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Application of a New Scaffold Concept for Computational Target Deconvolution of Chemical Cancer Cell Line Screens.

ABSTRACT: Target deconvolution of phenotypic assays is a hot topic in chemical biology and drug discovery. The ultimate goal is the identification of targets for compounds that produce interesting phenotypic readouts. A variety of experimental and computational strategies have been devised to aid this process. A widely applied computational approach infers putative targets of new active molecules on the basis of their chemical similarity to compounds with activity against known targets. Herein, we introduce a molecular scaffold-based variant for similarity-based target deconvolution from chemical cancer cell line screens that were used as a model system for phenotypic assays. A new scaffold type was used for substructure-based similarity assessment, termed analog series-based (ASB) scaffold. Compared with conventional scaffolds and compound-based similarity calculations, target assignment centered on ASB scaffolds resulting from screening hits and bioactive reference compounds restricted the number of target hypotheses in a meaningful way and lead to a significant enrichment of known cancer targets among candidates.

PROVIDER: S-EPMC6044569 | BioStudies |

REPOSITORIES: biostudies

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Collection of analog series-based scaffolds from public compound sources.

Project description:Aim:Providing a large and freely available in silico collection of analog series-based (ASB) scaffolds for computational design and medicinal chemistry applications. Methodology:Using a recently introduced computational method, ASB scaffolds with single and multiple substitution sites were systematically isolated from publicly available active compounds. Data:A total of 23,791 unique ASB scaffolds are made available in an organized and machine-readable form as an open access deposition. For each ASB scaffold, the number of analogs it represents is also provided. Next steps:The freely available collection of ASB scaffolds will be further analyzed to explore different types of scaffold relationships. The ASB scaffold collection will periodically be updated.

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Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens.

Project description:Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60-70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/).

| S-EPMC4380459 | BioStudies

Project description:Target identification remains a major challenge for modern drug discovery programs aimed at understanding the molecular mechanisms of drugs. Computational target prediction approaches like 2D chemical similarity searches have been widely used but are limited to structures sharing high chemical similarity. Here, we present a new computational approach called chemical similarity network analysis pull-down 3D (CSNAP3D) that combines 3D chemical similarity metrics and network algorithms for structure-based drug target profiling, ligand deorphanization, and automated identification of scaffold hopping compounds. In conjunction with 2D chemical similarity fingerprints, CSNAP3D achieved a >95% success rate in correctly predicting the drug targets of 206 known drugs. Significant improvement in target prediction was observed for HIV reverse transcriptase (HIVRT) compounds, which consist of diverse scaffold hopping compounds targeting the nucleotidyltransferase binding site. CSNAP3D was further applied to a set of antimitotic compounds identified in a cell-based chemical screen and identified novel small molecules that share a pharmacophore with Taxol and display a Taxol-like mechanism of action, which were validated experimentally using in vitro microtubule polymerization assays and cell-based assays.

| S-EPMC5511031 | BioStudies

Structural and Activity Profile Relationships Between Drug Scaffolds.

Project description:Core structures of current drugs have been assembled and their structural relationships and activity profiles have been explored. Drug scaffolds were frequently involved in different types of structural relationships. In addition, a variety of activity profile relationships between structurally related drug scaffolds were detected, ranging from closely overlapping to distinct profiles. Furthermore, when structural and activity profile relationships of scaffolds from drugs and bioactive compounds were compared, systematic differences were detected. Consensus activity profiles were introduced as a new approach for the qualitative and quantitative assessment of activity similarity of structurally related drugs represented by the same scaffold. On the basis of consensus activity profiles, scaffolds representing drugs active against distinct targets can be distinguished from drugs having similar target profiles and target hypotheses can be derived for individual drugs. Given the results of our analysis, drug scaffolds have been systematically organized according to structural and activity profile criteria. Our scaffold sets and the associated information are made freely available.

| S-EPMC4406972 | BioStudies

Deep scaffold hopping with multimodal transformer neural networks

Project description:Scaffold hopping is a central task of modern medicinal chemistry for rational drug design, which aims to design molecules of novel scaffolds sharing similar target biological activities toward known hit molecules. Traditionally, scaffolding hopping depends on searching databases of available compounds that can't exploit vast chemical space. In this study, we have re-formulated this task as a supervised molecule-to-molecule translation to generate hopped molecules novel in 2D structure but similar in 3D structure, as inspired by the fact that candidate compounds bind with their targets through 3D conformations. To efficiently train the model, we curated over 50 thousand pairs of molecules with increased bioactivity, similar 3D structure, but different 2D structure from public bioactivity database, which spanned 40 kinases commonly investigated by medicinal chemists. Moreover, we have designed a multimodal molecular transformer architecture by integrating molecular 3D conformer through a spatial graph neural network and protein sequence information through Transformer. The trained DeepHop model was shown able to generate around 70% molecules having improved bioactivity together with high 3D similarity but low 2D scaffold similarity to the template molecules. This ratio was 1.9 times higher than other state-of-the-art deep learning methods and rule- and virtual screening-based methods. Furthermore, we demonstrated that the model could generalize to new target proteins through fine-tuning with a small set of active compounds. Case studies have also shown the advantages and usefulness of DeepHop in practical scaffold hopping scenarios. <h4>Supplementary Information</h4> The online version contains supplementary material available at 10.1186/s13321-021-00565-5.

| S-EPMC8590293 | BioStudies

Drug-likeness analysis of traditional Chinese medicines: 2. Characterization of scaffold architectures for drug-like compounds, non-drug-like compounds, and natural compounds from traditional Chinese medicines.

Project description:<h4>Unlabelled</h4><h4>Background</h4>In order to better understand the structural features of natural compounds from traditional Chinese medicines, the scaffold architectures of drug-like compounds in MACCS-II Drug Data Report (MDDR), non-drug-like compounds in Available Chemical Directory (ACD), and natural compounds in Traditional Chinese Medicine Compound Database (TCMCD) were explored and compared.<h4>Results</h4>First, the different scaffolds were extracted from ACD, MDDR and TCMCD by using three scaffold representations, including Murcko frameworks, Scaffold Tree, and ring systems with different complexity and side chains. Then, by examining the accumulative frequency of the scaffolds in each dataset, we observed that the Level 1 scaffolds of the Scaffold Tree offer advantages over the other scaffold architectures to represent the scaffold diversity of the compound libraries. By comparing the similarity of the scaffold architectures presented in MDDR, ACD and TCMCD, structural overlaps were observed not only between MDDR and TCMCD but also between MDDR and ACD. Finally, Tree Maps were used to cluster the Level 1 scaffolds of the Scaffold Tree and visualize the scaffold space of the three datasets.<h4>Conclusion</h4>The analysis of the scaffold architectures of MDDR, ACD and TCMCD shows that, on average, drug-like molecules in MDDR have the highest diversity while natural compounds in TCMCD have the highest complexity. According to the Tree Maps, it can be observed that the Level 1 scaffolds present in MDDR have higher diversity than those presented in TCMCD and ACD. However, some representative scaffolds in MDDR with high frequency show structural similarities to those in TCMCD and ACD, suggesting that some scaffolds in TCMCD and ACD may be potentially drug-like fragments for fragment-based and de novo drug design.

| S-EPMC3561156 | BioStudies

Finding the molecular scaffold of nuclear receptor inhibitors through high-throughput screening based on proteochemometric modelling.

Project description:Nuclear receptors (NR) are a class of proteins that are responsible for sensing steroid and thyroid hormones and certain other molecules. In that case, NR have the ability to regulate the expression of specific genes and associated with various diseases, which make it essential drug targets. Approaches which can predict the inhibition ability of compounds for different NR target should be particularly helpful for drug development. In this study, proteochemometric modelling was introduced to analysis the bioactivity between chemical compounds and NR targets. Results illustrated the ability of our PCM model for high-throughput NR-inhibitor screening after evaluated on both internal (AUC?>?0.870) and external (AUC?>?0.746) validation set. Moreover, in-silico predicted bioactive compounds were clustered according to structure similarity and a series of representative molecular scaffolds can be derived for five major NR targets. Through scaffolds analysis, those essential bioactive scaffolds of different NR target can be detected and compared. Generally, the methods and molecular scaffolds proposed in this article can not only help the screening of potential therapeutic NR-inhibitors but also able to guide the future NR-related drug discovery.

2018-01-01 | S-EPMC5897275 | BioStudies

How polypharmacologic is each chemogenomics library?

Project description:Aim:High-throughput phenotypic screens have emerged as a promising avenue for small-molecule drug discovery. The challenge faced in high-throughput phenotypic screens is target deconvolution once a small molecule hit is identified. Chemogenomics libraries have emerged as an important tool for meeting this challenge. Here, we investigate their target-specificity by deriving a 'polypharmacology index' for broad chemogenomics screening libraries. Methods:All known targets of all the compounds in each library were plotted as a histogram and fitted to a Boltzmann distribution, whose linearized slope is indicative of the overall polypharmacology of the library. Results & conclusion:Comparison of libraries clearly distinguished the most target-specific library, which might be assumed to be more useful for target deconvolution in a phenotypic screen.

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Spiro[pyrrolidine-3, 3´-oxindole] as potent anti-breast cancer compounds: Their design, synthesis, biological evaluation and cellular target identification.

Project description:The spiro[pyrrolidine-3, 3´-oxindole] moiety is present as a core in number of alkaloids with substantial biological activities. Here in we report design and synthesis of a library of compounds bearing spiro[pyrrolidine-3, 3´-oxindole] motifs that demonstrated exceptional inhibitory activity against the proliferation of MCF-7 breast cancer cells. The synthesis involved a one pot Pictet Spengler-Oxidative ring contraction of tryptamine to the desired scaffolds and occurred in 1:1 THF and water with catalytic trifluoroacetic acid and stoichiometric N-bromosuccinimide as an oxidant. Phenotypic profiling indicated that these molecules induce apoptotic cell death in MCF-7 cells. Target deconvolution with most potent compound 5l from the library, using chemical proteomics indicated histone deacetylase 2 (HDAC2) and prohibitin 2 as the potential cellular binding partners. Molecular docking of 5l with HDAC2 provided insights pertinent to putative binding interactions.

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Pharmacological use of a novel scaffold, anomeric N,N-diarylamino tetrahydropyran: molecular similarity search, chemocentric target profiling, and experimental evidence.

Project description:Rational drug design against a determined target (disease, pathway, or protein) is the main strategy in drug discovery. However, regardless of the main strategy, chemists really wonder how to maximize the utility of their new compounds by drug repositioning them as clinical drug candidates in drug discovery. In this study, we started our drug discovery "from curiosity in the chemical structure of a drug scaffold itself" rather than "for a specific target". As a new drug scaffold, anomeric diarylamino cyclic aminal scaffold 1, was designed by combining two known drug scaffolds (diphenylamine and the most popular cyclic ether, tetrahydropyran/tetrahydrofuran) and synthesized through conventional Brønsted acid catalysis and metal-free ?-C(sp3)-H functionalized oxidative cyclization. To identify the utility of the new scaffold 1, it was investigated through 2D and 3D similarity screening and chemocentric target prediction. The predicted proteins were investigated by an experimental assay. The scaffold 1 was reported to have an antineuroinflammatory agent to reduce NO production, and compound 10 concentration-dependently regulated the expression level of IL-6, PGE-2, TNF-?, ER-?, VDR, CTSD, and iNOS, thus exhibiting neuroprotective activity.

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