Antitumor agents. 213. Modeling of epipodophyllotoxin derivatives using variable selection k nearest neighbor QSAR method

Computational Prediction of 3,5-Diaryl-1H-Pyrazole and spiropyrazolines derivatives as potential acetylcholinesterase inhibitors for alzheimer disease treatment by 3D-QSAR, molecular docking, molecular dynamics simulation, and ADME-Tox

The efficacy of 40 synthesized variants of 3,5-diaryl-1H-pyrazole and spiropyrazoline' derivatives as acetylcholinesterase inhibitors is verified using a quantitative three-dimensional structure-activity relationship (3D-QSAR) by comparative molecular field analysis (CoMFA) and molecular similarity index analysis (CoMSIA) models. In this research, different field models proved that CoMSIA/SE model is the best model with high predictive power compared to several models (Qved2 = O.65; R2 = 0.980; R2test = 0.727). Also, contour maps produced by CoMSIA/SE model have been employed to prove the key structural needs of the activity. Consequently, six new compounds have been generated. Among these compounds, M4 and M5 were the most active but remained toxic and had poor absorption capacities. While the M1, M2, M3 and M6 remained highly active while respecting ADMET's characteristics. Molecular docking results showed compound M2 better with acetylcholinesterase than compound 22. The interactions are classical hydrogen bonding with residues TYR:124, TYR:72, and SER:293, which play a critical role in the biological activity as AChE inhibitors. MD results confirmed the docking results and showed that compound M2 had satisfactory stability with (ΔGbinding = -151.225 KJ/mol) in the active site of AChE receptor compared with compound 22 (ΔGbinding = -133.375 KJ/mol). In addition, both compounds had good stability regarding RMSD, Rg, and RMSF. The previous results show that the newly designed compound M2 is more active in the active site of AChE receptor than compound 22.Communicated by Ramaswamy H. Sarma.

Lead/Drug Discovery from Natural Resources

Natural products and their derivatives have been shown to be effective drug candidates against various diseases for many years. Over a long period of time, nature has produced an abundant and prosperous source pool for novel therapeutic agents with distinctive structures. Major natural-product-based drugs approved for clinical use include anti-infectives and anticancer agents. This paper will review some natural-product-related potent anticancer, anti-HIV, antibacterial and antimalarial drugs or lead compounds mainly discovered from 2016 to 2022. Structurally typical marine bioactive products are also included. Molecular modeling, machine learning, bioinformatics and other computer-assisted techniques that are very important in narrowing down bioactive core structural scaffolds and helping to design new structures to fight against key disease-associated molecular targets based on available natural products are considered and briefly reviewed.

Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets

Although a wide variety of machine learning (ML) algorithms have been utilized to learn quantitative structure-activity relationships (QSARs), there is no agreed single best algorithm for QSAR learning. Therefore, a comprehensive understanding of the performance characteristics of popular ML algorithms used in QSAR learning is highly desirable. In this study, five linear algorithms [linear function Gaussian process regression (linear-GPR), linear function support vector machine (linear-SVM), partial least squares regression (PLSR), multiple linear regression (MLR) and principal component regression (PCR)], three analogizers [radial basis function support vector machine (rbf-SVM), K-nearest neighbor (KNN) and radial basis function Gaussian process regression (rbf-GPR)], six symbolists [extreme gradient boosting (XGBoost), Cubist, random forest (RF), multiple adaptive regression splines (MARS), gradient boosting machine (GBM), and classification and regression tree (CART)] and two connectionists [principal component analysis artificial neural network (pca-ANN) and deep neural network (DNN)] were employed to learn the regression-based QSAR models for 14 public data sets comprising nine physicochemical properties and five toxicity endpoints. The results show that rbf-SVM, rbf-GPR, XGBoost and DNN generally illustrate better performances than the other algorithms. The overall performances of different algorithms can be ranked from the best to the worst as follows: rbf-SVM > XGBoost > rbf-GPR > Cubist > GBM > DNN > RF > pca-ANN > MARS > linear-GPR ≈ KNN > linear-SVM ≈ PLSR > CART ≈ PCR ≈ MLR. In terms of prediction accuracy and computational efficiency, SVM and XGBoost are recommended to the regression learning for small data sets, and XGBoost is an excellent choice for large data sets. We then investigated the performances of the ensemble models by integrating the predictions of multiple ML algorithms. The results illustrate that the ensembles of two or three algorithms in different categories can indeed improve the predictions of the best individual ML algorithms.

Databases and QSAR for Cancer Research

In this review, we take a survey of bioinformatics databases and quantitative structure-activity relationship studies reported in published literature. Databases from the most general to special cancer-related ones have been included. Most commonly used methods of structure-based analysis of molecules have been reviewed, along with some case studies where they have been used in cancer research. This article is expected to be of use for general bioinformatics researchers interested in cancer and will also provide an update to those who have been actively pursuing this field of research.

Click chemistry-assisted synthesis of triazolo linked podophyllotoxin conjugates as tubulin polymerization inhibitors

A series of new triazolo linked 4β-amidopodophyllotoxin conjugates (9a-l) were synthesized using click chemistry and evaluated for their antitumor activity against four human cancer cell lines. Among them, two compounds (9c and 9j) showed significant anticancer activity with IC50 values of 0.9 and 0.07 μM, respectively. Biological studies are conducted into the cell-cycle distribution of these conjugates inducing G2/M-phase arrest, apart from an increase in the levels of caspase-3 proteins, followed by apoptotic cell death. A tubulin polymerization assay analysis showed that these compounds effectively inhibit microtubule assembly in HeLa cells and, moreover, Hoechst 33258 and Immunohistochemistry staining suggest that these compounds induce cell death by apoptosis. The docking studies showed that compounds 9c and 9j interact and bind efficiently with the tubulin protein at the colchicine site.

Synthesis and Anticancer Activity of 4β-Triazole-podophyllotoxin Glycosides

A series of novel 4β-triazole-podophyllotoxin glycosides were synthesized by utilizing the Click reaction. Evaluation of cytotoxicity against a panel of five human cancer cell lines (HL-60, SMMC-7721, A-549, MCF-7, SW480) using MTT assay shows that most of these compounds show weak cytotoxicity. It was observed that compound 16 shows the highest activity with IC50 values ranging from 2.85 to 7.28 μM, which is more potent than the control drugs etoposide and cisplatin against four of five cancer cell lines tested. Compound 16 is characterized with an α-D-galactosyl residue directly linked to the triazole ring and a 4'-OH group on the E ring of the podophyllotoxin scaffold. HPLC investigation of representative compound indicates that incorporation of a sugar moiety seems to improve the chemical stability of the podophyllotoxin scaffold.

Design, synthesis, and biological and docking studies of novel epipodophyllotoxin–chalcone hybrids as potential anticancer agents

Click-chemistry based design and efficient synthesis of podophyllotoxin–chalcone conjugates as potential topoisomerase-II inhibitors towards the development of better anticancer leads.

Computational identification of RNA functional determinants by three-dimensional quantitative structure-activity relationships

Anti-infection drugs target vital functions of infectious agents, including their ribosome and other essential non-coding RNAs. One of the reasons infectious agents become resistant to drugs is due to mutations that eliminate drug-binding affinity while maintaining vital elements. Identifying these elements is based on the determination of viable and lethal mutants and associated structures. However, determining the structure of enough mutants at high resolution is not always possible. Here, we introduce a new computational method, MC-3DQSAR, to determine the vital elements of target RNA structure from mutagenesis and available high-resolution data. We applied the method to further characterize the structural determinants of the bacterial 23S ribosomal RNA sarcin–ricin loop (SRL), as well as those of the lead-activated and hammerhead ribozymes. The method was accurate in confirming experimentally determined essential structural elements and predicting the viability of new SRL variants, which were either observed in bacteria or validated in bacterial growth assays. Our results indicate that MC-3DQSAR could be used systematically to evaluate the drug-target potentials of any RNA sites using current high-resolution structural data.

Rational approaches, design strategies, structure activity relationship and mechanistic insights for anticancer hybrids

A Hybrid drug which comprises the incorporation of two drug pharmacophores in one single molecule are basically designed to interact with multiple targets or to amplify its effect through action on another bio target as one single molecule or to counterbalance the known side effects associated with the other hybrid part(.) The present review article offers a detailed account of the design strategies employed for the synthesis of anticancer agents via molecular hybridization techniques. Over the years, the researchers have employed this technique to discover some promising chemical architectures displaying significant anticancer profiles. Molecular hybridization as a tool has been particularly utilized for targeting tubulin protein as exemplified through the number of research papers. The microtubule inhibitors such as taxol, colchicine, chalcones, combretasatin, phenstatins and vinca alkaloids have been utilized as one of the functionality of the hybrids and promising results have been obtained in most of the cases with some of the tubulin based hybrids exhibiting anticancer activity at nanomolar level. Linkage with steroids as biological carrier vector for anticancer drugs and the inclusion of pyrrolo [2,1-c] [1,4]benzodiazepines (PBDs), a family of DNA interactive antitumor antibiotics derived from Streptomyces species in hybrid structure based drug design has also emerged as a potential strategy. Various heteroaryl based hybrids in particular isatin and coumarins have also been designed and reported to posses' remarkable inhibitory potential. Apart from presenting the design strategies, the article also highlights the structure activity relationship along with mechanistic insights revealed during the biological evaluation of the hybrids.

Synthesis and anticancer activity of glucosylated podophyllotoxin derivatives linked via 4β-triazole rings

A series of 4β-triazole-linked glucose podophyllotoxin conjugates have been designed and synthesized by employing a click chemistry approach. All the compounds were evaluated for their anticancer activity against a panel of five human cancer cell lines (HL-60, SMMC-7721, A-549, MCF-7, SW480) using MTT assays. Most of these triazole derivatives have good anticancer activity. Among them, compound 35 showed the highest potency against all five cancer cell lines tested, with IC₅₀ values ranging from 0.59 to 2.90 μM, which is significantly more active than the drug etoposide currently in clinical use. Structure-activity relationship analysis reveals that the acyl substitution on the glucose residue, the length of oligoethylene glycol linker, and the 4'-demethylation of podophyllotoxin scaffold can significantly affect the potency of the anticancer activity. Most notably, derivatives with a perbutyrylated glucose residue show much higher activity than their counterparts with either a free glucose or a peracetylated glucose residue.

Quinazolino linked 4β-amidopodophyllotoxin conjugates regulate angiogenic pathway and control breast cancer cell proliferation

A series of new conjugates of quinazolino linked 4β-amidopodophyllotoxins 10aa-af and 10ba-bf were synthesized and evaluated for their anticancer activity against human pancreatic carcinoma (Panc-1) as well as breast cancer cell lines such as MCF-7 and MDA-MB-231 by employing MTT assay. Among these conjugates, some of them like 10bc, 10bd, 10be and 10bf exhibited high potency of cytotoxicity. Flow cytometric analysis showed that these conjugates arrested the cell cycle in the G2/M phase and caused the increase in expression of p53 and cyclin B1 protein with concomitant decrease in Cdk1 thereby suggesting the inhibitory action of these conjugates on mitosis. Interestingly, we observed a decrease in expression of proteins that control the tumor micro environment such as VEGF-A, STAT-3, ERK1/2, ERK-p, AKT-1 ser 473 phosphorylation in compounds treated breast cancer cells. Further, these effective conjugates have exhibited inhibitory action on integrin (αVβIII). Furthermore, the MCF-7 cells that were arrested and lost the proliferative capacity undergo mitochondrial mediated apoptosis by activation of caspases-9. Thus these conjugates have the potential to control breast cancer cell growth by effecting tumor angiogenesis and invasion.

Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening

Quantitative structure-activity relationship (QSAR) models have been developed for a data set of 3133 compounds defined as either active or inactive against P. falciparum. Because the data set was strongly biased toward inactive compounds, different sampling approaches were employed to balance the ratio of actives versus inactives, and models were rigorously validated using both internal and external validation approaches. The balanced accuracy for assessing the antimalarial activities of 70 external compounds was between 87% and 100% depending on the approach used to balance the data set. Virtual screening of the ChemBridge database using QSAR models identified 176 putative antimalarial compounds that were submitted for experimental validation, along with 42 putative inactives as negative controls. Twenty five (14.2%) computational hits were found to have antimalarial activities with minimal cytotoxicity to mammalian cells, while all 42 putative inactives were confirmed experimentally. Structural inspection of confirmed active hits revealed novel chemical scaffolds, which could be employed as starting points to discover novel antimalarial agents.

Synthesis and anticancer activity of heteroaromatic linked 4β-amido podophyllotoxins as apoptotic inducing agents

A series of different heteroaromatic linked 4β-amidopodophyllotoxin conjugates (16a-i, 17a-i and 18a-d) were synthesized and evaluated for anticancer activity against five human cancer cell lines. Among the series, one of the compound 17g showed significant antiproliferative activity in A549 (lung cancer) cell line. Flow cytometric analysis showed that 17g arrested the cell cycle in the G2/M phase leading to caspase-3 dependent apoptotic cell death. Further, Hoechst 33258 staining and DNA fragmentation assay also suggests that 17g induces cell death by apoptosis.

Synthesis of 4β-triazole-podophyllotoxin derivatives by azide-alkyne cycloaddition and biological evaluation as potential antitumor agents

A representative synthetic process of derivatizing the natural product podophyllotoxin utilizing the copper-catalyzed azide-alkyne cycloaddition (CuAAC) is described including molecular design, reaction optimization and X-ray structure confirmation. Evaluation of cytotoxicity against human cancer cell lines (Hela, K562 and K562/A02) using MTT assay proves that these triazole derivatives have good antitumor activities. High activities toward the drug resistant K562/A02 cell line reveal promising future for these derivatives. The rarely prepared 1,5-disubstituted triazole isomers, which would be omitted by the "click chemistry", were found to have superior cytotoxicities to that of the 1,4-disubstituted isomers.

Synthesis and antitumor activity of novel podophyllotoxin derivatives against multidrug-resistant cancer cells

Seven novel 4β-N-substituted podophyllotoxin derivatives with indole rings were prepared and evaluated for cytotoxicity against human cancer cell lines HeLa, KB, KBV, K562, and K562/AO2. Most of them demonstrated improved antitumor activity and weak multidrug resistance compared to the drugs currently available.

Synthetic and application perspectives of azapodophyllotoxins: alternative scaffolds of podophyllotoxin

Podophyllotoxin (1) has been known to possess anti-tumor activity and is still considered an important lead for research and development of antineoplastic agents. Derivatives of podophyllotoxin, namely etoposide (2), etopophos (3) and teniposide (4) have been developed and are currently used in clinic for the treatment of a variety of malignancies. These agents are also used in combination therapies with other drugs. Due to the drug resistance developed by cancer cells as well as side effects associated with the use of these agents in clinic, the search for new effective anticancer analogues of podophyllotoxin remains an intense area of research. The structural complexity of podophyllotoxin, arising from the presence of four stereogenic carbons in ring C has restricted most of the structural activity relationship (SAR) studied by derivatization of the parent natural product rather than by de novo multi-step chemical synthesis. These issues provide strong impetus to a search for analogues of 1 with simplified structures, which can be accessible via short synthetic sequences from simple starting materials. Even if such initial compounds might have diminished cytotoxic potencies compared with the parent cyclolignan, the ease of preparation of carefully designed libraries of analogues would lead to more informative SAR studies and expeditious structure optimization. In this regard, during the last two decades considerable efforts have been made to synthesize aza- analogs of podophyllotoxin, i. e. aza-podophyllotoxins, with hetero atoms at different positions of the podophyllotoxin skeleton, while keeping the basic podophyllotoxin structure. Recently, there have been significant efforts towards the convenient synthesis of aza-analogs of 1. The use of multicomponent reactions (MCRs) and the synergies of ultrasound and microwave irradiations have increased the synthetic speed and variety of azapodophyllotoxins which are and will be available to be tested against a diverse population of carcinomas and other diseases. It has been reported that several aza-podophyllotoxins retain a great fraction of the cytotoxicity associated with the parent lignan. This review focuses on the strategies towards synthesis of various aza-podophyllotoxin analogues and their biological activities.

Synthesis of Novel Functionalized 4-Aza-2,3-Didehydropodophyllotoxin Derivatives with Potential Antitumor Activity

Novel arylamino alcohols were synthesized and these alcohols were used to prepare 12 novel N-(2-hydroxy-ethyl)-2,3-didehydroazapodophyllotoxins, in one step, by simple reflux in ethanol. Isolated yields in the range of 50-70% were obtained.

Design and synthesis of C-ring lactone- and lactam-based podophyllotoxin analogues as anticancer agents

A series of novel podophyllotoxin (PDT) analogues was synthesized in which the lactone moiety was shifted to C ring. Some of the derivatives were also synthesized with modified A ring. Analogues 23 and 25 exhibited potent in vitro cytotoxicity against colon cancer (CaCO(2)) cell line. p-Demethylated E-ring analogues exhibited better potency than the corresponding methylated analogues. These analogues showed toxicity comparable to PDT against human erythrocytes albeit at much higher concentrations (100 microg/ml) than their cytotoxicity values.

Anticancer activity and quantitative-structure activity relationship (QSAR) studies of a series of antioxidant/anti-inflammatory aryl-acetic and hydroxamic acids

A series of aryl-acetic acids and hydroxamic acids possessing antioxidant/anti-inflammatory activities were tested for anticancer activity using different cancer cell lines. The compounds have low antitumor activity considering the 1/IC(50) values attained for the cell lines. Compound 5iv presents the best anticancer activity. Moreover, they depict the same activity pattern, suggesting similar mechanisms of action correlated to their antioxidant activities. The obtained results subjected in a QSAR analysis. It seems reasonable to conclude that the same molecular structural features are responsible for the compounds biological activity, these being the electron accepting/donating ability and the molar volume. For all cellular lines (HT-29, A-549 and OAW-42) log 1/IC(50) exhibits a reasonable correlation with a two parameters relationship in which the Esp-min and D term are present. Apart from Esp-min the other descriptor found important for anticancer activity is the molar volume (MgVol). The QSAR analyses did not indicate any role for lipophilicity Electrostatic potential, dipole moment and the bulk, primarily affect the biological response.

Novel 4 beta-anilino-podophyllotoxin derivatives: design synthesis and biological evaluation as potent DNA-topoisomerase II poisons and anti-MDR agents

A new series of 4 beta-anilino-podophyllotoxin analogs have been designed, synthesized and evaluated their bioactivities as novel DNA-topoisomerase II poisons as well as P-glycoprotein (P-gp)-dependent multidrug resistance (MDR) inhibitors. The new compounds show improved potency and efficacy with respect to the parent molecule etoposide (VP-16), one of the semisynthetic derivatives of podophyllotoxin. The treatment of 5k-n in KB/VCR cells caused G(2)/M phase arrest and finally induced apoptosis. Furthermore, molecular docking is applied to testify that 5k-n could not be the substrates of P-gp, which is consistent with the result of MDR1 and P-glycoprotein express tests. The most potent compound 5n is chosen for in vivo studies, the administration of 5n was effective in treatment of cancer with a lower dose than VP-16 in drug-sensitive xenograft model and drug-resistant xenograft model. Compound 5n is a potential drug candidate for anticancer chemotherapy.

Docking and 3D-QSAR studies of acetohydroxy acid synthase inhibitor sulfonylurea derivatives

Docking and three dimensional quantitative-structure activity relationship (3D-QSAR) studies were performed on acetohydroxy acid synthase (AHAS) inhibitor sulfonylurea analogues with potential herbicidal activity. The 3D-QSAR studies were carried out using shape, spatial and electronic descriptors along with a few structural parameters. Genetic function approximation (GFA) was used as the chemometric tool for this analysis. The whole data set (n = 45) was divided into a training set (75% of the data set) and a test set (remaining 25%) on the basis of the K-means clustering technique on a standardised topological, physicochemical and structural descriptor matrix. Models developed from the training set were used to predict the activity of the test set compounds. All models were validated internally, externally and using the Y-randomisation technique. Docking studies suggested that the molecules bind within a pocket of the enzyme formed by some important amino acid residues (Met351, Asp375, Arg377, Gly509, Met570 and Val571). In QSAR studies, molecular shape analysis showed that bulky substitution at the R(1) position may enhance AHAS inhibitory activity. Charged surface area descriptors suggested that negative charge distributed over a large surface area may enhance this activity. The hydrogen bond acceptor parameter supported the charged surface area descriptors and suggested that, for better activity, the number of electronegative atoms present in the molecule should be high. The spatial descriptors show that, for better activity, the molecules should possess a bulky substituent and a small substitution at the R(2) and R(3) positions, respectively.

New picropodophyllin analogs via palladium-catalyzed allylic alkylation-Hiyama cross-coupling sequences

Unsaturated malonyl esters underwent Pd-catalyzed intramolecular allylic alkylation to give 4-vinyl-substituted gamma-lactones. In contrast to the formerly studied cyclization of malonamides, this reaction could be achieved only with a substrate incorporating a suitably positioned silicon moiety, which directs the ionization toward the desired eta(3)-allylpalladium complex. The resulting 4-[dimethyl-(2-thienyl)silylvinyl]lactone could be subsequently engaged into Hiyama couplings with various iodoarenes, to give the corresponding 4-(alpha-styryl)-gamma-lactones. The use of a specifically substituted iodoarene generated an advanced tetracyclic lactone intermediate incorporating rings A-D of lignans belonging to the podophyllotoxin family. Subsequent electrophilic aromatic substitution with a variety of electron-rich arenes afforded the target picropodophyllin analogs.

Plant-derived natural product research aimed at new drug discovery

Many important bioactive compounds have been discovered from natural sources using bioactivity-directed fractionation and isolation (BDFl) [Balunas MJ, Kinghorn AD (2005) Drug discovery from medicinal plants. Life Sci 78:431-441]. Continuing discovery has also been facilitated by the recent development of new bioassay methods. These bioactive compounds are mostly plant secondary metabolites, and many naturally occurring pure compounds have become medicines, dietary supplements, and other useful commercial products. Active lead compounds can also be further modified to enhance the biological profiles and developed as clinical trial candidates. In this review, the authors will summarize research on many different useful compounds isolated or developed from plants with emphasis placed on those recently discovered by the authors' laboratories as antitumor and anti-HIV clinical trial candidates.

Synthesis, anti-fungal activity evaluation and QSAR studies on podophyllotoxin derivatives

The anti-fungal and cytotoxic activites of podophyllotoxin and seven C-4 substituted podophyllotoxin ester derivatives, viz: trans-cinnamyl, cis-cinnamyl, o-methoxy cinnamyl, dimethyl acrylyl, p-methoxy phenyl acetyl, 3,4-dimethoxy phenyl acetyl and 2,5-dimethoxy phenyl acetyl esters were evaluated on four fungi, viz: Macrophomina phaseolina, Fusarium oxysporum, Myrrothecium verrucarria and Asperigillus candidus, The podophyllotoxin derivatives were synthesised and their structures were elucidated. Quantitative structure activity relationships were developed between the activity of these compounds against the four fungi and molecular descriptors. The linear regression models developed had one to two descriptors. For all the cases the r 2 was in the range of 0.73 to 0.96, indicating good data fit and q 2 was in the range of 0.60 to 0.68, indicating that the predictive capabilities of the models were acceptable. Solvent accessible surface area (namely the partial positive solvent-accessible surface area), A log P, highest occupied molecular orbital and conformational energy were identified as important descriptors.

Probing the anticancer activity of nucleoside analogues: a QSAR model approach using an internally consistent training set

The cancer research community has begun to address the in silico modeling approaches, such as quantitative structure-activity relationships (QSAR), as an important alternative tool for screening potential anticancer drugs. With the compilation of a large dataset of nucleosides synthesized in our laboratories, or elsewhere, and tested in a single cytotoxic assay under the same experimental conditions, we recognized a unique opportunity to attempt to build predictive QSAR models. Here, we report a systematic evaluation of classification models to probe anticancer activity, based on linear discriminant analysis along with 2D-molecular descriptors. This strategy afforded a final QSAR model with very good overall accuracy and predictability on external data. Finally, we search for similarities between the natural nucleosides, present in RNA/DNA, and the active nucleosides well-predicted by the model. The structural information then gathered and the QSAR model per se shall aid in the future design of novel potent anticancer nucleosides.

QSAR--how good is it in practice? Comparison of descriptor sets on an unbiased cross section of corporate data sets

The quality of QSAR (Quantitative Structure-Activity Relationships) predictions depends on a large number of factors including the descriptor set, the statistical method, and the data sets used. Here we study the quality of QSAR predictions mainly as a function of the data set and descriptor type using partial least squares as the statistical modeling method. The study makes use of the fact that we have access to a large number of data sets and to a variety of different QSAR descriptors. The main conclusions are that the quality of the predictions depends both on the data set and the descriptor used. The quality of the predictions correlates positively with the size of the data set and the range of biological activities. There is no clear dependence of the quality of the predictions on the complexity of the data set. All of the descriptors tested produced useful predictions for some of the data sets. None of the descriptors is best for all data sets; it is therefore necessary to test in each individual case, which descriptor produces the best model. In our tests, 2D fragment based descriptors usually performed better than simpler descriptors based on augmented atom types. Possible reasons for these observations are discussed.

Supervised self-organizing maps in drug discovery. 2. Improvements in descriptor selection and model validation

The modeling of nonlinear descriptor-target relationships is a topic of considerable interest in drug discovery. We, herein, continue reporting the use of the self-organizing map-a nonlinear, topology-preserving pattern recognition technique that exhibits considerable promise in modeling and decoding these relationships. Since simulated annealing is an efficient tool for solving optimization problems, we combined the supervised self-organizing map with simulated annealing to build high-quality, highly predictive quantitative structure-activity/property relationship models. This technique was applied to six data sets representing a variety of biological endpoints. Since a high statistical correlation in the training set does not indicate a highly predictive model, the quality of all the models was confirmed by withholding a portion of each data set for external validation. Finally, we introduce new cross-validation and dynamic partitioning techniques to address model overfitting and assessment.

Identifying Biologically Active Compound Classes Using Phenotypic Screening Data and Sampling Statistics

Scoring the activity of compounds in phenotypic high-throughput assays presents a unique challenge because of the limited resolution and inherent measurement error of these assays. Techniques that leverage the structural similarity of compounds within an assay can be used to improve the hit-recovery rate from screening data. A technique is presented that uses clustering and sampling statistics to predict likely compound activity by scoring entire structural classes. A set of phenotypic assays performed against a commercially available compound library was used as a test set. Using the class-scoring technique, the resultant activity prediction scores were more reproducible than individual assay measurements, and class scoring recovered known active compounds more efficiently than individual assay measurements because class scoring had fewer false positives. Known biologically active compounds were recovered 87% of the time using class scores, suggesting a low false-negative rate that compared well to individual assay measurements. In addition, many weak and potentially novel classes of active compounds, overlooked by individual assay measurements, were suggested.

Synthesis and biological evaluation of new 4beta-anilino- and 4beta-imido-substituted podophyllotoxin congeners

A series of C-4-anilino- and C-4-imido-substituted new podophyllotoxin congeners have been designed, synthesized, and evaluated for their cytotoxicity and DNA topoisomerase-II (topo-II) inhibition potential. Some of these compounds have exhibited promising in vitro anticancer and topo-II inhibition activity.

Synthesis and Antitumor Activity of Some Curcumin Analogs

In this study, four new curcurmin analogs (compounds 1, 2, 17 and 18) were synthesized. 17 [3,5-bis(4-hydroxy-3-methoxy-5-methylcinnamyl)N-methylpiperidone] showed high activity with GI50, TGI, and LC50 MG-MID values of 21.3, 70.7, and 97.7 microM, respectively. 18 [3,5-bis(4-hydroxy-3-methoxy-5-methylcinnamyl)-N-ethylpiperidone] showed the highest activity in this study with GI50, TGI, LC50 MG-MID values of 4.4, 33.8, 89.1 microM, respectively. 18 is even more active than curcumin with GI50, TGI, LC50 MG-MID values of 38.4, 35.6, 66.0 microM; respectively. 8 showed moderate selectivity towards Leukemia cell line-subpanel with a ratio of 5.6 (curcumin ratio: 1.2 for the same subpanel). The in vitro anti-tumor screening reveals that the results go hand in hand with the in vitro free radical scavenging effects. The antioxidant effect of these compounds depends mainly on the stabilization of the formed phenoxy free radical for which the p-hydroxy phenyl moiety is essential. o-substitution by electron-donating groups like the o-methoxy group (and to a even higher degree by the ethoxy group) increases the stability of phenoxy free radical, hence increasing both free scavenging and anti-tumor effects. Increasing the alkyl group chain on the N in the series of substituted N-alkyl piperidones as well as the extension of conjugation, increases the stabilization of phenoxy free radical and thereby the activity towards both free radical scavenging and anti-tumor effects. This may be attributed to an increased positive inductive effect and/or increased lipophilicity of the new compounds, a fact which is proven by the superior activities of compounds 17 and 18.

Piecewise Hypersphere Modeling by Particle Swarm Optimization in QSAR Studies of Bioactivities of Chemical Compounds

As the structural diversity in a quantitative structure-activity relationship (QSAR) model increases, constructing a good model becomes increasingly difficult, and simply performing variable selection might not be sufficient to improve the model quality to make it practically usable. To combat this difficulty, an approach based on piecewise hypersphere modeling by particle swarm optimization (PHMPSO) is developed in this paper. It treats the linear models describing the sought-for subsets as hyperspheres which have different radii in the data space. According to the attribute of each hypersphere, all compounds in the training set are allocated to hyperspheres to construct submodels, and particle swarm optimization (PSO) is applied to search the optimal hyperspheres for finding satisfactory piecewise linear models. A new objective function is formulated to determine the appropriate piecewise models. The performance is assessed using three QSAR data sets. Experimental results have shown the good performance of this technique in improving the QSAR modeling.