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A deep learning framework for high-throughput mechanism-driven phenotype compound screening [article]

Thai-Hoang Pham, Yue T Qiu, Jucheng Zeng, Lei Xie, Ping Zhang
2020 biorxiv/medrxiv  
attention mechanism to model chemical substructure-gene and gene-gene feature associations.  ...  prediction of chemical-induced gene expression.  ...  and gene-chemical substructure feature associations and GCN for generating neural fingerprints.  ... 
doi:10.1101/2020.07.19.211235 pmid:32743586 pmcid:PMC7386506 fatcat:cwfqrhiq6rc3lfkkpghicylfu4

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

Yunyi Wu, Guanyu Wang
2018 International Journal of Molecular Sciences  
We also discuss the input parameter to the machine learning algorithm, especially its shift from chemical structural description only to that combined with human transcriptome data analysis, which can  ...  , image recognition, computational chemistry, and bioinformatics, with excellent performance.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijms19082358 pmid:30103448 fatcat:mjgeejthrzex7kbyxgncnncgla

Identification of structural features in chemicals associated with cancer drug response: A systematic data-driven analysis [article]

Suleiman A Khan, Seppo Virtanen, Olli P Kallioniemi, Krister Wennerberg, Antti Poso, Samuel Kaski
2014 arXiv   pre-print
We identify 11 compo-nents that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines, and identify structural groups that may be responsible  ...  Results: In this paper, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on ge-nome-wide gene expression across several cancer cell lines (CMap database  ...  The Pentacle feature (N2 distance range) found by GFA as related with HSP gene expression is represented with the yellow line.  ... 
arXiv:1312.7734v2 fatcat:p2fwb325lfbvzie2t2fdser7zy

Estimation of relationships between chemical substructures and antibiotic resistance-related gene expression in bacteria: Adapting a canonical correlation analysis for small sample data of gathered features using consensus clustering

Tsuyoshi Esaki, Takaaki Horinouchi, Yayoi Natsume-Kitatani, Yosui Nojima, Iwao Sakane, Hidetoshi Mastsui
2020 Chem-Bio Informatics Journal  
The CCA was performed using the clustered features, and it revealed relationships between the features of chemical substructures and the expression level of genes related to several types of antibiotic  ...  It is important to analyze the relationships between phenotypic changes and compound structures; hence, we performed a canonical correlation analysis (CCA) for high dimensional phenotypic and compound  ...  In this study, we prepared a set of 10 compounds with five clustered substructures and gene expression features.  ... 
doi:10.1273/cbij.20.58 fatcat:qlbpwypnjrhkdcqaavvoa2rwzi

Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity

Laura-Jayne Gardiner, Anna Paola Carrieri, Jenny Wilshaw, Stephen Checkley, Edward O Pyzer-Knapp, Ritesh Krishna
2020 Scientific Reports  
To achieve this, we use inexpensive transcriptomic profiles derived from human cell lines after chemical compound treatment to train our models combined with compound chemical structure information.  ...  We combine the use of insight into the feature-wise contributions to our predictions with the use of predictive uncertainties recovered from the Gaussian Process to improve the transparency and trustworthiness  ...  encoded the chemical fingerprint for the chemical compound that was used to generate the gene expression profile.  ... 
doi:10.1038/s41598-020-66481-0 pmid:32533004 pmcid:PMC7293302 fatcat:67fnp5lozjgotcc2hsbjhgpvym

The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug–drug interactions

Santiago Vilar, George Hripcsak
2016 Briefings in Bioinformatics  
We highlight profiles constructed with different biological data, such as target-protein interactions, gene expression measurements, adverse effects and disease profiles.  ...  Explosion of the availability of big data sources along with the development in computational methods provides a useful framework to study drugs' actions, such as interactions with pharmacological targets  ...  Molecular structure fingerprints Molecular structure fingerprints have been widely used to compare chemical similarity and discover new chemical entities with diverse biological functions.  ... 
doi:10.1093/bib/bbw048 pmid:27273288 pmcid:PMC6078166 fatcat:yeoxzrbacnbuplvfppmazr6ziq

In silico target fishing: Predicting biological targets from chemical structure

Jeremy L. Jenkins, Andreas Bender, John W. Davies
2006 Drug Discovery Today : Technologies  
In silico target fishing is an emerging technology that enables the prediction of biological targets of compounds on the basis of chemical structure by using information from increasingly available biologically  ...  ] with 750K records.  ...  Acknowledgements We thank Meir Glick, Jim Nettles, Zhan Deng, Nidhi, Thomas Crisman, Ansgar Schuffenhauer, John Peter Priestle, Kamal Azzaoui, Edgar Jacoby, John Tallarico, Bernd Rohde, Dmitri Mikhailov of  ... 
doi:10.1016/j.ddtec.2006.12.008 fatcat:7p6uljq43balfmxczca7eb7joi

An integrative analysis of small molecule transcriptional responses in the human malaria parasite Plasmodium falciparum

Geoffrey H. Siwo, Roger S. Smith, Asako Tan, Katrina A. Button-Simons, Lisa A. Checkley, Michael T. Ferdig
2015 BMC Genomics  
We investigated the impact of chemical composition and MOA on gene expression similarities that arise between perturbations by various compounds.  ...  Results: We find that small molecules associated with similar transcriptional responses contain similar chemical features, and/ or have a shared MOA.  ...  The Dana-Farber Cancer Institute provided the experimental compound JQ1 and Dr. Kiplin Guy at Saint Jude's children's hospital provided the SJ series of experimental compounds.  ... 
doi:10.1186/s12864-015-2165-1 pmid:26637195 pmcid:PMC4670519 fatcat:7ecsfojejfbqda4rorjok46bvm

Using Molecular Features of Xenobiotics to Predict Hepatic Gene Expression Response

Guy Haskin Fernald, Russ B. Altman
2013 Journal of Chemical Information and Modeling  
Therefore, we investigated the extent to which chemical features of small molecules can reliably be associated with significant changes in gene expression.  ...  However, our understanding of how the chemical features of small molecules influence gene expression is very limited.  ...  Gene transcripts as shown as circles, where each circle is drawn in the color (or colors) associated with the chemical features associated with its expression.  ... 
doi:10.1021/ci3005868 pmid:24010729 pmcid:PMC3810861 fatcat:p7r6kjiarneshesfzackpt7mim

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

Hyunho Kim, Eunyoung Kim, Ingoo Lee, Bongsung Bae, Minsu Park, Hojung Nam
2020 Biotechnology and Bioprocess Engineering  
In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.  ...  Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying  ...  (NRF-2017M3A9C 4092978) of the Ministry of Science, ICT.  ... 
doi:10.1007/s12257-020-0049-y pmid:33437151 pmcid:PMC7790479 fatcat:wqdmkkas2nb65gy3pymlgisuwi

Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis

Suleiman A. Khan, Seppo Virtanen, Olli P. Kallioniemi, Krister Wennerberg, Antti Poso, Samuel Kaski
2014 Computer applications in the biosciences : CABIOS  
We identify 11 components that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines and identify structural groups that may be responsible for  ...  Results: In this article, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on genomewide gene expression across several cancer cell lines [Connectivity  ...  They are able to group together compounds with dissimilar chemical structures and yet having the same type of molecular field properties.  ... 
doi:10.1093/bioinformatics/btu456 pmid:25161239 pmcid:PMC4147909 fatcat:hzkeopgv75dujdxjqfykdmyjzu

Machine Learning Uses Chemo-Transcriptomic Profiles to Stratify Antimalarial Compounds With Similar Mode of Action

Ashleigh van Heerden, Roelof van Wyk, Lyn-Marie Birkholtz
2021 Frontiers in Cellular and Infection Microbiology  
We developed a rational gene selection approach that could identify predictive features for MoA to train and generate ML models.  ...  The ML model was specific and sensitive enough to group new compounds into MoAs associated with their predicted target and was robust enough to be extended to also generate chemo-transcriptomic fingerprints  ...  All authors contributed to the paper and approved the submitted version of the paper.  ... 
doi:10.3389/fcimb.2021.688256 pmid:34268139 pmcid:PMC8277430 fatcat:zt72r36cq5etrdzktxons6rfba

Connecting synthetic chemistry decisions to cell and genome biology using small-molecule phenotypic profiling

Bridget K Wagner, Paul A Clemons
2009 Current Opinion in Chemical Biology  
Discovering small-molecule modulators for thousands of gene products requires multiple stages of biological testing, specificity evaluation, and chemical optimization.  ...  Many cellular profiling methods, including cellular sensitivity, gene expression, and cellular imaging, have emerged as methods to assess the functional consequences of biological perturbations.  ...  Some modern chemical fingerprints (including the ECFPs used by Young et al. [32] ) permit precise association of substructure features with individual "bits" in fingerprints.  ... 
doi:10.1016/j.cbpa.2009.09.018 pmid:19825513 pmcid:PMC2787914 fatcat:vokiiaf4trbmzg46hxro32lpdq

Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets

Benoît Baillif, Joerg Wichard, Oscar Méndez-Lucio, David Rouquié
2020 Frontiers in Chemistry  
Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks.  ...  Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures.  ...  GES, model using gene expression signature. Morgan FP, model using chemical fingerprints from counterpart GES model dataset.  ... 
doi:10.3389/fchem.2020.00296 pmid:32391323 pmcid:PMC7191531 fatcat:66la6joaxbcybbkmv4wahtpdxe

ChemProt [chapter]

Olivier Taboureau, Tudor Oprea
2013 Computational Chemogenomics  
Results from the disease chemical biology database associate citalopram, an antidepressant, with osteogenesis imperfect and leukemia and bisphenol A, an endocrine disruptor, with certain types of cancer  ...  ChemProt can assist in the in silico evaluation of environmental chemicals, natural products and approved drugs, as well as the selection of new compounds based on their activity profile against most known  ...  Descriptors and similarity measurement The chemical structure of the molecules was encoded using two rather different types of fingerprints.  ... 
doi:10.1201/b15631-8 fatcat:nx2zmj2kgndezjbpjr6fdubzxy
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