Binary quantitative structure-activity relationship (QSAR) analysis of estrogen receptor ligands

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Human Estrogen Receptor α Antagonists. Part 1: 3-D QSAR-Driven Rational Design of Innovative Coumarin-Related Antiestrogens as Breast Cancer Suppressants through Structure-Based and Ligand-Based Studies

The estrogen receptor α (ERα) represents a 17β-estradiol-inducible transcriptional regulator that initiates the RNA polymerase II-dependent transcriptional machinery, pointed for breast cancer (BC) development via either genomic direct or genomic indirect (i.e., tethered) pathway. To develop innovative ligands, structure-based (SB) three-dimensional (3-D) quantitative structure-activity relationship (QSAR) studies have been undertaken from structural data taken from partial agonists, mixed agonists/antagonists (selective estrogen receptor modulators (SERMs)), and full antagonists (selective ERα downregulators (SERDs)) correlated with either wild-type or mutated ERα receptors. SB and ligand-based (LB) alignments allow us to rule out guidelines for the SB/LB alignment of untested compounds. 3-D QSAR models for ERα ligands, coupled with SB/LB alignment, were revealed to be useful tools to dissect the chemical determinants for ERα-based anticancer activity as well as to predict their potency. The herein developed protocol procedure was verified through the design and potency prediction of 12 new coumarin-based SERMs, namely, 3DQ-1a to 3DQ-1e, that upon synthesis turned to be potent ERα antagonists by means of either in vitro or in vivo assays (described in the second part of this study).

Teaching and Learning Computational Drug Design: Student Investigations of 3D Quantitative Structure-Activity Relationships through Web Applications

The increasing use of information technology in the discovery of new molecular entities encourages the use of modern molecular-modeling tools to help teach important concepts of drug design to chemistry and pharmacy undergraduate students. In particular, statistical models such as quantitative structure-activity relationships (QSAR)-often as its 3D QSAR variant-are commonly used in the development and optimization of a leading compound. We describe how these drug discovery methods can be taught and learned by means of free and open-source web applications, specifically the online platform www.3d-qsar.com. This new suite of web applications has been integrated into a drug design teaching course, one that provides both theoretical and practical perspectives. We include the teaching protocol by which pharmaceutical biotechnology master students at Pharmacy Faculty of Sapienza Rome University are introduced to drug design. Starting with a choice among recent articles describing the potencies of a series of molecules tested against a biological target, each student is expected to build a 3D QSAR ligand-based model from their chosen publication, proceeding as follows: creating the initial data set (Py-MolEdit); generating the global minimum conformations (Py-ConfSearch); proposing a promising mutual alignment (Py-Align); and finally, building, and optimizing a robust 3D QSAR models (Py-CoMFA). These student activities also help validate these new molecular modeling tools, especially for their usability by inexperienced hands. To more fully demonstrate the effectiveness of this protocol and its tools, we include the work performed by four of these students (four of the coauthors), detailing the satisfactory 3D QSAR models they obtained. Such scientifically complete experiences by undergraduates, made possible by the efficiency of the 3D QSAR methodology, provide exposure to computational tools in the same spirit as traditional laboratory exercises. With the obsolescence of the classic Comparative Molecular Field Analysis Sybyl host, the 3dqsar web portal offers one of the few available means of performing this well-established 3D QSAR method.

Analyzing a broader spectrum of endocrine active organic contaminants in sewage sludge with high resolution LC-QTOF-MS suspect screening and QSAR toxicity prediction

Endocrine active contaminants (EACs) in environmental samples can pose a range of toxicological threats to ecosystems, especially through their impacts on reproductive pathways mediated by the estrogen receptor. The physicochemical properties of known organic EACs vary greatly and typically require different sample preparation techniques to identify different classes of compounds. EAC sources are similarly diverse, including both endogenous compounds and anthropogenic chemicals found in personal care products, pharmaceuticals, and their transformation products, which are often disposed of to sewers at their end of use. Looking for EACs in sewage sludge proposes a bottom-up, or end-of-use and treatment approach to discover environmentally relevant EACs, since many EACs accumulate in sludges even after application of robust wastewater treatment processes. This study demonstrates an extraction and analytical method capable of detecting a broad spectrum of known and suspected EACs via High Resolution Liquid Chromatography Quadropole Time-of-Flight Mass Spectrometry (LC-QTOF-MS) suspect screening of fourteen California sewage sludge samples. Spike-recovery experiments were performed using twelve carefully selected surrogates to assess different extraction solvents, sample weights, extraction pH values, procedures for combining extracts with different extraction pH's, and solid phase extraction cartridges. Using LC-QTOF-MS, identifications of several other organic compounds in the samples were made, a goal unachievable with unit resolution mass spectrometry. Suspect screening of California sludge samples discovered 118 compounds including hormones, pharmaceuticals, phosphate flame retardants, recreational drugs, antimicrobials, and pesticides. Additionally, 22 of these identified compounds are predicted to interfere with estrogen receptors or other reproductive/developmental pathways based on the VEGA QSAR toxicity prediction model.

Computational prediction models for assessing endocrine disrupting potential of chemicals

Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.

Machine learning in chemoinformatics and drug discovery

Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.

Probing the origin of estrogen receptor alpha inhibition via large-scale QSAR study

Estrogen is an important component for the sustenance of normal physiological functions of the mammary glands, particularly for growth and differentiation. Approximately, two-thirds of breast cancers are positive for estrogen receptor (ERs), which is a predisposing factor for the growth of breast cancer cells. As such, ERα represents a lucrative therapeutic target for breast cancer that has attracted wide interest in the search for inhibitory agents. However, the conventional laboratory processes are cost- and time-consuming. Thus, it is highly desirable to develop alternative methods such as quantitative structure–activity relationship (QSAR) models for predicting ER-mediated endocrine agitation as to simplify their prioritization for future screening. In this study, we compiled and curated a large, non-redundant data set of 1231 compounds with ERα inhibitory activity (pIC₅₀). Using comprehensive validation tests, it was clearly observed that the model utilizing the substructure count as descriptors, performed well considering two objectives: using less descriptors for model development and achieving high predictive performance (R_Tr² = 0.94, Q_CV² = 0.73, and Q_Ext² = 0.73). It is anticipated that our proposed QSAR model may become a useful high-throughput tool for identifying novel inhibitors against ERα.

This study compiles a large, non-redundant set of compounds tested for ERα inhibitory activity and applies QSAR modeling for unveiling the privileged substructures governing the activity.

Development of a recombinant human ovarian (BG1) cell line containing estrogen receptor α and β for improved detection of estrogenic/antiestrogenic chemicals

Estrogenic endocrine-disrupting chemicals are found in environmental and biological samples, commercial and consumer products, food, and numerous other sources. Given their ubiquitous nature and potential for adverse effects, a critical need exists for rapidly detecting these chemicals. The authors developed an estrogen-responsive recombinant human ovarian (BG1Luc4E2) cell line recently accepted by the US Environmental Protection Agency (USEPA) and Organisation for Economic Co-operation and Development (OECD) as a bioanalytical method to detect estrogen receptor (ER) agonists/antagonists. Unfortunately, these cells appear to contain only 1 of the 2 known ER isoforms, ERα but not ERβ, and the differential ligand selectivity of these ERs indicates that the currently accepted screening method only detects a subset of total estrogenic chemicals. To improve the estrogen screening bioassay, BG1Luc4E2 cells were stably transfected with an ERβ expression plasmid and positive clones identified using ERβ-selective ligands (genistein and Br-ERβ-041). A highly responsive clone (BG1LucERβc9) was identified that exhibited greater sensitivity and responsiveness to ERβ-selective ligands than BG1Luc4E2 cells, and quantitative reverse-transcription polymerase chain reaction confirmed the presence of ERβ expression in these cells. Screening of pesticides and industrial chemicals identified chemicals that preferentially stimulated ERβ-dependent reporter gene expression. Together, these results not only demonstrate the utility of this dual-ER recombinant cell line for detecting a broader range of estrogenic chemicals than the current BG1Luc4E2 cell line, but screening with both cell lines allows identification of ERα- and ERβ-selective chemicals.

Docking-based comparative intermolecular contacts analysis as new 3-D QSAR concept for validating docking studies and in silico screening: NMT and GP inhibitors as case studies

The significant role played by docking algorithms in drug discovery combined with their serious pitfalls prompted us to envisage a novel concept for validating docking solutions, namely, docking-based comparative intermolecular contacts analysis (dbCICA). This novel approach is based on the number and quality of contacts between docked ligands and amino acid residues within the binding pocket. It assesses a particular docking configuration on the basis of its ability to align a set of ligands within a corresponding binding pocket in such a way that potent ligands come into contact with binding site spots distinct from those approached by low-affinity ligands and vice versa. In other words, dbCICA evaluates the consistency of docking by assessing the correlation between ligands' affinities and their contacts with binding site spots. Optimal dbCICA models can be translated into valid pharmacophore models that can be used as 3-D search queries to mine structural databases for new bioactive compounds. dbCICA was implemented to search for new inhibitors of candida N-myristoyl transferase as potential antifungal agents and glycogen phosphorylase (GP) inhibitors as potential antidiabetic agents. The process culminated in five selective micromolar antifungal leads and nine GP inhibitory leads.

Elaborate ligand-based modeling reveals new nanomolar heat shock protein 90α inhibitors

Heat shock protein (Hsp90α) has been recently implicated in cancer prompting several attempts to discover and optimize new Hsp90α inhibitors. Toward this end, we explored the pharmacophoric space of 83 Hsp90α inhibitors using six diverse sets of inhibitors to identify high-quality pharmacophores. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of accessing a self-consistent quantitative structure activity relationship (QSAR) of optimal predictive potential (r(67)(2)=0.811, F 42.8, r(LOO)(2)=0.748, r(PRESS)(2) (against 16 external test inhibitors) = 0.619). Three orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least three binding modes accessible to ligands within the Hsp90α binding pocket. Receiver operating characteristic (ROC) curves analysis established the validity of QSAR-selected pharmacophores. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute (NCI) list of compounds and our in-house-built drugs and agrochemicals database (DAC). Twenty-five nanomolar and low micromolar Hsp90α inhibitors were identified. The most potent were formoterol, amodaquine, primaquine, and midodrine with IC(50) values of 3, 5, 6, and 20 nM, respectively.

Bayesian methods in virtual screening and chemical biology

The Naïve Bayesian Classifier, as well as related classification and regression approaches based on Bayes' theorem, has experienced increased attention in the cheminformatics world in recent years. In this contribution, we first review the mathematical framework on which Bayes' methods are built, and then continue to discuss implications of this framework as well as practical experience under which conditions Bayes' methods give the best performance in virtual screening settings. Finally, we present an overview of applications of Bayes' methods to both virtual screening and the chemical biology arena, where applications range from bridging phenotypic and mechanistic space of drug action to the prediction of ligand-target interactions.

QSAR models for predicting cathepsin B inhibition by small molecules--continuous and binary QSAR models to classify cathepsin B inhibition activities of small molecules

Cathepsin B is a potential target for the development of drugs to treat several important human diseases. A number of inhibitors targeting this protein have been developed in the past several years. Recently, a group of small molecules were identified to have inhibitory activity against cathepsin B through high throughput screening (HTS) tests. In this study, traditional continuous and binary QSAR models were built to classify the biological activities of previously identified compounds and to distinguish active compounds from inactive compounds for drug development based on the calculated molecular and physicochemical properties. Strong correlations were obtained for the continuous QSAR models with regression correlation coefficients (r²) and cross-validated correlation coefficients (q²) of 0.77 and 0.61 for all compounds, and 0.82 and 0.68 for the compound set excluding 3 outliers, respectively. The models were further validated through the leave-one-out (LOO) method and the training-test set method. The binary models demonstrated a strong level of predictability in distinguishing the active compounds from inactive compounds with accuracies of 0.89 and 0.94 for active and inactive compounds, respectively, in non-cross-validated models. Similar results were obtained for the cross-validated models. Collectively, these results demonstrate the models’ ability to discriminate between active and inactive compounds, suggesting that the models may be used to pre-screen compounds to facilitate compound optimization and to design novel inhibitors for drug development.

Pharmacophore and QSAR modeling of estrogen receptor beta ligands and subsequent validation and in silico search for new hits

The pharmacophoric space of estrogen receptor beta (ERbeta) was explored using a set of 119 known ligands. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select optimal combinations of pharmacophoric models and physicochemical descriptors in self-consistent and predictive quantitative structure-activity relationships (QSARs) (r(96)(2)=0.79-0.83, F-statistic=40.96-36.20, r(LOO)(2)=0.74-0.76 and r(PRESS)(2) against 23 external compounds=0.54-0.56, respectively). Four binding hypotheses emerged in two optimal QSAR equations suggesting the existence of distinct binding modes accessible to ligands within ERbeta binding pocket. The close similarity among the resulting pharmacophores prompted us to merge them in two hybrid models. The hybrid pharmacophores illustrated superior receiver operator characteristic curves (ROCs) and closely resembled binding interactions suggested by docking experiments. The resulting models and associated QSAR equations were employed to screen the national cancer institute (NCI) list of compounds and an in house built database of known drugs and agrochemicals to search for new ERbeta ligands.

Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection--what can we learn from earlier mistakes?

Within the last few years a considerable amount of evaluative studies has been published that investigate the performance of 3D virtual screening approaches. Thereby, in particular assessments of protein-ligand docking are facing remarkable interest in the scientific community. However, comparing virtual screening approaches is a non-trivial task. Several publications, especially in the field of molecular docking, suffer from shortcomings that are likely to affect the significance of the results considerably. These quality issues often arise from poor study design, biasing, by using improper or inexpressive enrichment descriptors, and from errors in interpretation of the data output. In this review we analyze recent literature evaluating 3D virtual screening methods, with focus on molecular docking. We highlight problematic issues and provide guidelines on how to improve the quality of computational studies. Since 3D virtual screening protocols are in general assessed by their ability to discriminate between active and inactive compounds, we summarize the impact of the composition and preparation of test sets on the outcome of evaluations. Moreover, we investigate the significance of both classic enrichment parameters and advanced descriptors for the performance of 3D virtual screening methods. Furthermore, we review the significance and suitability of RMSD as a measure for the accuracy of protein-ligand docking algorithms and of conformational space sub sampling algorithms.

Assessing potency of c-Jun N-terminal kinase 3 (JNK3) inhibitors using 2D molecular descriptors and binary QSAR methodology

JNK3 signaling pathway is gaining interest due to its involvement in many neurological disorders. The purpose of this study was to explore for the first time the use of a large and diverse dataset in combination with binary QSAR methodology for predicting JNK3 activity class. Data were extracted from Aureus Pharma' AurSCOPE Kinase knowledge database and active or inactive classes were assigned to ligands based on IC50 biological activity. Two sets of 2D molecular descriptors (P_VSA and BCUT) were used to build models using different biological activity thresholds. The design of the models was preceded by the evaluation of the chemical space covered by the datasets and an assessment of its chemical diversity. The best model was found using a 100 nM IC50 threshold with surface-based P_VSA descriptors. This binary QSAR model reached an overall accuracy of 98% and a leave-one-out cross-validated accuracy of 94%. Most relevant descriptors were found to encode size and hydrophobic interactions. These derived models can be useful for screening chemical libraries in the search for new JNK3 inhibitors.

Structure-nongenomic neuroprotection relationship of estrogens and estrogen-derived compounds

Nongenomic estrogen signaling pathways involve extranuclear estrogen receptors or function independently from estrogen receptors. These pathways participate in neuroprotection elicited by the hormone. Additional nongenomic neuroprotective effects are attributable to antioxidant and antiinflammatory actions of estrogens. Numerous chemical modifications to afford neuroprotective compounds from estrogens while eliminating estrogenicity and maintaining or enhancing nongenomic neuroprotection have been described. This review highlights recent structure-activity studies that revealed the importance of antioxidant effects for neuroprotective estrogen analogues and derivatives.

Sequence requirements and an optimization strategy for short antimicrobial peptides

Short antimicrobial host-defense peptides represent a possible alternative as lead structures to fight antibiotic resistant bacterial infections. Bac2A is a 12-mer linear variant of the naturally occurring bovine host defense peptide, bactenecin, and demonstrates moderate, broad-spectrum antimicrobial activity against Gram-positive and Gram-negative bacteria as well as against the yeast Candida albicans. With the assistance of a method involving peptide synthesis on a cellulose support, the primary sequence requirements for antimicrobial activity against the human pathogen Pseudomonas aeruginosa of 277 Bac2A variants were investigated by using a luciferase-based assay. Sequence scrambling of Bac2A led to activities ranging from superior or equivalent to Bac2A to inactive, indicating that good activity was not solely dependent on the composition of amino acids or the overall charge or hydrophobicity, but rather required particular linear sequence patterns. A QSAR computational analysis was applied to analyze the data resulting in a model that supported this sequence pattern hypothesis. The activity of selected peptides was confirmed by conventional minimal inhibitory concentration (MIC) analyses with a panel of human pathogen bacteria and fungi. Circular-dichroism (CD) spectroscopy with selected peptides in liposomes and membrane depolarization assays were consistent with a relationship between structure and activity. An additional optimization process was performed involving systematic amino acid substitutions of one of the optimal scrambled peptide variants, resulting in superior active peptide variants. This process provides a cost and time effective enrichment of new candidates for drug development, increasing the chances of finding pharmacologically relevant peptides.

SAR and QSAR modeling of endocrine disruptors

A number of xenobiotics by mimicking natural hormones can disrupt crucial functions in wildlife and humans. These chemicals termed endocrine disruptors are able to exert adverse effects through a variety of mechanisms. Fortunately, there is a growing interest in the study of these structurally diverse chemicals mainly through research programs based on in vitro and in vivo experimentations but also by means of SAR and QSAR models. The goal of our study was to retrieve from the literature all the papers dealing with structure-activity models on endocrine disruptor xenobiotics. A critical analysis of these models was made focusing our attention on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed.

Three-dimensional models of non-steroidal ligands: a comparative molecular field analysis

The estrogen receptor, ER, is an important biological target whose inhibition is known to be therapeutically relevant in the treatment of postmenopausal osteoporosis. In the present study, two prediction methods (CoMFA and GRIND (Almond)) were used to describe the binding modes of a set of estrogen receptor ligands. The critical alignment step presented in CoMFA was solved by using the information of the molecular descriptors space generated by grid-independent descriptors (GRIND). Then, it was possible to build robust and high predictive models based on the alignment-independent model. Since the structure of estrogen receptor is solved, the results of the present 3D QSAR models, given by the PLS maps based on molecular interaction fields (MIF) were compared to ligand-binding ER domains and showed good agreement.

Analysis of drug-induced effect patterns to link structure and side effects of medicines

The high failure rate of experimental medicines in clinical trials accentuates inefficiencies of current drug discovery processes caused by a lack of tools for translating the information exchange between protein and organ system networks. Recently, we reported that biological activity spectra (biospectra), derived from in vitro protein binding assays, provide a mechanism for assessing a molecule's capacity to modulate the function of protein-network components. Herein we describe the translation of adverse effect data derived from 1,045 prescription drug labels into effect spectra and show their utility for diagnosing drug-induced effects of medicines. In addition, notwithstanding the limitation imposed by the quality of drug label information, we show that biospectrum analysis, in concert with effect spectrum analysis, provides an alignment between preclinical and clinical drug-induced effects. The identification of this alignment provides a mechanism for forecasting clinical effect profiles of medicines.

Structure-based classification of active and inactive estrogenic compounds by decision tree, LVQ and kNN methods

The performance of decision tree (DT), learning vector quantization (LVQ), and k-nearest neighbour (kNN) methods classifying active and inactive estrogenic compounds in terms of their structure activity relationship (SAR) was evaluated. A set of 311 compounds was used for construction of the models, the predictive power of which was verified with separate training and test sets. Principal components derived from molecular descriptors calculated with DRAGON software were used as variables representing the structures of the compounds. Broadly, kNN had the best classification ability and DT the weakest, although the performance of each method was dependent on the group of compounds used for modelling. The best performance was obtained with kNN for the calf estrogen receptor data, averaging 98.3% of correctly classified compounds in the external tests. Overall, the results indicate that all the methods tested are suitable for the SAR classification of estrogenic compounds, producing models with a predictive power ranging from adequate to excellent.

The impact of variable selection on the modelling of oestrogenicity

Many oestrogenic chemicals exert their activity via specific interactions with the oestrogen receptor (ER). The objective of the present study was to identify significant descriptors associated with the ER binding affinities of a large and diverse set of compounds to drive quantitative structure-activity relationships (QSARs). To this end, a variety of statistical methods were employed for variable selection. These included stepwise regression and partial least squares (PLS) analyses, as well as a non-linear recursive partitioning method (Formal Inference-based Recursive Modelling). A total of 157 molecular descriptors including quantum mechanical, graph theoretical, indicator variables and log P were used in the study. Furthermore, cluster analysis of variables was performed to identify groups of descriptors representing similar molecular features. Hierarchical PLS analyses were performed, where the scores of the significant components of either PLS or principle component analysis (PCA), performed separately on each cluster, were used as the variables for the top model. This reduced the number of the variables representing the larger clusters, leading to a similar number of descriptors for each distinct molecular feature. The results showed that the most important molecular properties for stronger ER binding affinity are molecular size and shape, the presence of a phenol moiety as well as other aromatic groups, hydrophobicity and presence of double bonds. The best PLS model obtained, in terms of predictive ability, was a hierarchical PLS model. However, a rigorous validation study showed that the MLR model using descriptors selected by stepwise regression has greater predictive power than the PLS models.

Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions

Support Vector Machines (SVM) is a powerful classification and regression tool that is becoming increasingly popular in various machine learning applications. We tested the ability of SVM, in comparison with well-known neural network techniques, to predict drug-likeness and agrochemical-likeness for large compound collections. For both kinds of data, SVM outperforms various neural networks using the same set of descriptors. We also used SVM for estimating the activity of Carbonic Anhydrase II (CA II) enzyme inhibitors and found that the prediction quality of our SVM model is better than that reported earlier for conventional QSAR. Model characteristics and data set features were studied in detail.

Spline-fitting with a genetic algorithm: a method for developing classification structure-activity relationships

Classification methods allow for the development of structure-activity relationship models when the target property is categorical rather than continuous. We describe a classification method which fits descriptor splines to activities, with descriptors selected using a genetic algorithm. This method, which we identify as SFGA, is compared to the well-established techniques of recursive partitioning (RP) and soft independent modeling by class analogy (SIMCA) using five series of compounds: cyclooxygenase-2 (COX-2) inhibitors, benzodiazepine receptor (BZR) ligands, estrogen receptor (ER) ligands, dihydrofolate reductase (DHFR) inhibitors, and monoamine oxidase (MAO) inhibitors. Only 1-D and 2-D descriptors were used. Approximately 40% of compounds in each series were assigned to a test set, "cherry-picked" from the complete set such that they lie outside the training set as much as possible. SFGA produced models that were more predictive for all but the DHFR set, for which SIMCA was most predictive. RP gave the least predictive models for all but the MAO set. A similar trend was observed when using training and test sets to which compounds were randomly assigned and when gradually eliminating compounds from the (designed) training set. The stability of models was examined for the random and reduced sets, where stability means that classification statistics and the selected descriptors are similar for models derived from different sets. Here, SIMCA produced the most stable models, followed by SFGA and RP. We show that a consensus approach that combines all three methods outperforms the single best model for all data sets.

CoMFA and docking study of novel estrogen receptor subtype selective ligands

We present the results from a Comparative Molecular Field Analysis (CoMFA) and docking study of a diverse set of 36 estrogen receptor ligands whose relative binding affinities (RBA) with respect to 17beta-Estradiol were available in both isoforms of the nuclear estrogen receptors (ER alpha, ER beta). Initial CoMFA models exhibited a correlation between the experimental relative binding affinities and the molecular steric and electrostatic fields; ER alpha: r2 = 0.79, q2 = 0.44 ER beta: r2 = 0.93, q2 = 0.63. Addition of the solvation energy of the isolated ligand improved the predictive nature of the ER beta model initially; r2 = 0.96, q2 = 0.70 but upon rescrambling of the data-set and reselecting the training set at random, inclusion of the ligand solvation energy was found to have little effect on the predictive nature of the CoMFA models. The ligands were then docked inside the ligand binding domain (LBD) of both ER alpha and ER beta utilizing the docking program Gold, after-which the program CScore was used to rank the resulting poses. Inclusion of both the Gold and CScore scoring parameters failed to improve the predictive ability of the original CoMFA models. The subtype selectivity expressed as RBA(ER alpha/ER beta) of the test sets was predicted using the most predictive CoMFA models, as illustrated by the cross-validated r2. In each case the most selective ligands were ranked correctly illustrating the utility of this method as a prescreening tool in the development of novel estrogen receptor subtype selective ligands.

New hypoglycaemic agents selected by molecular topology

New compounds showing hypoglycaemic activity have been designed through a computer aided method based on quantitative structure-activity relationship (QSAR) and molecular connectivity. After calculation of topological indices for a set of 89 compounds including active and inactive with regards to hypoglycaemic action, linear discriminant analysis was performed so that a useful model to predict such an activity was achieved. Later on, the discriminant model was applied on a huge database so that fourteen compounds were selected as potential new hypoglycaemics. From them, just five were finally selected for experimental test on expected hypoglycaemic activity. Among the selected compounds, l-arabitol, Acid blue 161, 1,4-butanediol diglycidil ether and Acid red 151 stand out, which are comparable in potency to standard drugs such as tolbutamide. Acid blue has a glycaemia profile similar to that of tolbutamide but does not lead to a severe hypoglycaemic condition, while the profile of the other agents is near normality.

In silico prediction of drug toxicity

It is essential, in order to minimise expensive drug failures due to toxicity being found in late development or even in clinical trials, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of putative toxicity is advisable even before synthesis. Thus the use of predictive toxicology is called for. A number of in silico approaches to toxicity prediction are discussed. Quantitative structure-activity relationships (QSARs), relating mostly to specific chemical classes, have long been used for this purpose, and exist for a wide range of toxicity endpoints. However, QSARs also exist for the prediction of toxicity of very diverse libraries, although often such QSARs are of the classification type; that is, they predict simply whether or not a compound is toxic, and do not give an indication of the level of toxicity. Examples are given of all of these. A number of expert systems are available for toxicity prediction, most of them covering a range of toxicity endpoints. Those discussed include TOPKAT, CASE, DEREK, HazardExpert, OncoLogic and COMPACT. Comparative tests of the ability of these systems to predict carcinogenicity show that improvement is still needed. The consensus approach is recommended, whereby the results from several prediction systems are pooled.

Virtual screening to enrich hit lists from high-throughput screening: a case study on small-molecule inhibitors of angiogenin

"Hit lists" generated by high-throughput screening (HTS) typically contain a large percentage of false positives, making follow-up assays necessary to distinguish active from inactive substances. Here we present a method for improving the accuracy of HTS hit lists by computationally based virtual screening (VS) of the corresponding chemical libraries and selecting hits by HTS/VS consensus. This approach was applied in a case study on the target-enzyme angiogenin, a potent inducer of angiogenesis. In conjunction with HTS of the National Cancer Institute Diversity Set and ChemBridge DIVERSet E (approximately 18,000 compounds total), VS was performed with two flexible library docking/scoring methods, DockVision/Ludi and GOLD. Analysis of the results reveals that dramatic enrichment of the HTS hit rate can be achieved by selecting compounds in consensus with one or both of the VS functions. For example, HTS hits ranked in the top 2% by GOLD included 42% of the true hits, but only 8% of the false positives; this represents a sixfold enrichment over the HTS hit rate. Notably, the HTS/VS method was effective in selecting out inhibitors with midmicromolar dissociation constants typical of leads commonly obtained in primary screens.