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Chemotypes are a new approach for representing molecules, chemical substructures and patterns, reaction rules, and reactions. Chemotypes are capable of integrating types of information beyond what is possible using current representation methods (e.g., SMARTS patterns) or reaction transformations (e.g., SMIRKS, reaction SMILES). Chemotypes are expressed in the XML-based Chemical Subgraphs and Reactions Markup Language (CSRML), and can be encoded not only with connectivity and topology but also with properties of atoms, bonds, electronic systems, or molecules. CSRML has been developed in parallel with a public set of chemotypes, i.e., the ToxPrint chemotypes, which are designed to provide excellent coverage of environmental, regulatory, and commercial-use chemical space, as well as to represent chemical patterns and properties especially relevant to various toxicity concerns. A software application, ChemoTyper has also been developed and made publicly available in order to enable chemotype searching and fingerprinting against a target structure set. The public ChemoTyper houses the ToxPrint chemotype CSRML dictionary, as well as reference implementation so that the query specifications may be adopted by other chemical structure knowledge systems. The full specifications of the XML-based CSRML standard used to express chemotypes are publicly available to facilitate and encourage the exchange of structural knowledge.

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Background: In many countries, officially approved drug information known as summary of product characteristics (SPC) is mostly available in text form, which cannot be used for Clinical Decision Support Systems (CDSS). It may be essential however to substantiate CDSS advice with such legally binding text snippets. In an attempt to link various drug data sources including SPC towards a CDSS to support medication safety in psychiatric patients we arrived at the notion of an effect object., Methods: A requirements analysis revealed data items and data structure which are needed from the patient and from the drug information source for the CDSS functionality. Published drug data modelling approaches were analyzed and found unsuitable. A conceptional database modeling approach using top down and bottom up modeling was performed., Results: The schema based data model implemented within the django framework centered on SPC "effect objects" which comprise all SPC data required for the respective CDSS function such as search for contraindications in the proposed medication. Today six effect objects have been defined for contraindications and warnings, missing indications, adverse effects, drug-drug interactions, dosing and pharmacokinetics., Conclusion: The transformation of SPC data to a database-driven "effect objects" structure permits decoupling between the CDSS functions and different underlying data sources and supports the design of reusable, stable and verified CDSS functions.

We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 µM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans.

Nature, especially the plant kingdom, is a rich source for novel bioactive compounds that can be used as lead compounds for drug development. In order to exploit this resource, the two neural network-based virtual screening techniques novelty detection with self-organizing maps (SOMs) and counterpropagation neural network were evaluated as tools for efficient lead structure discovery. As application scenario, significant descriptors for acetylcholinesterase (AChE) inhibitors were determined and used for model building, theoretical model validation, and virtual screening. Top-ranked virtual hits from both approaches were docked into the AChE binding site to approve the initial hits. Finally, in vitro testing of selected compounds led to the identification of forsythoside A and (+)-sesamolin as novel AChE inhibitors.

Nowadays, in medicinal chemistry adenosine receptors represent some of the most studied targets, and there is growing interest on the different adenosine receptor (AR) subtypes. The AR subtypes selectivity is highly desired in the development of potent ligands to achieve the therapeutic success. So far, very few ligand-based strategies have been investigated to predict the receptor subtypes selectivity. In the present study, we have carried out a novel application of the multilabel classification approach by combining our recently reported autocorrelated molecular descriptors encoding for the molecular electrostatic potential (autoMEP) with support vector machines (SVMs). Three valuable models, based on decreasing thresholds of potency, have been generated as in series quantitative sieves for the simultaneous prediction of the hA(1)R, hA(2A)R, hA(2B)R, and hA(3)R subtypes potency profile and selectivity of a large collection, more than 500, of known inverse agonists such as xanthine, pyrazolo-triazolo-pyrimidine, and triazolo-pyrimidine analogues. The robustness and reliability of our multilabel classification models were assessed by predicting an internal test set. Finally, we have applied our strategy to 13 newly synthesized pyrazolo-triazolo-pyrimidine derivatives inferring their full adenosine receptor potency spectrum and hAR subtypes selectivity profile.

Each drug can potentially be metabolized by different CYP450 isoforms. In the development of new drugs, the prediction of the metabolic fate is important to prevent drug-drug interactions. In the present study, a collection of 580 CYP450 substrates is deeply analyzed by applying multi- and single-label classification strategies, after the computation and selection of suitable molecular descriptors. Cross-training with support vector machine, multilabel k-nearest-neighbor and counterpropagation neural network modeling methods were used in the multilabel approach, which allows one to classify the compounds simultaneously in multiple classes. In the single-label models, automatic variable selection was combined with various cross-validation experiments and modeling techniques. Moreover, the reliability of both multi- and single-label models was assessed by the prediction of an external test set. Finally, the predicted results of the best models were compared to show that, even if the models present similar performances, the multilabel approach more coherently reflects the real metabolism information.

We developed a structure-property-activity relationship (SPAR)-model for psychopharmacological drugs acting as non-competitive 5-HT(3A) receptor antagonists by using a decision-tree learner provided by the RapidMiner machine learning tool. A single molecular descriptor, namely the molecular dipole moment per molecular weight (mu/MW), predicts whether or not a substance non-competitively antagonizes 5-HT-induced Na(+) currents. A low mu/MW is compatible with drug-cumulation in apolar lipid rafts. This study confirms that size-intensive descriptors allow the development of compact SPAR models.

Sesquiterpene lactones are the active components of a variety of medicinal plants from the Asteraceae family. They possess biological activities such as the inhibition of NF-kappaB and the release inhibition of the vasoactive serotonin. On the basis of a data set of 54 SLs, we report the development of a quantitative model for the prediction of serotonin release inhibition. Comparing this model with a previous investigation of the target NF-kappaB, structural features necessary for specific compounds could be acquired. Atomic properties encoded by radial distribution function and molecular surface potentials encoded by autocorrelation were used as descriptors. Whereas some descriptors describe the structural requirements for both activities, other descriptors can be used to decide whether an SL is more active to NF-kappaB or to serotonin release. Again, counter propagation neural networks proved to be a valuable tool to establish structure-activity relationships that are necessary for the search for and optimization of lead structures.

Some organic weak bases induce a detachment from inner lysosomal membranes and subsequent inactivation of acid sphingomyelinase (ASM) and thus work as functional ASM inhibitors. The aim of the present investigation was to develop a structure-property-activity relation (SPAR) model in order to specify the structural and physicochemical characteristics of probes capable of functionally inhibiting ASM. High p K a and high log P values are necessary but not sufficient preconditions for functional inhibition of ASM. The experimental data supported the requirement of an additional factor, which is necessary for functional inhibition of ASM. This factor k is related to the steric hindrance of the most basic nitrogen atom and presumably modulates the free presentation of a protonated nitrogen atom at the inner lysosomal surface. During the course of the study, we characterized 26 new functional ASM inhibitors, including doxepine 63, fluoxetine 104, maprotiline 109, nortriptyline 114, paroxetine 118, sertraline 124, suloctidil 125, and terfenadine 127.

A data set of 379 drugs and drug analogs that are metabolized by human cytochrome P450 (CYP) isoforms 3A4, 2D6, and 2C9, respectively, was studied. A series of descriptor sets directly calculable from the constitution of these drugs was systematically investigated as to their power into classifying a compound into the CYP isoform that metabolizes it. In a four-step build-up process eventually 303 different descriptor components were investigated for 146 compounds of a training set by various model building methods, such as multinomal logistic regression, decision tree, or support vector machine (SVM). Automatic variable selection algorithms were used in order to decrease the number of descriptors. A comprehensive scheme of cross-validation (CV) experiments was applied to assess the robustness and reliability of the four models developed. In addition, the predictive power of the four models presented in this paper was inspected by predicting an external validation data set with 233 compounds. The best model has a leave-one-out (LOO) cross-validated predictivity of 89% and gives 83% correct predictions for the external validation data set. For our favored model we showed the strong influence on the predictivity of the way a data set is split into a training and test data set.

Self-organizing maps were trained to separate high- and low-active propafenone-type inhibitors of P-glycoprotein. The trained maps were subsequently used to identify highly active compounds in a virtual screen of the SPECS compound library.

A variety of sesquiterpene lactones (SLs) possess considerable anti-inflammatory activity. Several studies have shown that they exert this effect in part by inhibiting the activation of the transcription factor NF-kappaB. In the present study we elaborated on the investigation of a data set of 103 structurally diverse SLs for which we had previously developed several different QSAR equations dependent on the skeletal type. Use of 3D structure descriptors resulted in a single model for the entire data set. In particular, local radial distribution functions (L-RDF) were used that centered on the methylene-carbonyl substructure believed to be the site of attack of cysteine-38 of the p65/NF-kappaB subunit. The model was developed by using a counterpropagation neural network (CPGNN), attesting to the power of this method for establishing structure-activity-relationships. The investigations shed more light onto the influence of the chemical structure on NF-kappaB inhibitory activity.

This work describes an application of artificial neural networks on a small data set of sesquiterpene lactones (STLs) of three tribes of the family Asteraceae. Structurally different types of representative STLs from seven subtribes of the tribes Eupatorieae, Heliantheae and Vernonieae were selected as input data for self-organizing neural networks. Encoding the 3D molecular structures of STLs and their projection onto Kohonen maps allowed the classification of Asteraceae into tribes and subtribes. This approach allowed the evaluation of structural similarities among different sets of 3D structures of sesquiterpene lactones and their correlation with the current taxonomic classification of the family. Predictions of the occurrence of STLs from a plant species according to the taxa they belong to were also performed by the networks. The methodology used in this work can be applied to chemosystematic or chemotaxonomic studies of Asteraceae.