High Throughput Read-Across for Screening a Large Inventory of Related Structures by Balancing Artificial Intelligence/Machine Learning and Human Knowledge.

Read-across is an in silico method applied in chemical risk assessment for data-poor chemicals. The read-across outcomes for repeated-dose toxicity end points include the no-observed-adverse-effect level (NOAEL) and estimated uncertainty for a particular category of effects. We have previously developed a new paradigm for estimating NOAELs based on chemoinformatics analysis and experimental study qualities from selected analogues, not relying on quantitative structure-activity relationships (QSARs) or rule-based SAR systems, which are not well-suited to end points for which the underpinning data are weakly grounded in specific chemical-biological interactions. The central hypothesis of this approach is that similar compounds have similar toxicity profiles and, hence, similar NOAEL values. Analogue quality (AQ) quantifies the suitability of an analogue candidate for reading across to the target by considering similarity from structure, physicochemical, ADME (absorption, distribution, metabolism, excretion), and biological perspectives. Biological similarity is based on experimental data; assay vectors derived from aggregations of ToxCast/Tox21 data are used to derive machine learning (ML) hybrid rules that serve as biological fingerprints to capture target-analogue similarity relevant to specific effects of interest, for example, hormone receptors (ER/AR/THR). Once one or more analogues have been qualified for read-across, a decision theory approach is used to estimate confidence bounds for the NOAEL of the target. The confidence interval is dramatically narrowed when analogues are constrained to biologically related profiles. Although this read-across process works well for a single target with several analogues, it can become unmanageable when, for example, screening multiple targets (e.g., virtual screening library) or handling a parent compound having numerous metabolites. To this end, we have established a digitalized framework to enable the assessment of a large number of substances, while still allowing for human decisions for filtering and prioritization. This workflow was developed and validated through a use case of a large set of bisphenols and their metabolites.

A New CSRML Structure-Based Fingerprint Method for Profiling and Categorizing Per- and Polyfluoroalkyl Substances (PFAS).

The term PFAS encompasses diverse per- and polyfluorinated alkyl (and increasingly aromatic) chemicals spanning industrial processes, commercial uses, environmental occurrence, and potential concerns. With increased chemical curation, currently exceeding 14,000 structures in the PFASSTRUCTV5 inventory on EPA's CompTox Chemicals Dashboard, has come increased motivation to profile, categorize, and analyze the PFAS structure space using modern cheminformatics approaches. Making use of the publicly available ToxPrint chemotypes and ChemoTyper application, we have developed a new PFAS-specific fingerprint set consisting of 129 TxP_PFAS chemotypes coded in CSRML, a chemical-based XML-query language. These are split into two groups, the first containing 56 mostly bond-type ToxPrints modified to incorporate attachment to either a CF group or F atom to enforce proximity to the fluorinated portion of the chemical. This focus resulted in a dramatic reduction in TxP_PFAS chemotype counts relative to the corresponding ToxPrint counts (averaging 54%). The remaining TxP_PFAS chemotypes consist of various lengths and types of fluorinated chains, rings, and bonding patterns covering indications of branching, alternate halogenation, and fluorotelomers. Both groups of chemotypes are well represented across the PFASSTRUCT inventory. Using the ChemoTyper application, we show how the TxP_PFAS chemotypes can be visualized, filtered, and used to profile the PFASSTRUCT inventory, as well as to construct chemically intuitive, structure-based PFAS categories. Lastly, we used a selection of expert-based PFAS categories from the OECD Global PFAS list to evaluate a small set of analogous structure-based TxP_PFAS categories. TxP_PFAS chemotypes were able to recapitulate the expert-based PFAS category concepts based on clearly defined structure rules that can be computationally implemented and reproducibly applied to process large PFAS inventories without need to consult an expert. The TxP_PFAS chemotypes have the potential to support computational modeling, harmonize PFAS structure-based categories, facilitate communication, and allow for more efficient and chemically informed exploration of PFAS chemicals moving forward.

Geometry-Based versus Small-Molecule Tracking Method for Tunnel Identification: Benefits and Pitfalls.

Different methods for tunnel identification, geometry-based and small-molecule tracking approaches, were compared to provide their benefits and pitfalls. Results obtained for both crystal structures and molecular dynamics (MD) simulations were analyzed to investigate if a more computationally demanding method would be beneficial. Careful examination of the results is essential for the low-diameter tunnel description, and assessment of the tunnel functionality based only on their geometrical parameters is challenging. We showed that the small-molecule tracking approach can provide a detailed description of the system; however, it can also be the most computationally demanding.

Evolution of tunnels in α/ß-hydrolase fold proteins-What can we learn from studying epoxide hydrolases?

The evolutionary variability of a protein's residues is highly dependent on protein region and function. Solvent-exposed residues, excluding those at interaction interfaces, are more variable than buried residues whereas active site residues are considered to be conserved. The abovementioned rules apply also to α/ß-hydrolase fold proteins-one of the oldest and the biggest superfamily of enzymes with buried active sites equipped with tunnels linking the reaction site with the exterior. We selected soluble epoxide hydrolases as representative of this family to conduct the first systematic study on the evolution of tunnels. We hypothesised that tunnels are lined by mostly conserved residues, and are equipped with a number of specific variable residues that are able to respond to evolutionary pressure. The hypothesis was confirmed, and we suggested a general and detailed way of the tunnels' evolution analysis based on entropy values calculated for tunnels' residues. We also found three different cases of entropy distribution among tunnel-lining residues. These observations can be applied for protein reengineering mimicking the natural evolution process. We propose a 'perforation' mechanism for new tunnels design via the merging of internal cavities or protein surface perforation. Based on the literature data, such a strategy of new tunnel design could significantly improve the enzyme's performance and can be applied widely for enzymes with buried active sites.

Do Similar Structures Have Similar No Observed Adverse Effect Level (NOAEL) Values? Exploring Chemoinformatics Approaches for Estimating NOAEL Bounds and Uncertainties.

Determination of the no observed adverse effect level (NOAEL) of a substance is an important step in safety and regulatory assessments. Application of conventional in silico strategies, for example, quantitative structure-activity relationship (QSAR) models, to predict NOAEL values is inherently problematic. Whereas QSAR models for well-defined toxicity endpoints such as Ames mutagenicity or skin sensitization can be developed from mechanistic knowledge of molecular initiating events and adverse outcome pathways, QSAR is not appropriate for predicting a NOAEL value, a concentration at which "no effect" is observed. This paper presents a chemoinformatics approach and explores how it can be further refined through the incorporation of toxicity endpoint-specific information to estimate confidence bounds for the NOAEL of a target substance, given experimentally determined NOAEL values for one or more suitable analogues. With a sufficiently large NOAEL database, we analyze how a difference in NOAEL values for pairs of structures depends on their pairwise similarity, where similarity takes both structural features and physicochemical properties into account. The width of the estimate NOAEL confidence interval is proportional to the uncertainty. Using the new threshold of toxicological concern (TTC) database enriched with antimicrobials, examples are presented to illustrate how uncertainty decreases with increasing analogue quality and also how NOAEL bounds estimation can be significantly improved by filtering the full database to include only substances that are in structure categories relevant to the target and analogue.

Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment.

Drug-induced liver injury (DILI) remains a challenge when translating knowledge from the preclinical stage to human use cases. Attempts to model human DILI directly based on the information from drug labels have had some success; however, the approach falls short of providing insights or addressing uncertainty due to the difficulty of decoupling the idiosyncratic nature of human DILI outcomes. Our approach in this comparative analysis is to leverage existing preclinical and clinical data as well as information on metabolism to better translate mammalian to human DILI. The human DILI knowledge base from the United States Food and Drug Administration (U.S. FDA) National Center for Toxicology Research contains 1036 pharmaceuticals from diverse therapeutic categories. A human DILI training set of 305 oral marketed drugs was prepared and a binary classification scheme applied. The second knowledge base consists of mammalian repeated dose toxicity with liver toxicity data from various regulatory sources. Within this knowledge base, we identified 278 pharmaceuticals containing 198 marketed or withdrawn oral drugs with data from the U.S. FDA new drug application and 98 active pharmaceutical ingredients from ToxCast. From this collection, a set of 225 oral drugs was prepared as the mammalian hepatotoxicity training set with particular end points of pathology findings in the liver and bile duct. Both human and mammalian data sets were processed using various learning algorithms, including artificial intelligence approaches. The external validations for both models were comparable to the training statistics. These data sets were also used to extract species-differentiating chemotypes that differentiate DILI effects on humans from mammals. A systematic workflow was devised to predict human DILI and provide mechanistic insights. For a given query molecule, both human and mammalian models are run. If the predictions are discordant, both metabolites and parents are investigated for quantitative structure-activity relationship and species-differentiating chemotypes. Their results are combined using the Dempster-Shafer decision theory to yield a final outcome prediction for human DILI with estimated uncertainty. Finally, these tools are implementable within an in silico platform for systematic evaluation.

AQUA-DUCT 1.0: structural and functional analysis of macromolecules from an intramolecular voids perspective.

MOTIVATION: Tunnels, pores, channels, pockets and cavities contribute to proteins architecture and performance. However, analysis and characteristics of transportation pathways and internal binding cavities are performed separately. We aimed to provide universal tool for analysis of proteins integral interior with access to detailed information on the ligands transportation phenomena and binding preferences. RESULTS: AQUA-DUCT version 1.0 is a comprehensive method for macromolecules analysis from the intramolecular voids perspective using small ligands as molecular probes. This version gives insight into several properties of macromolecules and facilitates protein engineering and drug design by the combination of the tracking and local mapping approach to small ligands. AVAILABILITY AND IMPLEMENTATION: http://www.aquaduct.pl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploring Solanum tuberosum Epoxide Hydrolase Internal Architecture by Water Molecules Tracking.

Several different approaches are used to describe the role of protein compartments and residues in catalysis and to identify key residues suitable for the modification of the activity or selectivity of the desired enzyme. In our research, we applied a combination of molecular dynamics simulations and a water tracking approach to describe the water accessible volume of Solanum tuberosum epoxide hydrolase. Using water as a molecular probe, we were able to identify small cavities linked with the active site: (i) one made up of conserved amino acids and indispensable for the proper positioning of catalytic water and (ii) two others in which modification can potentially contribute to enzyme selectivity and activity. Additionally, we identified regions suitable for de novo tunnel design that could also modify the catalytic properties of the enzyme. The identified hot-spots extend the list of the previously targeted residues used for modification of the regioselectivity of the enzyme. Finally, we have provided an example of a simple and elegant process for the detailed description of the network of cavities and tunnels, which can be used in the planning of enzyme modifications and can be easily adapted to the study of any other protein.

BALCONY: an R package for MSA and functional compartments of protein variability analysis.

BACKGROUND: Here, we present an R package for entropy/variability analysis that facilitates prompt and convenient data extraction, manipulation and visualization of protein features from multiple sequence alignments. BALCONY can work with residues dispersed across a protein sequence and map them on the corresponding alignment of homologous protein sequences. Additionally, it provides several entropy and variability scores that indicate the conservation of each residue. RESULTS: Our package allows the user to visualize evolutionary variability by locating the positions most likely to vary and to assess mutation candidates in protein engineering. CONCLUSION: In comparison to other R packages BALCONY allows conservation/variability analysis in context of protein structure with linkage of the appropriate metrics with physicochemical features of user choice. AVAILABILITY: CRAN project page: https://cran.r-project.org/package=BALCONY and our website: http://www.tunnelinggroup.pl/software/ for major platforms: Linux/Unix, Windows and Mac OS X.

AQUA-DUCT: a ligands tracking tool.

MOTIVATION: The identification and tracking of molecules which enter active site cavity requires screening the positions of thousands of single molecules along several thousand molecular dynamic steps. To fill the existing gap between tools searching for tunnels and pathways and advanced tools employed for accelerated water flux investigations, we have developed AQUA-DUCT. RESULTS: AQUA-DUCT is an easy-to-use tool that facilitates analysis of the behaviour of molecules that penetrate any selected region in a protein. It can be used for any type of molecules, e.g. water, oxygen, carbon dioxide, organic solvents, ions. AVAILABILITY AND IMPLEMENTATION: Linux, Windows, macOS, OpenBSD, http://www.aquaduct.pl . CONTACT: a.gora@tunnelinggroup.pl or info@aquaduct.pl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Molecular descriptor data explain market prices of a large commercial chemical compound library.

The relationship between the structure and a property of a chemical compound is an essential concept in chemistry guiding, for example, drug design. Actually, however, we need economic considerations to fully understand the fate of drugs on the market. We are performing here for the first time the exploration of quantitative structure-economy relationships (QSER) for a large dataset of a commercial building block library of over 2.2 million chemicals. This investigation provided molecular statistics that shows that on average what we are paying for is the quantity of matter. On the other side, the influence of synthetic availability scores is also revealed. Finally, we are buying substances by looking at the molecular graphs or molecular formulas. Thus, those molecules that have a higher number of atoms look more attractive and are, on average, also more expensive. Our study shows how data binning could be used as an informative method when analyzing big data in chemistry.

New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modeling.

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.

Integrative Modeling Strategies for Predicting Drug Toxicities at the eTOX Project.

Early prediction of safety issues in drug development is at the same time highly desirable and highly challenging. Recent advances emphasize the importance of understanding the whole chain of causal events leading to observable toxic outcomes. Here we describe an integrative modeling strategy based on these ideas that guided the design of eTOXsys, the prediction system used by the eTOX project. Essentially, eTOXsys consists of a central server that marshals requests to a collection of independent prediction models and offers a single user interface to the whole system. Every of such model lives in a self-contained virtual machine easy to maintain and install. All models produce toxicity-relevant predictions on their own but the results of some can be further integrated and upgrade its scale, yielding in vivo toxicity predictions. Technical aspects related with model implementation, maintenance and documentation are also discussed here. Finally, the kind of models currently implemented in eTOXsys is illustrated presenting three example models making use of diverse methodology (3D-QSAR and decision trees, Molecular Dynamics simulations and Linear Interaction Energy theory, and fingerprint-based QSAR).

Structure-based modeling of dye-fiber affinity with SOM-4D-QSAR paradigm: application to set of anthraquinone derivatives.

A comparative structure-affinity study of anthraquinone dyes adsorption on cellulose fibre is presented in this paper. We used receptor-dependent 4D-QSAR methods based on grid and neural (SOM) methodology coupled with IVEPLS procedure. The applied RD 4D-QSAR approach focuses mainly on the ability of mapping dye properties to verify the concept of tinctophore in dye chemistry. Moreover, the stochastic SMV procedure to investigate the predictive ability of the method for a large population of 4D-QSAR models was employed. The obtained findings were compared with the previously published RI 3D/4D-QSAR models for the corresponding anthraquinone trainings sets. The neutral (protonated) and anionic (deprotonated) forms of anthraquinone scaffold were examined in order to deal with the uncertainty of the dye ionization state. The results are comparable to both the neutral and anionic dye sets regardless of the occupancy and charge descriptors applied, respectively. It is worth noting that the SOM-4D-QSAR behaves comparably to the cubic counterpart which is observed in each training/test subset specification (4D-QSAR-Jo vs SOM- 4D-QSARo and 4D-QSAR-Jq vs SOM-4D-QSARq). Additionally, an attempt was made to specify a common set of variables contributing significantly to dye-fiber binding affinity; it was simultaneously performed for some arbitrary chosen SMV models. The presented RD 4D-QSAR methodology together with IVE-PLS procedure provides a robust and predictive modeling technique, which facilitates detailed specification of the molecular motifs significantly contributing to the fiber-dye affinity.

Probing a chemical space for fragmental topology-activity landscapes (FRAGTAL): application for diketo acid and catechol HIV integrase inhibitor offspring fragments.

Fragmental topology-activity landscapes (FRAGTAL), a new concept for encoding molecular descriptors for fragonomics into the framework of the molecular database records is presented in this paper. Thus, a structural repository containing biological activity data was searched in a substructure mode by a series of molecular fragments constructed in an incremental or decremental manner. The resulted series of database hits annotated with their activities construct FRAGTAL descriptors encoding a frequency of the certain fragments among active compounds and/or their activities. Actually, this method might be interpreted as a simplified adaptation of the frequent subgraph mining (FSM) method. The FRAGTAL method reconstructs the way in which medicinal chemists are used to designing a prospective drug structure intuitively. A representative example of the practical application of FRAGTAL within the ChemDB Anti-HIV/OI/TB database for disclosing new fragments for HIV-1 integrase inhibition is discussed. In particular, FRAGTAL method identifies ethyl malonate amide (EMA) as the diketo acid (DKA) related arrangement. Since new molecular constructs based on the EMA fragment are still a matter of future investigations we referred to this as anthe DKA offspring.

Mapping fragmental drug-likeness in the MoStBioDat environment: intramolecular hydrogen bonding motifs in ß-ketoenols.

A detailed knowledge of hydrogen bond geometry and its directional preferences is vital for in silico investigations of the ligand-receptor short-range non-covalent interactions. The spatial arrangement of the carbonyl and hydroxyl groups seems to determine the capability of ß-ketoenol derivatives to recognize the surrounding environment by forming inter- and intra-molecular hydrogen bonds (IHB). In the current study we examined the application of the MoStBioDat platform for a massive database screening of the IHB motifs in ß-ketoenol subunits (O=C-C=C-OH). Then, the virtual 3D structural data derived from ZINC and PubChem repository were compared to the experimentally determined CSD data. Differences specific for each database were discovered, which indicated inaccuracies in the simulated data.

Does molecular docking reveal alternative chemopreventive mechanism of activation of oxidoreductase by sulforaphane isothiocyanates?

Isothiocyanates (ITC) are well-known chemopreventive agents extracted from vegetables. This activity results from the activation of human oxidoreductase. In this letter, the uncompetitive activatory mechanism of ITC was investigated using docking and molecular dynamics simulations. This indicates that NAD(P)H:quinone oxidoreductase can efficiently improve enzyme-substrate recognition within the catalytic site if the ITC activator supports the interaction in the uncompetitive binding site.

Receptor independent and receptor dependent CoMSA modeling with IVE-PLS: application to CBG benchmark steroids and reductase activators.

Comparative molecular surface analysis (CoMSA) with robust IVE-PLS variable elimination if tested for the benchmark CBG steroid series provides highly predictive RI 3D QSAR models, but failed however to model the activity of sulforaphane (SP) activators of quinone reductase. The application of the SP poses obtained from multipose molecular docking to model the RD IVE-PLS CoMSA resulted in a predictive form. This model indicated lipophilic potential as the activity determinant. The individual molecular surface areas of the highest contribution to the SP activity was identified and visualized by CoMSA contour plots.

Modeling robust QSAR.

Quantitative Structure Activity Relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. This method can be defined as indirect molecular design by the iterative sampling of the chemical compounds space to optimize a certain property and thus indirectly design the molecular structure having this property. However, modeling the interactions of chemical molecules in biological systems provides highly noisy data, which make predictions a roulette risk. In this paper we briefly review the origins for this noise, particularly in multidimensional QSAR. This was classified as the data, superimposition, molecular similarity, conformational, and molecular recognition noise. We also indicated possible robust answers that can improve modeling and predictive ability of QSAR, especially the self-organizing mapping of molecular objects, in particular, the molecular surfaces, a method that was brought into chemistry by Gasteiger and Zupan.

3D QSAR study of hypolipidemic asarones by comparative molecular surface analysis.

Three-dimensional quantitative structure-activity relationship (3D QSAR) modeled for alpha-asarone derivatives using the comparative molecular surface analysis (CoMSA) allowed us to reveal a correlation between the activity of these compounds and the electrostatic potential at the molecular surface. The grid formalism (s-CoMSA) allowed us to indicate a pharmacophore that is of key importance for compound activity. The CoMSA formalism coupled with the iterative variable elimination method gives a highly predictive model.