Query Compound Research Articles

Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach

The umami taste is one of the five basic taste modalities normally linked to the protein content in food. The implementation of fast and cost-effective tools for the prediction of the umami taste of a molecule remains extremely interesting to understand the molecular basis of this taste and to effectively rationalise the production and consumption of specific foods and ingredients. However, the only examples of umami predictors available in the literature rely on the amino acid sequence of the analysed peptides, limiting the applicability of the models. In the present study, we developed a novel ML-based algorithm, named VirtuousUmami, able to predict the umami taste of a query compound starting from its SMILES representation, thus opening up the possibility of potentially using such a model on any database through a standard and more general molecular description. Herein, we have tested our model on five databases related to foods or natural compounds. The proposed tool will pave the way toward the rationalisation of the molecular features underlying the umami taste and toward the design of specific peptide-inspired compounds with specific taste properties.

Ligand-Based Discovery of a Small Molecule as Inhibitor of α-Synuclein Amyloid Formation

α-Synuclein (α-Syn) aggregates are implicated in Parkinson's disease (PD), so inhibitors of α-Syn aggregation have been intensively explored. It has been demonstrated that small molecules might be able to reduce α-Syn aggregation in fibrils, thus exerting neuroprotective effects in models of PD. To expand our knowledge about the structural requirements for blocking the recognition process into the oligomeric assembly of α-Syn aggregates, we performed a ligand-based virtual screening procedure using two well-known α-Syn aggregation inhibitors, SynuClean-D and ZPD-2, as query compounds. A collection of thirty-four compounds bearing distinct chemical functionalities and mutual chemical features were studied in a Th-T fluorescence test, thus identifying 5-(2,6-dinitro-4-(trifluoromethyl)benzyl)-1-methyl-1H-tetrazole (named MeSC-04) as a potent α-Syn amyloid formation inhibitor that demonstrated similar behavior when compared to SynuClean-D in the thioflavin-T-monitored kinetic assays, with both molecules reducing the number and size of amyloid fibrils, as evidenced by electron microscopy. Molecular modeling studies suggested the binding mode of MeSC-04 through the identification of putative druggable pockets on α-syn fibrils and a subsequent consensus docking methodology. Overall, this work could furnish new insights in the development of α-Syn amyloid inhibitors from synthetic sources.

QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads.

ALK tyrosine kinase ALK TK is an important target in the development of anticancer drugs. In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm–multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R2 = 0.69–0.87, F = 403.46–292.11, etc., internal validation parameters; Q2LOO = 0.69–0.86, Q2LMO = 0.69–0.86, CCCcv = 0.82–0.93, etc., or external validation parameters Q2F1 = 0.64–0.82, Q2F2 = 0.63–0.82, Q2F3 = 0.65–0.81, R2ext = 0.65–0.83 including RMSEtr < RMSEcv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.

First report on soil ecotoxicity prediction against Folsomia candida using intelligent consensus predictions and chemical read-across.

Soil invertebrates serve as an outstanding biological indicator of the terrestrial ecosystem and overall soil quality, considering their high sensitivity when compared to other indicators of soil quality. In this study, the available soil ecotoxicity data (pEC50) against the soil invertebrate Folsomia candida (C. name: Springtail) (n = 45) were collated from the database of ECOTOX (cfpub.epa.gov/ecotox) and subjected to QSAR analysis using 2D descriptors. Four partial least squares (PLS) models were built based on the features selected through genertic algorithm followed by the best subset selection. These four models were then used as inputs for Intelligent Consensus Predictor version 1.2 (PLS version) to get the final consensus predictions, using the best selection of predictions (compound-wise) from four "qualified" individual models. Both internal and external validations metrics of the consensus predictions are well- balanced and within the acceptable range as per the OECD criteria. The consensus model was found to be better than the previous developed models for this endpoint. Predictions were also made using the Chemical Read-across approach, which showed even better external validation metric values than the consensus predictions. From the selected features in the QSAR models, it has been found out that molecular weight and presence of adi-thiophosphate group, electron donor groups, and polyhalogen substitutions have a significant impact on the soil ecotoxicity. The soil ecotoxicological risk assessment of organic chemicals can therefore be prioritized by these features. The models developed from diverse structural organic compounds can be applied to any new query compound for data gap filling.

Quantitative predictions from chemical read-across and their confidence measures

In silico modeling new approach methodologies (NAMs) are viewed as a promising starting point for filling the existing gaps in safety and ecosafety data. Read-across is one of the most widely used alternative tools for hazard assessment, aimed at filling data gaps. However, there are no systematic studies or recommendations on the measures to identify the quality of read-across predictions for the data points without any experimental response data. Recently, we have reported a new similarity-based read-across algorithm for the prediction of toxicity (biological activity in general) of untested compounds from structural analogues (the tool available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home). Three similarity estimation techniques such as, Euclidean distance-based similarity, Gaussian kernel function similarity, and Laplacian kernel function similarity are used in this algorithm. As the confidence of predictions for untested compounds is important information, we have addressed this issue here by consideration of several similarity and error – based criteria. The role of these measures in discriminating high and low residual query compounds is studied in three different approaches: (a) comparison of means of a measure for high and low residual groups; (b) development of classification models for absolute residuals to identify the contributing measures; (c) application of the sum of ranking differences (SRD) approach to identify the measures closer to the reference rank defined by the absolute residuals. Finally, the frequency of occurrences of different measures in the three approaches is compared. The results from three data sets with 10 divisions of source and target compounds in each case indicate that weighted standard deviation of the predicted response values appear to be the most deterministic feature for the reliability of predictions followed by different similarity-based features. The derived reliability measures will provide a greater confidence to the quality of quantitative predictions from the chemical read-across tool for new query compounds.

Methods to Detect Small Molecule Inhibition of RING E3 Ligase Activity.

Protein ubiquitination is an essential post-translational modification that regulates a large number of cellular processes. This reaction is facilitated by the consecutive action of three central enzymes, i.e., E1 activating enzyme, E2 conjugating enzyme, and the E3 ligase. More than 600 E3 enzymes guarantee the specificity and selectivity of these reactions and thus represent an exciting, while highly underrepresented, class of drug targets. Specific methods can be employed to monitor their activity and thus query compound libraries for inhibitory small molecules. Here, we describe two protocols-one high-throughput and one low-throughput method-to detect E3 ligase activity and test small molecule modulation. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: AlphaScreen assay to measure TRAF6-Ubc13 interaction Basic Protocol 2: Gel-based in vitro ubiquitination assay (K63-linked chains).

DeLA-Drug: A Deep Learning Algorithm for Automated Design of Druglike Analogues.

In this paper, we present a deep learning algorithm for automated design of druglike analogues (DeLA-Drug), a recurrent neural network (RNN) model composed of two long short-term memory (LSTM) layers and conceived for data-driven generation of similar-to-bioactive compounds. DeLA-Drug captures the syntax of SMILES strings of more than 1 million compounds belonging to the ChEMBL28 database and, by employing a new strategy called sampling with substitutions (SWS), generates molecules starting from a single user-defined query compound. Remarkably, the algorithm preserves druglikeness and synthetic accessibility of the known bioactive compounds present in the ChEMBL28 repository. The absence of any time-demanding fine-tuning procedure enables DeLA-Drug to perform a fast generation of focused libraries for further high-throughput screening and makes it a suitable tool for performing de novo design even in low-data regimes. To provide a concrete idea of its applicability, DeLA-Drug was applied to the cannabinoid receptor subtype 2 (CB2R), a known target involved in different pathological conditions such as cancer and neurodegeneration. DeLA-Drug, available as a free web platform (http://www.ba.ic.cnr.it/softwareic/deladrugportal/), can help medicinal chemists interested in generating analogues of compounds already available in their laboratories and, for this reason, good candidates for an easy and low-cost synthesis.

Prediction reliability of QSAR models: an overview of various validation tools.

The reliability of any quantitative structure-activity relationship (QSAR) model depends on multiple aspects such as the accuracy of the input dataset, selection of significant descriptors, the appropriate splitting process of the dataset, statistical tools used, and most notably on the measures of validation. Validation, the most crucial step in QSAR model development, confirms the reliability of the developed QSAR models and the acceptability of each step in the model development. The present review deals with various validation tools that involve multiple techniques that improve the model quality and robustness. The double cross-validation tool helps in building improved quality models using different combinations of the same training set in an inner cross-validation loop. This exhaustive method is also integrated for small datasets (< 40 compounds) in another tool, namely the small dataset modeler tool. The main aim of QSAR researchers is to improve prediction quality by lowering the prediction errors for the query compounds. 'Intelligent' selection of multiple models and consensus predictions integrated in the intelligent consensus predictor tool were found to be more externally predictive than individual models. Furthermore, another tool called Prediction Reliability Indicator was explained to understand the quality of predictions for a true external set. This tool uses a composite scoring technique to identify query compounds as 'good' or 'moderate' or 'bad' predictions. We have also discussed a quantitative read-across tool which predicts a chemical response based on the similarity with structural analogues. The discussed tools are freely available from https://dtclab.webs.com/software-tools or http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/ and https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home (for read-across).

The SwissSimilarity 2021 Web Tool: Novel Chemical Libraries and Additional Methods for an Enhanced Ligand-Based Virtual Screening Experience.

Hit finding, scaffold hopping, and structure–activity relationship studies are important tasks in rational drug discovery. Implementation of these tasks strongly depends on the availability of compounds similar to a known bioactive molecule. SwissSimilarity is a web tool for low-to-high-throughput virtual screening of multiple chemical libraries to find molecules similar to a compound of interest. According to the similarity principle, the output list of molecules generated by SwissSimilarity is expected to be enriched in compounds that are likely to share common protein targets with the query molecule and that can, therefore, be acquired and tested experimentally in priority. Compound libraries available for screening using SwissSimilarity include approved drugs, clinical candidates, known bioactive molecules, commercially available and synthetically accessible compounds. The first version of SwissSimilarity launched in 2015 made use of various 2D and 3D molecular descriptors, including path-based FP2 fingerprints and ElectroShape vectors. However, during the last few years, new fingerprinting methods for molecular description have been developed or have become popular. Here we would like to announce the launch of the new version of the SwissSimilarity web tool, which features additional 2D and 3D methods for estimation of molecular similarity: extended-connectivity, MinHash, 2D pharmacophore, extended reduced graph, and extended 3D fingerprints. Moreover, it is now possible to screen for molecular structures having the same scaffold as the query compound. Additionally, all compound libraries available for screening in SwissSimilarity have been updated, and several new ones have been added to the list. Finally, the interface of the website has been comprehensively rebuilt to provide a better user experience. The new version of SwissSimilarity is freely available starting from December 2021.

In silico identification and study of potential anti-mosquito juvenile hormone binding protein (MJHBP) compounds as candidates for dengue virus - Vector insecticides

Dengue has become a huge global health burden. It is currently recognized as the most rapidly spreading mosquito-borne viral disease. Yet, there are currently no licensed vaccines or specific therapeutics to manage the virus, thus, scaling up vector control approaches is important in controlling this viral spread. This study aimed to identify and study in silico, potential anti-mosquito compounds targeting Juvenile hormone (JH) mediated pathways via the Mosquito Juvenile Hormone Binding Protein (MJHBP). The study was implemented using series of computational methods. The query compounds included pyrethroids and those derived from ZINC and ANPDB databases using a simple pharmacophore model in Molecular Operating Environment (MOE). Molecular docking of selected compounds’ library was implemented in MOE. The resultant high-score compounds were further validated by molecular dynamics simulation via Maestro 12.3 module and the respective Prime/Molecular Mechanics Generalized Born Surface Area (Prime/MM-GBSA) binding energies computed. The study identified compounds-pyrethroids, natural and synthetic - with high docking energy scores (ranging from 10.91–12.34 kcal/mol). On further analysis of the high-ranking (in terms of docking scores) compounds using MD simulation, the compounds - Ekeberin D4, Maesanin, Silafluofen and ZINC16919139- revealed very low binding energies (−122.99, −72.91 -104.50 and,-74.94 kcal/mol respectively), fairly stable complex and interesting interaction with JH-binding site amino acid residues on MJHBP. Further studies can explore these compounds in vitro/in vivo in the search for more efficient mosquito vector control.

A Combined approach of QSAR study, molecular docking and pharmacokinetics prediction of promising Amide-Ac6-aminoacetonitriles Cathepsin K inhibitors

• Combined use of QSAR, molecular docking and pharmacokinetics prediction approaches. • One promising Cathepsin K inhibitor was identified. Cathepsin K (Cat-K) highly expressed in osteoclasts, is considered to be a promising target for osteoclast-related maladies. In the present study, quantitative structure–activity relationships (QSAR) and molecular docking approaches were used to identify promising Cat-K inhibitors from a series of Amide-Ac6-aminoacetonitriles analogs. During the two-dimensional QSAR study, the statistical model PLS_MDL was chosen (r 2 = 0.823, q 2 (cross-validation) = 0.732). Descriptors with positive or negative contributions were derived from model PLS_MDL. In order to build robust three-dimensional QSAR models, three different molecular fingerprints were used as analysis precepts for clustering and three different minimization algorithms were employed for generation of molecular poses. The best database which used molecular fingerprint of FCFP and minimization algorithm of Smart Minimizer was selected for further QSAR model construction. The ideal CoMFA and CoMSIA models were acquired (CoMFA: r 2 = 0.963, q 2 = 0.674, r 2 pred = 0.702 and Q 2 (F2) = 0.616; CoMSIA_SEHA: r 2 = 0.989, q 2 = 0.622, r 2 pred = 0.719 and Q 2 (F2) = 0.638). Physicochemical, structural, electrostatic, and steric properties were derived from the created QSAR models and then applied to portray a design scheme. Furthermore, after a selecting of docking scheme, docking studies were carried out to measure the binding affinity of eligible query compounds and to reveal the detailed interactions between ligand and the target protein. Amongst these designed compounds, compound19OX32 was focused for its preferable predicted activity and docking score. Strong interactions and extra hydrogen bonds which echoed the previous summarized design scheme were also observed via a deep investigation of docking result. Consequently, compound 19OX32 was identified as promising Cat-K inhibitor.

Quantitative Polypharmacology Profiling Based on a Multifingerprint Similarity Predictive Approach.

We present a new quantitative ligand-based bioactivity prediction approach employing a multifingerprint similarity search algorithm, enabling the polypharmacological profiling of small molecules. Quantitative bioactivity predictions are made on the basis of the statistical distributions of multiple Tanimoto similarity θ values, calculated through 13 different molecular fingerprints, and of the variation of the measured biological activity, reported as ΔpIC50, for all of the ligands sharing a given protein drug target. The application data set comprises as much as 4241 protein drug targets as well as 418 485 ligands selected from ChEMBL (release 25) by employing a set of well-defined filtering rules. Several large internal and external validation studies were carried out to demonstrate the robustness and the predictive potential of the herein proposed method. Additional comparative studies, carried out on two freely available and well-known ligand-target prediction platforms, demonstrated the reliability of our proposed approach for accurate ligand-target matching. Moreover, two applicative cases were also discussed to practically describe how to use our predictive algorithm, which is freely available as a user-friendly web platform. The user can screen single or multiple queries at a time and retrieve the output as a terse html table or as a json file including all of the information concerning the explored similarities to obtain a deeper understanding of the results. High-throughput virtual reverse screening campaigns, allowing for a given query compound the quick detection of the potential drug target from a large collection of them, can be carried out in batch on demand.

SimilarityLab: Molecular Similarity for SAR Exploration and Target Prediction on the Web

Exploration of chemical space around hit, experimental, and known active compounds is an important step in the early stages of drug discovery. In academia, where access to chemical synthesis efforts is restricted in comparison to the pharma-industry, hits from primary screens are typically followed up through purchase and testing of similar compounds, before further funding is sought to begin medicinal chemistry efforts. Rapid exploration of druglike similars and structure–activity relationship profiles can be achieved through our new webservice SimilarityLab. In addition to searching for commercially available molecules similar to a query compound, SimilarityLab also enables the search of compounds with recorded activities, generating consensus counts of activities, which enables target and off-target prediction. In contrast to other online offerings utilizing the USRCAT similarity measure, SimilarityLab’s set of commercially available small molecules is consistently updated, currently containing over 12.7 million unique small molecules, and not relying on published databases which may be many years out of date. This ensures researchers have access to up-to-date chemistries and synthetic processes enabling greater diversity and access to a wider area of commercial chemical space. All source code is available in the SimilarityLab source repository.

QSAR modelling of inhalation toxicity of diverse volatile organic molecules using no observed adverse effect concentration (NOAEC) as the endpoint

Nowadays, air pollution due to urbanization and reduction of forestry is emerging as a serious threat to humans and the environment. According to the World Health Organization, respiratory diseases are the third most mortality factor in the world. Chemical research organizations and industries are producing a large number of new chemical compounds continuously. Although toxicity testing of those chemicals on animals is costly, resource and time consuming, these data cannot be properly extrapolated to humans and other animals, and also these raise ethical issues. In this background, we have developed Quantitative Structure-Activity Relationship (QSAR) models using the No Observed Adverse Effect Concentration (NOAEC) as the endpoint to assess inhalation toxicity of diverse organic chemicals, commonly used and exposed by us in our daily life. No Observed Adverse Effect Concentration (NOAEC) can be used for long term toxicity studies towards the human inhalation risk assessment, as recommended by Organization for Economic Co-operation and Development (OECD) in guidance document 39. A particular QSAR model may not be equally effective for prediction of all query compounds from a given set of compounds; therefore, we have developed multiple models, which are robust, sound and well predictive from the statistical point of view to forecast the NOAEC values for the new untested compounds. Subsequently the validated individual models were employed to generate consensus models, in order to improve the quality of predictions and to reduce prediction errors. We have investigated some crucial structural features from these models which may regulate inhalation toxicity for newly produced molecules. Thus, our developed models may help in toxicity assessment towards reducing the health hazards for new chemicals.

Exploring Chemical Information in PubChem.

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical database that serves scientific communities as well as the general public. This database collects chemical information from hundreds of data sources and organizes them into multiple data collections, including Substance, Compound, BioAssay, Protein, Gene, Pathway, and Patent. These collections are interlinked with each other, allowing users to discover related records in the various collections (e.g., drugs targeting a protein or genes modulated by a chemical). PubChem can be searched by keyword (e.g., a chemical, protein, or gene name) as well as by chemical structure. The input structure can be provided using popular line notations or drawn with the PubChem Sketcher. PubChem supports various types of structure searches, including identity search, 2‐D and 3‐D similarity searches, and substructure and superstructure searches. Results from multiple searches can be combined using Boolean operators (i.e., AND, OR, and NOT) to formulate complex queries. PubChem allows the user to quickly retrieve a list of records annotated with a particular classification or ontological term. This paper provides step‐by‐step instructions on how to explore PubChem data with examples of commonly requested tasks. © 2021. This article is a U.S. Government work and is in the public domain in the USA. Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Finding genes and proteins that interact with a given compoundBasic Protocol 2: Finding drug‐like compounds similar to a query compound through a two‐dimensional (2‐D) similarity searchBasic Protocol 3: Finding compounds similar to a query compound through a three‐dimensional (3‐D) similarity searchSupport Protocol: Computing similarity scores between compoundsBasic Protocol 4: Getting the bioactivity data for the hit compounds from substructure searchBasic Protocol 5: Finding drugs that target a particular geneBasic Protocol 6: Getting bioactivity data of all chemicals tested against a protein.Basic Protocol 7: Finding compounds annotated with classifications or ontological termsBasic Protocol 8: Finding stereoisomers and isotopomers of a compound through identity search

CyProduct: A Software Tool for Accurately Predicting the Byproducts of Human Cytochrome P450 Metabolism.

In silico metabolism prediction is a cheminformatic task of autonomously predicting the set of metabolic byproducts produced from a specified molecule and a set of enzymes or reactions. Here, we describe a novel machine learned in silico cytochrome P450 (CYP450) metabolism prediction suite, called CyProduct, that accurately predicts metabolic byproducts for a specified molecule and a human CYP450 isoform. It includes three modules: (1) CypReact, a tool that predicts if the query compound reacts with a given CYP450 enzyme, (2) CypBoM, a tool that accurately predicts the "bond site" of the reaction (i.e., which specific bonds within the query molecule react with the CYP isoform), and (3) MetaboGen, a tool that generates the metabolic byproducts based on CypBoM's bond-site prediction. CyProduct predicts metabolic biotransformation products for each of the nine most important human CYP450 enzymes. CypBoM uses an important new concept called "bond of metabolism" (BoM), which extends the traditional "site of metabolism" (SoM) by specifying the information about the set of chemical bonds that is modified or formed in a metabolic reaction (rather than the specific atom). We created a BoM database for 1845 CYP450-mediated Phase I reactions, then used this to train the CypBoM Predictor to predict the reactive bond locations on substrate molecules. CypBoM Predictor's cross-validated Jaccard score for reactive bond prediction ranged from 0.380 to 0.452 over the nine CYP450 enzymes. Over variants of a test set of 68 known CYP450 substrates and 30 nonreactants, CyProduct outperformed the other packages, including ADMET Predictor, BioTransformer, and GLORY, by an average of 200% (with respect to Jaccard score) in terms of predicting metabolites. The CyProduct suite and the data sets are freely available at https://bitbucket.org/wishartlab/cyproduct/src/master/.

Inverse Virtual Screening for the rapid re-evaluation of the presumed biological safe profile of natural products. The case of steviol from Stevia rebaudiana glycosides on farnesoid X receptor (FXR)

Nonnutritive sweeteners (NNSs) are widely employed as dietary substitutes for classical sugars thanks to their safety profile and low toxicity. In this study, a re-evaluation of the biological effects of steviol (1), the main metabolite from Stevia rebaudiana glycosides, was performed using the Inverse Virtual Screening (IVS) target fishing computational approach. Starting from well-known pharmacological properties of Stevia rebaudiana glycosides, this computational tool was employed for predicting the putative interacting targets of 1 and, afterwards, of its five synthetic ester derivatives 2–6, accounting a large panel of proteins involved in cancer and inflammation events. Applying this methodology, the farnesoid X receptor (FXR) was identified as the putative target partner of 1–6. The predicted ligand-protein interactions were corroborated by transactivation assays, specifically disclosing the agonistic activity of 1 and the antagonistic activities of 2–6 on FXR. The reported results highlight the feasibility of IVS as a fast and potent tool for predicting the interacting targets of query compounds, addressing the re-evaluation of their bioactivity. In light of the obtained results, the presumably safe profile of known compounds, such as the case of steviol (1), is critically discussed.

Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation.

Free energies govern the behavior of soft and liquid matter, and improving their predictions could have a large impact on the development of drugs, electrolytes, or homogeneous catalysts. Unfortunately, it is challenging to devise an accurate description of effects governing solvation such as hydrogen-bonding, van der Waals interactions, or conformational sampling. We present a Free energy Machine Learning (FML) model applicable throughout chemical compound space and based on a representation that employs Boltzmann averages to account for an approximated sampling of configurational space. Using the FreeSolv database, FML's out-of-sample prediction errors of experimental hydration free energies decay systematically with training set size, and experimental uncertainty (0.6 kcal/mol) is reached after training on 490 molecules (80% of FreeSolv). Corresponding FML model errors are on par with state-of-the art physics based approaches. To generate the input representation for a new query compound, FML requires approximate and short molecular dynamics runs. We showcase its usefulness through analysis of solvation free energies for 116k organic molecules (all force-field compatible molecules in the QM9 database), identifying the most and least solvated systems and rediscovering quasi-linear structure-property relationships in terms of simple descriptors such as hydrogen-bond donors, number of NH or OH groups, number of oxygen atoms in hydrocarbons, and number of heavy atoms. FML's accuracy is maximal when the temperature used for the molecular dynamics simulation to generate averaged input representation samples in training is the same as for the query compounds. The sampling time for the representation converges rapidly with respect to the prediction error.

Introducing DerivatizeME and its Application in the Augmentation of a Natural Product Library

The large chemical space universe can be traversed by screening libraries of compounds that possess novel medicinally relevant chemistries, properties and complexity criteria. These libraries can be populated with the use of exhaustive, de novo approaches or inspired, combinatorial approaches. By assuming that natural products within screening libraries may be classified as a source of feedstock for populating virtual libraries, they can act as scaffolds upon which exhaustive approaches may be used in exploring chemical space. In order to achieve this, we have built DerivatizeME as a tool that enumerates derivatives of query compounds in order to evaluate their relevance for further assessment and development. This technique was applied to natural products present in the South African natural compound database (SANCDB). By expanding the chemical space of SANCDB compounds through the generation of SANCDB derivatives, we were able to graduate some natural products that were in undesirable regions of medicinally relevant chemical space, to acceptable regions of this chemical space. These modified scaffolds are available for further development, testing and evaluation in a manner similar to natural product driven focused libraries. The natural product parent is used, through its derivatives, instead of being discarded from screening protocols. This approach has the potential to enhance the efficiency of the natural product library in providing successful hits, amplifying the potential that they possess to access both novel bioactives and privileged scaffolds which may have otherwise been overlooked.

Epigenetic Target Profiler: A Web Server to Predict Epigenetic Targets of Small Molecules.

The identification of protein targets of small molecules is essential for drug discovery. With the increasing amount of chemogenomic data in the public domain, multiple ligand-based models for target prediction have emerged. However, these models are generally biased by the number of known ligands for different targets, which involves an under-representation of epigenetic targets, and despite the increasing importance of epigenetic targets in drug discovery, there are no open tools for epigenetic target prediction. In this work, we introduce Epigenetic Target Profiler (ETP), a freely accessible and easy-to-use web application for the prediction of epigenetic targets of small molecules. For a query compound, ETP predicts its bioactivity profile over a panel of 55 different epigenetic targets. To that aim, ETP uses a consensus model based on two binary classification models for each target, relying on support vector machines and built on molecular fingerprints of different design. A distance-to-model parameter related to the reliability of the predictions is included to facilitate their interpretability and assist in the identification of small molecules with potential epigenetic activity. Epigenetic Target Profiler is freely available at http://www.epigenetictargetprofiler.com.