Measuring CAMD Technique Performance. 2. How “Druglike” Are Drugs? Implications of Random Test Set Selection Exemplified Using Druglikeness Classification Models

Challenging Reverse Screening: A Benchmark Study for Comprehensive Evaluation

As an efficient way of computational target prediction, reverse docking can find not only potential targets but also binding modes for a query ligand. Though the number of available docking tools keeps expanding, there is still not a comprehensive evaluation study which can uncover the advantages and limitations of these strategies in the research field of computational target-fishing. In this study, we propose a brand-new evaluation dataset tailor-made for reverse docking, which is composed of a true positive set (the core set) and two negative sets (the similar decoy set and the dissimilar decoy set). The proposed evaluation dataset can assess the prediction performance of docking tools as various values affected by varying degrees of inter-target ranking bias. The performance of four classical docking programs (AutoDock, AutoDock Vina, Glide and GOLD) was evaluated utilizing our dataset, and a biased prediction performance was observed regarding binding site properties. The results demonstrated that Glide (SP) and Glide(XP) had the best capacity to find true targets whether there was inter-target ranking bias or not.

A random forest model for predicting crystal packing of olanzapine solvates

A random forest (RF) classification model obtained from physicochemical properties of solvents and crystal structures of olanzapine has for the first time enabled the prediction of 3-D crystal packings of solvates. A novel solvate was obtained by targeted crystallization from the solvent identified by RF model.

How Open Data Shapes In Silico Transporter Modeling

Chemical compound bioactivity and related data are nowadays easily available from open data sources and the open medicinal chemistry literature for many transmembrane proteins. Computational ligand-based modeling of transporters has therefore experienced a shift from local (quantitative) models to more global, qualitative, predictive models. As the size and heterogeneity of the data set rises, careful data curation becomes even more important. This includes, for example, not only a tailored cutoff setting for the generation of binary classes, but also the proper assessment of the applicability domain. Powerful machine learning algorithms (such as multi-label classification) now allow the simultaneous prediction of multiple related targets. However, the more complex, the less interpretable these models will get. We emphasize that transmembrane transporters are very peculiar, some of which act as off-targets rather than as real drug targets. Thus, careful selection of the right modeling technique is important, as well as cautious interpretation of results. We hope that, as more and more data will become available, we will be able to ameliorate and specify our models, coming closer towards function elucidation and the development of safer medicine.

Computational Study Exploring the Interaction Mechanism of Benzimidazole Derivatives as Potent Cattle Bovine Viral Diarrhea Virus Inhibitors

Bovine viral diarrhea virus (BVDV) infections are prevailing in cattle populations on a worldwide scale. The BVDV RNA-dependent RNA polymerase (RdRp), as a promising target for new anti-BVDV drug development, has attracted increasing attention. To explore the interaction mechanism of 65 benzimidazole scaffold-based derivatives as BVDV inhibitors, presently, a computational study was performed based on a combination of 3D-QSAR, molecular docking, and molecular dynamics (MD) simulations. The resultant optimum CoMFA and CoMSIA models present proper reliabilities and strong predictive abilities (with Q(2) = 0. 64, R(2)ncv = 0.93, R(2)pred = 0.80 and Q(2) = 0. 65, R(2)ncv = 0.98, R(2)pred = 0.86, respectively). In addition, there was good concordance between these models, molecular docking, and MD results. Moreover, the MM-PBSA energy analysis reveals that the major driving force for ligand binding is the polar solvation contribution term. Hopefully, these models and the obtained findings could offer better understanding of the interaction mechanism of BVDV inhibitors as well as benefit the new discovery of more potent BVDV inhibitors.

The application of in silico drug-likeness predictions in pharmaceutical research

The concept of drug-likeness, established from the analyses of the physiochemical properties or/and structural features of existing small organic drugs or/and drug candidates, has been widely used to filter out compounds with undesirable properties, especially poor ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles. Here, we summarize various approaches for drug-likeness evaluations, including simple rules/filters based on molecular properties/structures and quantitative prediction models based on sophisticated machine learning methods, and provide a comprehensive review of recent advances in this field. Moreover, the strengths and weaknesses of these approaches are briefly outlined. Finally, the drug-likeness analyses of natural products and traditional Chinese medicines (TCM) are discussed.

Profiling the interaction mechanism of indole-based derivatives targeting the HIV-1 gp120 receptor

A glycoprotein exposed on a viral surface, human immunodeficiency virus type 1 (HIV-1) gp120 is essential for virus entry into cells as it plays a vital role in seeking out specific cell surface receptors for entry.

Extended-connectivity fingerprints

Extended-connectivity fingerprints (ECFPs) are a novel class of topological fingerprints for molecular characterization. Historically, topological fingerprints were developed for substructure and similarity searching. ECFPs were developed specifically for structure-activity modeling. ECFPs are circular fingerprints with a number of useful qualities: they can be very rapidly calculated; they are not predefined and can represent an essentially infinite number of different molecular features (including stereochemical information); their features represent the presence of particular substructures, allowing easier interpretation of analysis results; and the ECFP algorithm can be tailored to generate different types of circular fingerprints, optimized for different uses. While the use of ECFPs has been widely adopted and validated, a description of their implementation has not previously been presented in the literature.

Quantifying biogenic bias in screening libraries

In lead discovery, libraries of 10⁶ molecules are screened for biological activity. Given the over 10⁶⁰ drug-like molecules thought possible, such screens might never succeed. That they do, even occasionally, implies a biased selection of library molecules. Here a method is developed to quantify the bias in screening libraries towards biogenic molecules. With this approach, we consider what is missing from screening libraries and how they can be optimized.

Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data

Refined nearest neighbor analysis was recently introduced for the analysis of virtual screening benchmark data sets. It constitutes a technique from the field of spatial statistics and provides a mathematical framework for the nonparametric analysis of mapped point patterns. Here, refined nearest neighbor analysis is used to design benchmark data sets for virtual screening based on PubChem bioactivity data. A workflow is devised that purges data sets of compounds active against pharmaceutically relevant targets from unselective hits. Topological optimization using experimental design strategies monitored by refined nearest neighbor analysis functions is applied to generate corresponding data sets of actives and decoys that are unbiased with regard to analogue bias and artificial enrichment. These data sets provide a tool for Maximum Unbiased Validation (MUV) of virtual screening methods. The data sets and a software package implementing the MUV design workflow are freely available at http://www.pharmchem.tu-bs.de/lehre/baumann/MUV.html.

A constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problem

Background

The inverse-QSAR problem seeks to find a new molecular descriptor from which one can recover the structure of a molecule that possess a desired activity or property. Surprisingly, there are very few papers providing solutions to this problem. It is a difficult problem because the molecular descriptors involved with the inverse-QSAR algorithm must adequately address the forward QSAR problem for a given biological activity if the subsequent recovery phase is to be meaningful. In addition, one should be able to construct a feasible molecule from such a descriptor. The difficulty of recovering the molecule from its descriptor is the major limitation of most inverse-QSAR methods.

Results

In this paper, we describe the reversibility of our previously reported descriptor, the vector space model molecular descriptor (VSMMD) based on a vector space model that is suitable for kernel studies in QSAR modeling. Our inverse-QSAR approach can be described using five steps: (1) generate the VSMMD for the compounds in the training set; (2) map the VSMMD in the input space to the kernel feature space using an appropriate kernel function; (3) design or generate a new point in the kernel feature space using a kernel feature space algorithm; (4) map the feature space point back to the input space of descriptors using a pre-image approximation algorithm; (5) build the molecular structure template using our VSMMD molecule recovery algorithm.

Conclusion

The empirical results reported in this paper show that our strategy of using kernel methodology for an inverse-Quantitative Structure-Activity Relationship is sufficiently powerful to find a meaningful solution for practical problems.

Electronic supplementary material

The online version of this article (doi:10.1186/1758-2946-1-4) contains supplementary material, which is available to authorized users.

Assessing drug-likeness--what are we missing?

The concept of drug-likeness helps to optimise pharmacokinetic and pharmaceutical properties, for example, solubility, chemical stability, bioavailability and distribution profile. A number of molecular descriptors have emerged as reasonably informative and predictive, for example, the Rule-of-Five. Here, we review some current approaches, then discuss their major shortcoming, namely the static nature of the structural features and physicochemical properties they encode. As we demonstrate, molecules are not 'frozen statues' but 'dancing ballerinas', and several of their computable physicochemical properties are conformation-dependent and lead to the concept of property spaces. Molecular sensitivity (namely, how much a given computable physicochemical property varies as a function of flexibility) appears as a promising descriptor to encode some of the information contained in molecular property spaces.