ChemMaps.com v2.0: exploring the environmental chemical universe.

Access to computationally based visualization tools to navigate chemical space has become more important due to the increasing size and diversity of publicly accessible databases, associated compendiums of high-throughput screening (HTS) results, and other descriptor and effects data. However, application of these techniques requires advanced programming skills that are beyond the capabilities of many stakeholders. Here we report the development of the second version of the ChemMaps.com webserver (https://sandbox.ntp.niehs.nih.gov/chemmaps/) focused on environmental chemical space. The chemical space of ChemMaps.com v2.0, released in 2022, now includes approximately one million environmental chemicals from the EPA Distributed Structure-Searchable Toxicity (DSSTox) inventory. ChemMaps.com v2.0 incorporates mapping of HTS assay data from the U.S. federal Tox21 research collaboration program, which includes results from around 2000 assays tested on up to 10 000 chemicals. As a case example, we showcased chemical space navigation for Perfluorooctanoic Acid (PFOA), part of the Per- and polyfluoroalkyl substances (PFAS) chemical family, which are of significant concern for their potential effects on human health and the environment.

Computational application of internationally harmonized defined approaches to skin sensitization: DASS App.

BACKGROUND: Chemically induced skin sensitization, or allergic contact dermatitis, is a common occupational and public health issue. Regulatory authorities require an assessment of potential to cause skin sensitization for many chemical products. Defined approaches for skin sensitization (DASS) identify potential chemical skin sensitizers by integrating data from multiple non-animal tests based on human cells, molecular targets, and computational model predictions using standardized data interpretation procedures. While several DASS are internationally accepted by regulatory agencies, the data interpretation procedures vary in logical complexity, and manual application can be time-consuming or prone to error. RESULTS: We developed the DASS App, an open-source web application, to facilitate user application of three regulatory testing strategies for skin sensitization assessment: the Two-out-of-Three (2o3), the Integrated Testing Strategy (ITS), and the Key Event 3/1 Sequential Testing Strategy (KE 3/1 STS) without the need for software downloads or computational expertise. The application supports upload and analysis of user-provided data, includes steps to identify inconsistencies and formatting issues, and provides predictions in a downloadable format. CONCLUSION: This open-access web-based implementation of internationally harmonized regulatory guidelines for an important public health endpoint is designed to support broad user uptake and consistent, reproducible application. The DASS App is freely accessible via https://ntp.niehs.nih.gov/go/952311 and all scripts are available on GitHub ( https://github.com/NIEHS/DASS ).

Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling.

Imputation machine learning (ML) surpasses traditional approaches in modeling toxicity data. The method was tested on an open-source data set comprising approximately 2500 ingredients with limited in vitro and in vivo data obtained from the OECD QSAR Toolbox. By leveraging the relationships between different toxicological end points, imputation extracts more valuable information from each data point compared to well-established single end point methods, such as ML-based Quantitative Structure Activity Relationship (QSAR) approaches, providing a final improvement of up to around 0.2 in the coefficient of determination. A significant aspect of this methodology is its resilience to the inclusion of extraneous chemical or experimental data. While additional data typically introduces a considerable level of noise and can hinder performance of single end point QSAR modeling, imputation models remain unaffected. This implies a reduction in the need for laborious manual preprocessing tasks such as feature selection, thereby making data preparation for ML analysis more efficient. This successful test, conducted on open-source data, validates the efficacy of imputation approaches in toxicity data analysis. This work opens the way for applying similar methods to other types of sparse toxicological data matrices, and so we discuss the development of regulatory authority guidelines to accept imputation models, a key aspect for the wider adoption of these methods.

Tox21BodyMap: a webtool to map chemical effects on the human body.

To support rapid chemical toxicity assessment and mechanistic hypothesis generation, here we present an intuitive webtool allowing a user to identify target organs in the human body where a substance is estimated to be more likely to produce effects. This tool, called Tox21BodyMap, incorporates results of 9,270 chemicals tested in the United States federal Tox21 research consortium in 971 high-throughput screening (HTS) assays whose targets were mapped onto human organs using organ-specific gene expression data. Via Tox21BodyMap's interactive tools, users can visualize chemical target specificity by organ system, and implement different filtering criteria by changing gene expression thresholds and activity concentration parameters. Dynamic network representations, data tables, and plots with comprehensive activity summaries across all Tox21 HTS assay targets provide an overall picture of chemical bioactivity. Tox21BodyMap webserver is available at https://sandbox.ntp.niehs.nih.gov/bodymap/.

InterPred: a webtool to predict chemical autofluorescence and luminescence interference.

High-throughput screening (HTS) research programs for drug development or chemical hazard assessment are designed to screen thousands of molecules across hundreds of biological targets or pathways. Most HTS platforms use fluorescence and luminescence technologies, representing more than 70% of the assays in the US Tox21 research consortium. These technologies are subject to interferent signals largely explained by chemicals interacting with light spectrum. This phenomenon results in up to 5-10% of false positive results, depending on the chemical library used. Here, we present the InterPred webserver (version 1.0), a platform to predict such interference chemicals based on the first large-scale chemical screening effort to directly characterize chemical-assay interference, using assays in the Tox21 portfolio specifically designed to measure autofluorescence and luciferase inhibition. InterPred combines 17 quantitative structure activity relationship (QSAR) models built using optimized machine learning techniques and allows users to predict the probability that a new chemical will interfere with different combinations of cellular and technology conditions. InterPred models have been applied to the entire Distributed Structure-Searchable Toxicity (DSSTox) Database (∼800,000 chemicals). The InterPred webserver is available at https://sandbox.ntp.niehs.nih.gov/interferences/.

Benchmarking 2D/3D/MD-QSAR Models for Imatinib Derivatives: How Far Can We Predict?

Imatinib, a 2-phenylaminopyridine-based BCR-ABL tyrosine kinase inhibitor, is a highly effective drug for treating Chronic Myeloid Leukemia (CML). However, cases of drug resistance are constantly emerging due to various mutations in the ABL kinase domain; thus, it is crucial to identify novel bioactive analogues. Reliable QSAR models and molecular docking protocols have been shown to facilitate the discovery of new compounds from chemical libraries prior to experimental testing. However, as the vast majority of QSAR models strictly relies on 2D descriptors, the rise of 3D descriptors directly computed from molecular dynamics simulations offers new opportunities to potentially augment the reliability of QSAR models. Herein, we employed molecular docking and molecular dynamics on a large series of Imatinib derivatives and developed an ensemble of QSAR models relying on deep neural nets (DNN) and hybrid sets of 2D/3D/MD descriptors in order to predict the binding affinity and inhibition potencies of those compounds. Through rigorous validation tests, we showed that our DNN regression models achieved excellent external prediction performances for the pKi data set (n = 555, R2 ≥ 0.71. and MAE ≤ 0.85), and the pIC50 data set (n = 306, R2 ≥ 0.54. and MAE ≤ 0.71) with strict validation protocols based on external test sets and 10-fold native and nested cross validations. Interestingly, the best DNN and random forest models performed similarly across all descriptor sets. In fact, for this particular series of compounds, our external test results suggest that incorporating additional 3D protein-ligand binding site fingerprint, descriptors, or even MD time-series descriptors did not significantly improve the overall R2 but lowered the MAE of DNN QSAR models. Those augmented models could still help in identifying and understanding the key dynamic protein-ligand interactions to be optimized for further molecular design.

Exploring drug space with ChemMaps.com.

Motivation: Easily navigating chemical space has become more important due to the increasing size and diversity of publicly-accessible databases such as DrugBank, ChEMBL or Tox21. To do so, modelers typically rely on complex projection techniques using molecular descriptors computed for all the chemicals to be visualized. However, the multiple cheminformatics steps required to prepare, characterize, compute and explore those molecules, are technical, typically necessitate scripting skills, and thus represent a real obstacle for non-specialists. Results: We developed the ChemMaps.com webserver to easily browse, navigate and mine chemical space. The first version of ChemMaps.com features more than 8000 approved, in development, and rejected drugs, as well as over 47 000 environmental chemicals. Availability and implementation: The webserver is freely available at http://www.chemmaps.com.

RealityConvert: a tool for preparing 3D models of biochemical structures for augmented and virtual reality.

MOTIVATION: There is a growing interest for the broad use of Augmented Reality (AR) and Virtual Reality (VR) in the fields of bioinformatics and cheminformatics to visualize complex biological and chemical structures. AR and VR technologies allow for stunning and immersive experiences, offering untapped opportunities for both research and education purposes. However, preparing 3D models ready to use for AR and VR is time-consuming and requires a technical expertise that severely limits the development of new contents of potential interest for structural biologists, medicinal chemists, molecular modellers and teachers. RESULTS: Herein we present the RealityConvert software tool and associated website, which allow users to easily convert molecular objects to high quality 3D models directly compatible for AR and VR applications. For chemical structures, in addition to the 3D model generation, RealityConvert also generates image trackers, useful to universally call and anchor that particular 3D model when used in AR applications. The ultimate goal of RealityConvert is to facilitate and boost the development and accessibility of AR and VR contents for bioinformatics and cheminformatics applications. AVAILABILITY AND IMPLEMENTATION: http://www.realityconvert.com. CONTACT: dfourch@ncsu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Structural Isosteres of Phosphate Groups in the Protein Data Bank.

We developed a computational workflow to mine the Protein Data Bank for isosteric replacements that exist in different binding site environments but have not necessarily been identified and exploited in compound design. Taking phosphate groups as examples, the workflow was used to construct 157 data sets, each composed of a reference protein complexed with AMP, ADP, ATP, or pyrophosphate as well other ligands. Phosphate binding sites appear to have a high hydration content and large size, resulting in U-shaped bioactive conformations recurrently found across unrelated protein families. A total of 16 413 replacements were extracted, filtered for a significant structural overlap on phosphate groups, and sorted according to their SMILES codes. In addition to the classical isosteres of phosphate, such as carboxylate, sulfone, or sulfonamide, unexpected replacements that do not conserve charge or polarity, such as aryl, aliphatic, or positively charged groups, were found.

PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins.

Predicting protein pocket's ability to bind drug-like molecules with high affinity, i.e. druggability, is of major interest in the target identification phase of drug discovery. Therefore, pocket druggability investigations represent a key step of compound clinical progression projects. Currently computational druggability prediction models are attached to one unique pocket estimation method despite pocket estimation uncertainties. In this paper, we propose 'PockDrug-Server' to predict pocket druggability, efficient on both (i) estimated pockets guided by the ligand proximity (extracted by proximity to a ligand from a holo protein structure) and (ii) estimated pockets based solely on protein structure information (based on amino atoms that form the surface of potential binding cavities). PockDrug-Server provides consistent druggability results using different pocket estimation methods. It is robust with respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using different estimation methods and outperformed recent druggability models for apo pockets. It can be carried out from one or a set of apo/holo proteins using different pocket estimation methods proposed by our web server or from any pocket previously estimated by the user. PockDrug-Server is publicly available at: http://pockdrug.rpbs.univ-paris-diderot.fr.

Highlights from the Third International Society for Computational Biology (ISCB) European Student Council Symposium 2014.

In this meeting report, we give an overview of the talks, presentations and posters presented at the third European Symposium of the International Society for Computational Biology (ISCB) Student Council. The event was organized as a satellite meeting of the 13th European Conference for Computational Biology (ECCB) and took place in Strasbourg, France on September 6th, 2014.

PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties.

Predicting protein druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods' influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 Å to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5-10%) than previous studies that used an identical apo set. In conclusion, this study confirms the influence of pocket estimation on pocket druggability prediction and proposes PockDrug as a new model that overcomes pocket estimation variability.

GOHTAM: a website for 'Genomic Origin of Horizontal Transfers, Alignment and Metagenomics'.

MOTIVATION: This website allows the detection of horizontal transfers based on a combination of parametric methods and proposes an origin by researching neighbors in a bank of genomic signatures. This bank is also used to research an origin to DNA fragments from metagenomics studies. RESULTS: Different services are provided like the possibility of inferring a phylogenetic tree with sequence signatures or comparing two genomes and displaying the rearrangements that happened since their separation. AVAILABILITY AND IMPLEMENTATION: http://gohtam.rpbs.univ-paris-diderot.fr/

Cheminformatics analysis of chemicals that increase estrogen and progesterone synthesis for a breast cancer hazard assessment.

Factors that increase estrogen or progesterone (P4) action are well-established as increasing breast cancer risk, and many first-line treatments to prevent breast cancer recurrence work by blocking estrogen synthesis or action. In previous work, using data from an in vitro steroidogenesis assay developed for the U.S. Environmental Protection Agency (EPA) ToxCast program, we identified 182 chemicals that increased estradiol (E2up) and 185 that increased progesterone (P4up) in human H295R adrenocortical carcinoma cells, an OECD validated assay for steroidogenesis. Chemicals known to induce mammary effects in vivo were very likely to increase E2 or P4 synthesis, further supporting the importance of these pathways for breast cancer. To identify additional chemical exposures that may increase breast cancer risk through E2 or P4 steroidogenesis, we developed a cheminformatics approach to identify structural features associated with these activities and to predict other E2 or P4 steroidogens from their chemical structures. First, we used molecular descriptors and physicochemical properties to cluster the 2,012 chemicals screened in the steroidogenesis assay using a self-organizing map (SOM). Structural features such as triazine, phenol, or more broadly benzene ramified with halide, amine or alcohol, are enriched for E2 or P4up chemicals. Among E2up chemicals, phenol and benzenone are found as significant substructures, along with nitrogen-containing biphenyls. For P4up chemicals, phenol and complex aromatic systems ramified with oxygen-based groups such as flavone or phenolphthalein are significant substructures. Chemicals that are active for both E2up and P4up are enriched with substructures such as dihydroxy phosphanedithione or are small chemicals that contain one benzene ramified with chlorine, alcohol, methyl or primary amine. These results are confirmed with a chemotype ToxPrint analysis. Then, we used machine learning and artificial intelligence algorithms to develop and validate predictive classification QSAR models for E2up and P4up chemicals. These models gave reasonable external prediction performances (balanced accuracy ~ 0.8 and Matthews Coefficient Correlation ~ 0.5) on an external validation. The QSAR models were enriched by adding a confidence score that considers the chemical applicability domain and a ToxPrint assessment of the chemical. This profiling and these models may be useful to direct future testing and risk assessments for chemicals related to breast cancer and other hormonally-mediated outcomes.

High-Throughput Chemical Screening and Structure-Based Models to Predict hERG Inhibition.

Chemical inhibition of the human ether-a -go-go-related gene (hERG) potassium channel leads to a prolonged QT interval that can contribute to severe cardiotoxicity. The adverse effects of hERG inhibition are one of the principal causes of drug attrition in clinical and pre-clinical development. Preliminary studies have demonstrated that a wide range of environmental chemicals and toxicants may also inhibit the hERG channel and contribute to the pathophysiology of cardiovascular (CV) diseases. As part of the US federal Tox21 program, the National Center for Advancing Translational Science (NCATS) applied a quantitative high throughput screening (qHTS) approach to screen the Tox21 library of 10,000 compounds (~7871 unique chemicals) at 14 concentrations in triplicate to identify chemicals perturbing hERG activity in the U2OS cell line thallium flux assay platform. The qHTS cell-based thallium influx assay provided a robust and reliable dataset to evaluate the ability of thousands of drugs and environmental chemicals to inhibit hERG channel protein, and the use of chemical structure-based clustering and chemotype enrichment analysis facilitated the identification of molecular features that are likely responsible for the observed hERG activity. We employed several machine-learning approaches to develop QSAR prediction models for the assessment of hERG liabilities for drug-like and environmental chemicals. The training set was compiled by integrating hERG bioactivity data from the ChEMBL database with the Tox21 qHTS thallium flux assay data. The best results were obtained with the random forest method (~92.6% balanced accuracy). The data and scripts used to generate hERG prediction models are provided in an open-access format as key in vitro and in silico tools that can be applied in a translational toxicology pipeline for drug development and environmental chemical screening.

Cheminformatics Analysis of Fluoroquinolones and their Inhibition Potency Against Four Pathogens.

Drug-resistant bacteria are a worldwide public health concern. As the prevalence of multi-drug resistant pathogens outpaces the discovery of new antibacterials, it is of importance to explore the structure-activity relationships for series of known bactericides with proven scaffolds. Herein, we assembled a set of 507 fluoroquinolone analogues all experimentally tested for their inhibition potency against four pathogens: Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Streptococcus pneumoniae. We relied on cheminformatics techniques to characterize and cluster them based on their structural similarity and analyzed the structure-activity relationships identified for each cluster of fluoroquinolones. Then, we utilized machine learning techniques to develop and validate predictive QSAR models for computing the inhibition potencies (pMIC) of analogues for each pathogen. These QSAR models afforded reasonable external prediction performances (R2≥0.6, MAE∼0.4). This study confirmed that (i) there are both global and local inter-pathogen concordance regarding the antibacterial potency of fluoroquinolones, (ii) small clusters of fluoroquinolone analogues are characterized by unique patterns of strain selectivity and potency, the latter being potentially useful to design new analogues with enhanced potency and/or selectivity towards a given pathogen, and (iii) robust QSAR models were obtained allowing for future design of new bioactive fluoroquinolones.

Yale School of Public Health Symposium: An overview of the challenges and opportunities associated with per- and polyfluoroalkyl substances (PFAS).

On December 13, 2019, the Yale School of Public Health hosted a symposium titled "Per- and Polyfluoroalkyl Substances (PFAS): Challenges and Opportunities" in New Haven, Connecticut. The meeting focused on the current state of the science on these chemicals, highlighted the challenges unique to PFAS, and explored promising opportunities for addressing them. It brought together participants from Yale University, the National Institute of Environmental Health Sciences, the University of Massachusetts Amherst, the University of Connecticut, the Connecticut Agricultural Experiment Station, the Connecticut Departments of Public Health and Energy and Environmental Protection, and the public and private sectors. Presentations during the symposium centered around several primary themes. The first reviewed the current state of the science on the health effects associated with PFAS exposure and noted key areas that warranted future research. As research in this field relies on specialized laboratory analyses, the second theme considered commercially available methods for PFAS analysis as well as several emerging analytical approaches that support health studies and facilitate the investigation of a broader range of PFAS. Since mitigation of PFAS exposure requires prevention and cleanup of contamination, the third theme highlighted new nanotechnology-enabled PFAS remediation technologies and explored the potential of green chemistry to develop safer alternatives to PFAS. The fourth theme covered collaborative efforts to assess the vulnerability of in-state private wells and small public water supplies to PFAS contamination by adjacent landfills, and the fifth focused on strategies that promote successful community engagement. This symposium supported a unique interdisciplinary coalition established during the development of Connecticut's PFAS Action Plan, and discussions occurring throughout the symposium revealed opportunities for collaborations among Connecticut scientists, state and local officials, and community advocates. In doing so, it bolstered the State of Connecticut's efforts to implement the ambitious initiatives that its PFAS Action Plan recommends.

High-Throughput Screening to Predict Chemical-Assay Interference.

The U.S. federal consortium on toxicology in the 21st century (Tox21) produces quantitative, high-throughput screening (HTS) data on thousands of chemicals across a wide range of assays covering critical biological targets and cellular pathways. Many of these assays, and those used in other in vitro screening programs, rely on luciferase and fluorescence-based readouts that can be susceptible to signal interference by certain chemical structures resulting in false positive outcomes. Included in the Tox21 portfolio are assays specifically designed to measure interference in the form of luciferase inhibition and autofluorescence via multiple wavelengths (red, blue, and green) and under various conditions (cell-free and cell-based, two cell types). Out of 8,305 chemicals tested in the Tox21 interference assays, percent actives ranged from 0.5% (red autofluorescence) to 9.9% (luciferase inhibition). Self-organizing maps and hierarchical clustering were used to relate chemical structural clusters to interference activity profiles. Multiple machine learning algorithms were applied to predict assay interference based on molecular descriptors and chemical properties. The best performing predictive models (accuracies of ~80%) have been included in a web-based tool called InterPred that will allow users to predict the likelihood of assay interference for any new chemical structure and thus increase confidence in HTS data by decreasing false positive testing results.

The Key Characteristics of Carcinogens: Relationship to the Hallmarks of Cancer, Relevant Biomarkers, and Assays to Measure Them.

The key characteristics (KC) of human carcinogens provide a uniform approach to evaluating mechanistic evidence in cancer hazard identification. Refinements to the approach were requested by organizations and individuals applying the KCs. We assembled an expert committee with knowledge of carcinogenesis and experience in applying the KCs in cancer hazard identification. We leveraged this expertise and examined the literature to more clearly describe each KC, identify current and emerging assays and in vivo biomarkers that can be used to measure them, and make recommendations for future assay development. We found that the KCs are clearly distinct from the Hallmarks of Cancer, that interrelationships among the KCs can be leveraged to strengthen the KC approach (and an understanding of environmental carcinogenesis), and that the KC approach is applicable to the systematic evaluation of a broad range of potential cancer hazards in vivo and in vitro We identified gaps in coverage of the KCs by current assays. Future efforts should expand the breadth, specificity, and sensitivity of validated assays and biomarkers that can measure the 10 KCs. Refinement of the KC approach will enhance and accelerate carcinogen identification, a first step in cancer prevention.See all articles in this CEBP Focus section, "Environmental Carcinogenesis: Pathways to Prevention."

Characterization of Ionizable Groups' Environments in Proteins and Protein-Ligand Complexes through a Statistical Analysis of the Protein Data Bank.

We conduct a statistical analysis of the molecular environment of common ionizable functional groups in both protein-ligand complexes and inside proteins from the Protein Data Bank (PDB). In particular, we characterize the frequency, type, and density of the interacting atoms as well as the presence of a potential counterion. We found that for ligands, most guanidinium groups, half of primary and secondary amines, and one-fourth of imidazole neighbor a carboxylate group. Tertiary amines bind more rarely near carboxylate groups, which may be explained by a crowded neighborhood and hydrophobic character. In comparison to the environment seen by the ligands, inside proteins, an environment enriched in main-chain atoms is found, and the prevalence of direct charge neutralization by carboxylate groups is different. When the ionizable character of water molecules and phenolic or hydroxyl groups is accounted, considering a high-resolution dataset (less than 1.5 Å), charge neutralization could occur for well above 80% of the ligand functional groups considered, but for tertiary amines.