conjugacy class graph of the Dihedral groups

Nur Idayu Alimon, Nor Haniza Sarmin, Ahmad Erfanian

MATEMATIKA, 2019;35(1):51-57.
MyJurnal

Topological indices are numerical values that can be analysed to predict the chemical properties of the molecular structure and the topological indices are computed for a graph related to groups. Meanwhile, the conjugacy class graph of is defined as a graph with a vertex set represented by the non-central conjugacy classes of . Two distinct vertices are connected if they have a common prime divisor. The main objective of this article is to find various topological indices including the Wiener index, the first Zagreb index and the second Zagreb index for the conjugacy class graph of dihedral groups of order where the dihedral group is the group of symmetries of regular polygon, which includes rotations and reflections. Many topological indices have been determined for simple and connected graphs in general but not graphs related to groups. In this article, the Wiener index and Zagreb index of conjugacy class graph of dihedral groups are generalized.

Matched MeSH terms: Quantitative Structure-Activity Relationship
Fulltext 3D-QSAR modelling dataset of bioflavonoids for predicting the potential modulatory effect on P-glycoprotein activity

Wongrattanakamon P, Lee VS, Nimmanpipug P, Jiranusornkul S

Data Brief, 2016 Dec;9:35-42.
PMID: 27626051 DOI: 10.1016/j.dib.2016.08.004

The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consisted of 23 bioflavonoids was used as a training set. Regarding the obtained MLR QSAR model, R of 0.963, R (2)=0.927, [Formula: see text], SEE=0.197, F=33.849 and q (2)=0.927 were achieved. The true predictabilities of QSAR model were justified by evaluation with the external dataset (Table 4). The pFARs of representative flavonoids were predicted by MLR QSAR modelling. The data showed that internal and external validations may generate the same conclusion.

Matched MeSH terms: Quantitative Structure-Activity Relationship
Fulltext Hits-to-Lead Optimization of the Natural Compound 2,4,6-Trihydroxy-3-geranyl-acetophenone (tHGA) as a Potent LOX Inhibitor: Synthesis, Structure-Activity Relationship (SAR) Study, and Computational Assignment

Ng CH, Rullah K, Abas F, Lam KW, Ismail IS, Jamaludin F, et al.

Molecules, 2018 Sep 30;23(10).
PMID: 30274341 DOI: 10.3390/molecules23102509

A new series of 2,4,6-trihydroxy-3-geranyl-acetophenone (tHGA) analogues were synthesized and evaluated for their lipoxygenase (LOX) inhibitory activity. Prenylated analogues 4a⁻g (half maximal inhibitory concentration (IC50) values ranging from 35 μ M to 95 μ M) did not exhibit better inhibitory activity than tHGA (3a) (IC50 value: 23.6 μ M) due to the reduction in hydrophobic interaction when the alkyl chain length was reduced. One geranylated analogue, 3d, with an IC50 value of 15.3 μ M, exhibited better LOX inhibitory activity when compared to tHGA (3a), which was in agreement with our previous findings. Kinetics study showed that the most active analogue (3e) and tHGA (3a) acted as competitive inhibitors. The combination of in silico approaches of molecular docking and molecular dynamic simulation revealed that the lipophilic nature of these analogues further enhanced the LOX inhibitory activity. Based on absorption, distribution, metabolism, excretion, and toxicity (ADMET) and toxicity prediction by komputer assisted technology (TOPKAT) analyses, all geranylated analogues (3a⁻g) showed no hepatotoxicity effect and were biodegradable, which indicated that they could be potentially safe drugs for treating inflammation.

Matched MeSH terms: Structure-Activity Relationship
Pharmacophore Modelling and Virtual Screening Studies for the Discovery of Potential Natural Products Based PDE1B Inhibitor Lead Compounds

Shy TW, Gaurav A

Cent Nerv Syst Agents Med Chem, 2021;21(3):195-204.
PMID: 34970959 DOI: 10.2174/1871524922666211231115638

AIM: The aim of the present study was to apply pharmacophore based virtual screening to a natural product database to identify potential PDE1B inhibitor lead compounds for neurodegenerative and neuropsychiatric disorders.
BACKGROUND: Neurodegenerative and neuropsychiatric disorders are a major health burden globally. The existing therapies do not provide optimal relief and are associated with substantial adverse effects. This has resulted in a huge unmet medical need for newer and more effective therapies for these disorders. Phosphodiesterase (PDEs) enzymes have been identified as potential targets of drugs for neurodegenerative and neuropsychiatric disorders, and one of the subtypes, i.e., PDE1B, accounts for more than 90 % of total brain PDE activity associated with learning and memory process, making it an interesting drug target for the treatment of neurodegenerative disorders.
OBJECTIVES: The present study has been conducted to identify potential PDE1B inhibitor lead compounds from the natural product database.
METHODS: Ligand-based pharmacophore models were generated and validated; they were then employed for virtual screening of Universal Natural Products Database (UNPD) followed by docking with PDE1B to identify the best hit compound.
RESULTS: Virtual screening led to the identification of 85 compounds which were then docked into the active site of PDE1B. Out of the 85 compounds, six showed a higher affinity for PDE1B than the standard PDE1B inhibitors. The top scoring compound was identified as Cedreprenone.
CONCLUSION: Virtual screening of UNPD using Ligand based pharmacophore led to the identification of Cedreprenone, a potential new natural PDE1B inhibitor lead compound.

Matched MeSH terms: Quantitative Structure-Activity Relationship
Fulltext Ceramicines M-P from Chisocheton ceramicus: isolation and structure-activity relationship study

Nugroho AE, Hashimoto A, Wong CP, Yokoe H, Tsubuki M, Kaneda T, et al.

J Nat Med, 2018 Jan;72(1):64-72.
PMID: 28822030 DOI: 10.1007/s11418-017-1109-2

Ceramicines are a series of limonoids which were isolated from the bark of Malaysian Chisocheton ceramicus (Meliaceae) and show various biological activities. Ceramicine B, in particular, has been reported to show a strong lipid droplet accumulation (LDA) inhibitory activity on a mouse pre-adipocyte cell line (MC3T3-G2/PA6). With the purpose of discovering compounds with stronger activity than ceramicine B, we further investigated the constituents of C. ceramicus. As a result, from the bark of C. ceramicus four new ceramicines (ceramicines M-P, 1-4) were isolated, and their structures were determined on the basis of NMR and mass spectroscopic analyses in combination with NMR chemical shift calculations. LDA inhibitory activity of 1-4 was evaluated. Compounds 1-3 showed LDA inhibitory activity, and 3 showed better selectivity than ceramicine B while showing activity at the same order of magnitude as ceramicine B. Since 3, which possess a carbonyl group at C-7, showed better selectivity than 5, which possess a 7α-OH group, while showing activity at the same order of magnitude as 5, we also investigated the effect of the substituent at C-7 by synthesizing several derivatives and evaluating their LDA inhibitory activity. Accordingly, we confirmed the importance of the presence of a 7α-OH group to the LDA inhibitory activity.

Matched MeSH terms: Structure-Activity Relationship
Ceramicines U-Z from Chisocheton ceramicus and structure-antimalarial activity relationship study

Nugroho AE, Komuro T, Kawaguchi T, Shindo Y, Wong CP, Hirasawa Y, et al.

J Nat Med, 2024 Jan;78(1):68-77.
PMID: 37690111 DOI: 10.1007/s11418-023-01746-2

Ceramicines are a series of limonoids which were isolated from the barks of Malaysian Chisocheton ceramicus (Meliaceae), and were known to show various biological activity. Six new limonoids, ceramicines U-Z (1-6), with a cyclopentanone[α]phenanthrene ring system with a β-furyl ring at C-17 were isolated from the barks of C. ceramicus. Their structures were determined on the basis of the 1D and 2D NMR analyses, and their absolute configurations were investigated by CD spectroscopy. Ceramicine W (3) exhibited potent antimalarial activity against Plasmodium falciparum 3D7 strain with IC50 value of 1.2 µM. In addition, the structure-antimalarial activity relationship (SAR) of the ceramicines was investigated to identify substituent patterns that may enhance activity. It appears that ring B and the functional groups in the vicinity of rings B and C are critical for the antimalarial activity of the ceramicines. In particular, bulky ester substituents with equatorial orientation at C-7 and C-12 greatly increase the antimalarial activity.

Matched MeSH terms: Structure-Activity Relationship
The structure characteristics, biosynthesis and health benefits of naturally occurring rare flavonoids

Bo S, Chang SK, Chen Y, Sheng Z, Jiang Y, Yang B

Crit Rev Food Sci Nutr, 2024;64(9):2490-2512.
PMID: 36123801 DOI: 10.1080/10408398.2022.2124396

Rare flavonoids, a special subclass of naturally occurring flavonoids with diverse structures including pterocarpans, aurones, neoflavonoids, homoisoflavones, diphenylpropanes, rotenoids and 2-phenylethyl-chromones. They are mainly found in legumes with numerous health benefits. Rare flavonoids are regarded as minor flavonoids due to their very limited abundance in nature. This review gives an overview of the natural occurrences of rare flavonoids from previous literatures. Recent findings on the biosynthesis of rare flavonoids have been updated by describing their structural characteristics and classifications. Recent findings on the health benefits of rare flavonoids have also been compiled and discussed. Natural rare flavonoids with various characteristics from different subclasses from plant-based food sources are stated. They show a wide range of health benefits, including antibacterial, anticancer, anti-osteoporosis and antiviral activities. Studies reviewed suggest that rare flavonoids possessing different skeletons demonstrate different characteristic bioactivities by discussing their mechanism of actions and structure-activity relationships. Besides, recent advances on the biosynthesis of rare flavonoids, such as pterocarpans, rotenoids and aurones are well-known, while the biosynthesis of other subclasses remain unknown. The perspectives and further applications of rare flavonoids using metabolic engineering strategies also be expected.

Matched MeSH terms: Structure-Activity Relationship
Limonoids from the fruits of Chisocheton erythrocarpus and their mosquito larvicidal activities

Ngadni MA, Chong SL, Hazni H, Asib N, Ishak IH, Mohmad Misnan N, et al.

Phytochemistry, 2024 Jun;222:114092.
PMID: 38604323 DOI: 10.1016/j.phytochem.2024.114092

Phytochemical study of the fruits of Chisocheton erythrocarpus (Hiern) allowed the identification of eight undescribed limonoids, namely erythrocarpines O - V (1-6, 7a and 7b), along with seven known compounds. The structures of these compounds were elucidated based on spectroscopic and HRMS data, along with electronic circular dichroism to configure the absolute configuration. Erythrocarpines O and P are γ-hydroxybutenolide analogs of mexicanolide-type limonoids while erythrocarpine Q - V are phragmalin-type limonoids possessing a 1,29-oxymethylene bridge with either benzoyl or cinnamoyl moiety in their structures. Mosquito larvicidal activity revealed that crude DCM extract of C. erythrocarpus possessed a good larvicidal effect against Aedes aegypti larvae in 48 h (LC50 = 153.0 ppm). Subsequent larvicidal activity of isolated compounds indicated that erythrocarpine G (10) and 14-deoxyxyloccensin K (11) were responsible for the enhanced larvicidal effect of the extract, reporting LC50 values of 18.55 ppm and 41.16 ppm, respectively. Moreover, residual activity testing of the crude DCM extract revealed that the duration of its larvicidal effects is up to 14 days, where it maintained a 98 % larval mortality throughout the test period, under laboratory conditions.

Matched MeSH terms: Structure-Activity Relationship
Implementing relevance feedback in ligand-based virtual screening using Bayesian inference network

Abdo A, Salim N, Ahmed A

J Biomol Screen, 2011 Oct;16(9):1081-8.
PMID: 21862688 DOI: 10.1177/1087057111416658

Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.

Matched MeSH terms: Quantitative Structure-Activity Relationship*
Optimization of antiproliferative activity of substituted phenyl 4-(2-oxoimidazolidin-1-yl) benzenesulfonates: QSAR and CoMFA analyses

Masand VH, Mahajan DT, Alafeefy AM, Bukhari SN, Elsayed NN

Eur J Pharm Sci, 2015 Sep 18;77:230-7.
PMID: 26066412 DOI: 10.1016/j.ejps.2015.06.001

Multiple separate quantitative structure-activity relationships (QSARs) models were built for the antiproliferative activity of substituted Phenyl 4-(2-Oxoimidazolidin-1-yl)-benzenesulfonates (PIB-SOs). A variety of descriptors were considered for PIB-SOs through QSAR model building. Genetic algorithm (GA), available in QSARINS, was employed to select optimum number and set of descriptors to build the multi-linear regression equations for a dataset of PIB-SOs. The best three parametric models were subjected to thorough internal and external validation along with Y-randomization using QSARINS, according to the OECD principles for QSAR model validation. The models were found to be statistically robust with high external predictivity. The best three parametric model, based on steric, 3D- and finger print descriptors, was found to have R(2)=0.91, R(2)ex=0.89, and CCCex=0.94. The CoMFA model, which is based on a combination of steric and electrostatic effects and graphically inferred using contour plots, gave F=229.34, R(2)CV=0.71 and R(2)=0.94. Steric repulsion, frequency of occurrence of carbon and nitrogen at topological distance of seven, and internal electronic environment of the molecule were found to have correlation with the anti-tumor activity of PIB-SOs.

Matched MeSH terms: Quantitative Structure-Activity Relationship*
Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression

Alharthi AM, Lee MH, Algamal ZY, Al-Fakih AM

SAR QSAR Environ Res, 2020 Aug;31(8):571-583.
PMID: 32628042 DOI: 10.1080/1062936X.2020.1782467

One of the most challenging issues when facing a Quantitative structure-activity relationship (QSAR) classification model is to deal with the descriptor selection. Penalized methods have been adapted and have gained popularity as a key for simultaneously performing descriptor selection and QSAR classification model estimation. However, penalized methods have drawbacks such as having biases and inconsistencies that make they lack the oracle properties. This paper proposes an adaptive penalized logistic regression (APLR) to overcome these drawbacks. This is done by employing a ratio (BWR) of the descriptors between-groups sum of squares (BSS) to the within-groups sum of squares (WSS) for each descriptor as a weight inside the L1-norm. The proposed method was applied to one dataset that consists of a diverse series of antimicrobial agents with their respective bioactivities against Candida albicans. By experimental study, it has been shown that the proposed method (APLR) was more efficient in the selection of descriptors and classification accuracy than the other competitive methods that could be used in developing QSAR classification models. Another dataset was also successfully experienced. Therefore, it can be concluded that the APLR method had significant impact on QSAR analysis and studies.

Matched MeSH terms: Quantitative Structure-Activity Relationship*

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