The blockage of the hERG K+ channels is closely associated with lethal cardiac arrhythmia. The notorious ligand promiscuity of this channel earmarked hERG as one of the most important antitargets to be considered in early stages of drug development process. Herein we report on the development of an innovative and freely accessible web server for early identification of putative hERG blockers and non-blockers in chemical libraries. We have collected the largest publicly available curated hERG dataset of 5,984 compounds. We succeed in developing robust and externally predictive binary (CCR ≈0.8) and multiclass models (accuracy ≈0.7). These models are available as a web-service freely available for public at http://labmol.farma-cia.ufg.br/predherg/. Three following outcomes are available for the users: prediction by binary model, prediction by multi-class model, and the probability maps of atomic contribution. The Pred-hERG will be continuously updated and upgraded as new information became available.

Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as “black boxes” they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.

Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDA-required procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure–activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83–0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg).

Chemically induced skin sensitization is a complex immunological disease with a profound impact on quality of life and working ability. Despite some progress in developing alternative methods for assessing the skin sensitization potential of chemical substances, there is no in vitro test that correlates well with human data. Computational QSAR models provide a rapid screening approach and contribute valuable information for the assessment of chemical toxicity. We describe the development of a freely accessible web-based and mobile application for the identification of potential skin sensitizers. The application is based on previously developed binary QSAR models of skin sensitization potential from human (109 compounds) and murine local lymph node assay (LLNA, 515 compounds) data with good external correct classification rate (0.70-0.81 and 0.72-0.84, respectively). We also included a multiclass skin sensitization potency model based on LLNA data (accuracy ranging between 0.73 and 0.76). When a user evaluates a compound in the web app, the outputs are (i) binary predictions of human and murine skin sensitization potential; (ii) multiclass prediction of murine skin sensitization; and (iii) probability maps illustrating the predicted contribution of chemical fragments. The app is the first tool available that incorporates quantitative structure-activity relationship (QSAR) models based on human data as well as multiclass models for LLNA. The Pred-Skin web app version 1.0 is freely available for the web, iOS, and Android (in development) at the LabMol web portal ( http://labmol.com.br/predskin/ ), in the Apple Store, and on Google Play, respectively. We will continuously update the app as new skin sensitization data and respective models become available.

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Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential.

Pharmacophore approaches have evolved to be one of the most successful tools in drug discovery, especially since the past two decades. 3D pharmacophore methods are now commonly used as part of more complex workflows in drug discovery campaigns, and have been successfully and extensively applied in virtual screening (VS) approaches. This review provides a perspective of how to assess the performance of 3D pharmacophore models to be used in VS. Since 3D VS 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 modeling and external sets on the outcome of evaluations. Moreover, we highlight the significance of both classic enrichment parameters and advanced descriptors for the performance of 3D pharmacophore-based virtual screening methods.

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