ScaffComb: A Phenotype-Based Framework for Drug Combination Virtual Screening in Large-Scale Chemical Datasets.
Ontology highlight
ABSTRACT:
SUBMITTER: Ye Z
PROVIDER: S-EPMC8693048 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Virtual screening is receiving renewed attention in drug discovery, but progress is hampered by challenges on two fronts: handling the ever-increasing sizes of libraries of drug-like compounds and separating true positives from false positives. Here, we developed a machine learning-enabled pipeline for large-scale virtual screening that promises breakthroughs on both fronts. By clustering compounds according to molecular properties and limited docking against a drug target, the full library was trimmed by 10-fold; the remaining compounds were then screened individually by docking; and finally, a dense neural network was trained to classify the hits into true and false positives. As illustration, we screened for inhibitors against RPN11, the deubiquitinase subunit of the proteasome, and a drug target for breast cancer.
| S-EPMC8478848 | biostudies-literature
Project description:BackgroundDrug combinations have the potential to improve efficacy while limiting toxicity. To robustly identify synergistic combinations, high-throughput screens using full dose-response surface are desirable but require an impractical number of data points. Screening of a sparse number of doses per drug allows to screen large numbers of drug pairs, but complicates statistical assessment of synergy. Furthermore, since the number of pairwise combinations grows with the square of the number of drugs, exploration of large screens necessitates advanced visualization tools.ResultsWe describe a statistical and visualization framework for the analysis of large-scale drug combination screens. We developed an approach suitable for datasets with large number of drugs pairs even if small number of data points are available per drug pair. We demonstrate our approach using a systematic screen of all possible pairs among 108 cancer drugs applied to melanoma cell lines. In this dataset only two dose-response data points per drug pair and two data points per single drug test were available. We used a Bliss-based linear model, effectively borrowing data from the drug pairs to obtain robust estimations of the singlet viabilities, consequently yielding better estimates of drug synergy. Our method improves data consistency across dosing thus likely reducing the number of false positives. The approach allows to compute p values accounting for standard errors of the modeled singlets and combination viabilities. We further develop a synergy specificity score that distinguishes specific synergies from those arising with promiscuous drugs. Finally, we developed a summarized interactive visualization in a web application, providing efficient access to any of the 439,000 data points in the combination matrix ( http://www.cmtlab.org:3000/combo_app.html ). The code of the analysis and the web application is available at https://github.com/arnaudmgh/synergy-screen .ConclusionsWe show that statistical modeling of single drug response from drug combination data can help determine significance of synergy and antagonism in drug combination screens with few data point per drug pair. We provide a web application for the rapid exploration of large combinatorial drug screen. All codes are available to the community, as a resource for further analysis of published data and for analysis of other drug screens.
| S-EPMC6378741 | biostudies-literature
Project description:Accurate identification of compound-protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and BindingDB, as well as of the known drug-target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 receptor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI.
| S-EPMC7056933 | biostudies-literature
Project description:With the ever-increasing number of synthesis-on-demand compounds for drug lead discovery, there is a great need for efficient search technologies. We present the successful application of a virtual screening method that combines two advances: (1) it avoids full library enumeration (2) products are evaluated by molecular docking, leveraging protein structural information. Crucially, these advances enable a structure-based technique that can efficiently explore libraries with billions of molecules and beyond. We apply this method to identify inhibitors of ROCK1 from almost one billion commercially available compounds. Out of 69 purchased compounds, 27 (39%) have Ki values < 10 µM. X-ray structures of two leads confirm their docked poses. This approach to docking scales roughly with the number of reagents that span a chemical space and is therefore multiple orders of magnitude faster than traditional docking.
| S-EPMC9616902 | biostudies-literature
Project description:The large, and increasing, number of chemical compounds poses challenges to the exploration of such datasets. In this work, we propose the usage of recommender systems to identify compounds of interest to scientific researchers. Our approach consists of a hybrid recommender model suitable for implicit feedback datasets and focused on retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares and Bayesian Personalized Ranking) and a new content-based algorithm, using the semantic similarity between the chemical compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of chemical compounds, CheRM-20, with more than 16.000 items (chemical compounds). The hybrid model was able to improve the results of the collaborative-filtering algorithms, by more than ten percentage points in most of the assessed evaluation metrics.
| S-EPMC7903631 | biostudies-literature
Project description:BackgroundMultilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states.MethodsThe framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification.ResultsWe illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes.ConclusionsThis framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.
| S-EPMC5975674 | biostudies-literature
Project description:Anti-cancer drug development involves enormous expenditure and risk. For rapid and economical identification of novel, bioavailable anti-tumour chemicals, the use of appropriate in vivo tumour models suitable for large-scale screening is key. Using a Drosophila Ras-driven tumour model, we demonstrate that tumour overgrowth can be curtailed by feeding larvae with chemicals that have the in vivo pharmacokinetics essential for drug development and known efficacy against human tumour cells. We then develop an in vivo 96-well plate chemical screening platform to carry out large-scale chemical screening with the tumour model. In a proof-of-principle pilot screen of 2000 compounds, we identify the glutamine analogue, acivicin, a chemical with known activity against human tumour cells, as a potent and specific inhibitor of Drosophila tumour formation. RNAi-mediated knockdown of candidate acivicin target genes implicates an enzyme involved in pyrimidine biosynthesis, CTP synthase, as a possible crucial target of acivicin-mediated inhibition. Thus, the pilot screen has revealed that Drosophila tumours are glutamine-dependent, which is an emerging feature of many human cancers, and has validated the platform as a powerful and economical tool for in vivo chemical screening. The platform can also be adapted for use with other disease models, thus offering widespread applications in drug development.
| S-EPMC3597034 | biostudies-literature
Project description:BackgroundThere are presently hundreds of online databases hosting millions of chemical compounds and associated data. As a result of the number of cheminformatics software tools that can be used to produce the data, subtle differences between the various cheminformatics platforms, as well as the naivety of the software users, there are a myriad of issues that can exist with chemical structure representations online. In order to help facilitate validation and standardization of chemical structure datasets from various sources we have delivered a freely available internet-based platform to the community for the processing of chemical compound datasets.ResultsThe chemical validation and standardization platform (CVSP) both validates and standardizes chemical structure representations according to sets of systematic rules. The chemical validation algorithms detect issues with submitted molecular representations using pre-defined or user-defined dictionary-based molecular patterns that are chemically suspicious or potentially requiring manual review. Each identified issue is assigned one of three levels of severity - Information, Warning, and Error - in order to conveniently inform the user of the need to browse and review subsets of their data. The validation process includes validation of atoms and bonds (e.g., making aware of query atoms and bonds), valences, and stereo. The standard form of submission of collections of data, the SDF file, allows the user to map the data fields to predefined CVSP fields for the purpose of cross-validating associated SMILES and InChIs with the connection tables contained within the SDF file. This platform has been applied to the analysis of a large number of data sets prepared for deposition to our ChemSpider database and in preparation of data for the Open PHACTS project. In this work we review the results of the automated validation of the DrugBank dataset, a popular drug and drug target database utilized by the community, and ChEMBL 17 data set. CVSP web site is located at http://cvsp.chemspider.com/.ConclusionA platform for the validation and standardization of chemical structure representations of various formats has been developed and made available to the community to assist and encourage the processing of chemical structure files to produce more homogeneous compound representations for exchange and interchange between online databases. While the CVSP platform is designed with flexibility inherent to the rules that can be used for processing the data we have produced a recommended rule set based on our own experiences with the large data sets such as DrugBank, ChEMBL, and data sets from ChemSpider.
| S-EPMC4494041 | biostudies-literature
Project description:warpDOCK is an open-source pipeline for virtual small-molecule drug discovery using cloud infrastructure. warpDOCK is designed from the ground up for the Oracle Cloud Infrastructure (OCI), enabling harmonious parallelism of docking calculations over thousands to hundreds of thousands of cores. This enables cost-effective sampling of ultra-large chemical libraries, potentially reducing the time to identify lead drug candidates by orders of magnitude. By utilizing established docking software and automating each step of the process, warpDOCK makes large-scale virtual screening accessible to a broad user group. The warpDOCK code can be found at the BruningLab GitHub repository (https://github.com/BruningLab/warpDOCK).
| S-EPMC10433467 | biostudies-literature
Project description:Large-scale virtual screening of boronic acid derivatives was performed to identify nonpeptidic covalent inhibitors of the β5i subunit of the immunoproteasome. A hierarchical virtual screening cascade including noncovalent and covalent docking steps was applied to a virtual library of over 104,000 compounds. Then, 32 virtual hits were selected, out of which five were experimentally confirmed. Biophysical and biochemical tests showed micromolar binding affinity and time-dependent inhibitory potency for two compounds. These results validate the computational protocol that allows the screening of large compound collections. One of the lead-like boronic acid derivatives identified as a covalent immunoproteasome inhibitor is a suitable starting point for chemical optimization.
| S-EPMC6680723 | biostudies-literature