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Virginia Commonwealth University
1.
Wang, Chen.
High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome.
Degree: PhD, Computer Science, 2018, Virginia Commonwealth University
URL: https://doi.org/10.25772/E2X0-B462 
;
https://scholarscompass.vcu.edu/etd/5509
► Drugs exert their (therapeutic) effects via molecular-level interactions with proteins and other biomolecules. Computational prediction of drug-protein interactions plays a significant role in the…
(more)
▼ Drugs exert their (therapeutic) effects via molecular-level interactions with proteins and other biomolecules. Computational
prediction of
drug-protein interactions plays a significant role in the effort to improve our current and limited knowledge of these interactions. The use of the putative
drug-protein interactions could facilitate the discovery of novel applications of drugs, assist in cataloging their targets, and help to explain the details of medicinal efficacy and side-effects of drugs. We investigate current studies related to the computational
prediction of
drug-protein interactions and categorize them into protein structure-based and similarity-based methods. We evaluate three representative structure-based predictors and develop a Protein-
Drug Interaction Database (PDID) that includes the putative
drug targets generated by these three methods for the entire structural human proteome. To address the fact that only a limited set of proteins has known structures, we study the similarity-based methods that do not require this information. We review a comprehensive set of 35 high-impact similarity-based predictors and develop a novel, high-quality benchmark database. We group these predictors based on three types of similarities and their combinations that they use. We discuss and compare key architectural aspects of these methods including their source databases, internal databases and predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually or in all possible combinations. We assess predictive quality at the database-wide
drug-protein interaction level and we are the first to also include evaluation across individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures AUC of 0.93. We offer a first-of-its-kind analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the
drug targets and several extrinsic properties of the input
drug structures,
drug profiles and
drug targets.
Advisors/Committee Members: Lukasz Kurgan.
Subjects/Keywords: drug-protein interactions; computational prediction; drug target database; drug repurposing; drug side-effects; Bioinformatics
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APA (6th Edition):
Wang, C. (2018). High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome. (Doctoral Dissertation). Virginia Commonwealth University. Retrieved from https://doi.org/10.25772/E2X0-B462 ; https://scholarscompass.vcu.edu/etd/5509
Chicago Manual of Style (16th Edition):
Wang, Chen. “High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome.” 2018. Doctoral Dissertation, Virginia Commonwealth University. Accessed April 23, 2021.
https://doi.org/10.25772/E2X0-B462 ; https://scholarscompass.vcu.edu/etd/5509.
MLA Handbook (7th Edition):
Wang, Chen. “High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome.” 2018. Web. 23 Apr 2021.
Vancouver:
Wang C. High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome. [Internet] [Doctoral dissertation]. Virginia Commonwealth University; 2018. [cited 2021 Apr 23].
Available from: https://doi.org/10.25772/E2X0-B462 ; https://scholarscompass.vcu.edu/etd/5509.
Council of Science Editors:
Wang C. High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome. [Doctoral Dissertation]. Virginia Commonwealth University; 2018. Available from: https://doi.org/10.25772/E2X0-B462 ; https://scholarscompass.vcu.edu/etd/5509
University of Melbourne
2.
Hameed, Pathima Nusrath.
In silico methods for drug repositioning and drug-drug interaction prediction.
Degree: 2018, University of Melbourne
URL: http://hdl.handle.net/11343/219484
► Drug repositioning and drug-drug interaction (DDI) prediction are two fundamental applications having a large impact on drug development and clinical care. Drug repositioning aims to…
(more)
▼ Drug repositioning and drug-drug interaction (DDI) prediction are two fundamental applications having a large impact on drug development and clinical care. Drug repositioning aims to identify new uses for existing drugs. Moreover, understanding harmful DDIs is essential to enhance the effects of clinical care. Exploring both therapeutic uses and adverse effects of drugs or a pair of drugs have significant benefits in pharmacology. The use of computational methods to support drug repositioning and DDI prediction enable improvements in the speed of drug development compared to in vivo and in vitro methods.
This thesis investigates the consequences of employing a representative training sample in achieving better performance for DDI classification. The Positive-Unlabeled Learning method introduced in this thesis aims to employ representative positives as well as reliable negatives to train the binary classifier for inferring potential DDIs. Moreover, it explores the importance of a finer-grained similarity metric to represent the pairwise drug similarities.
Drug repositioning can be approached by new indication detection. In this study, Anatomical Therapeutic Chemical (ATC) classification is used as the primary source to determine the indications/therapeutic uses of drugs for drug repositioning. This thesis presents a two-tiered clustering approach for obtaining pairwise drug similarity and heterogeneous drug data integration which is employed for large-scale drug repositioning.
Moreover, this thesis demonstrates subnetwork identification as a suitable approach for new indication detection for existing drugs. Subnetwork identification method identifies a subgraph from a large drug similarity network, connecting a set of given drugs known as ‘terminals’. In this study, the ‘terminals’ are selected according to the ATC classification system; hence meaningful subnetworks are identified. The proposed subnetwork identification method is employed to infer drug repositioning candidates for cardiovascular diseases and diseases related to the nervous system.
New target detection for existing drugs is also beneficial for drug repositioning. This thesis proposes a useful computational method for target clustering which is extended to identify new drug-target relationships. It demonstrates the significance of integrating dimensionality reduction and outlier detection to overcome the limitations arising from the incomplete drug-target interaction data.
The clinical significance and literature-based evidence illustrate the relevance of the proposed methods. The proposed methods can be employed in other similar applications where applicable.
Subjects/Keywords: drug repurposing; biomedical informatics; DDI prediction; drug-target prediction; heterogeneous data integration; cluster evaluation
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APA (6th Edition):
Hameed, P. N. (2018). In silico methods for drug repositioning and drug-drug interaction prediction. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/219484
Chicago Manual of Style (16th Edition):
Hameed, Pathima Nusrath. “In silico methods for drug repositioning and drug-drug interaction prediction.” 2018. Doctoral Dissertation, University of Melbourne. Accessed April 23, 2021.
http://hdl.handle.net/11343/219484.
MLA Handbook (7th Edition):
Hameed, Pathima Nusrath. “In silico methods for drug repositioning and drug-drug interaction prediction.” 2018. Web. 23 Apr 2021.
Vancouver:
Hameed PN. In silico methods for drug repositioning and drug-drug interaction prediction. [Internet] [Doctoral dissertation]. University of Melbourne; 2018. [cited 2021 Apr 23].
Available from: http://hdl.handle.net/11343/219484.
Council of Science Editors:
Hameed PN. In silico methods for drug repositioning and drug-drug interaction prediction. [Doctoral Dissertation]. University of Melbourne; 2018. Available from: http://hdl.handle.net/11343/219484
University of California – Santa Cruz
3.
Woehrmann, Marcos H.
Predicting the Mode of Action of Bioactive Compounds via High Throughput Screening and Computational Algorithms.
Degree: Biomolecular Engineering and Bioinformatics, 2015, University of California – Santa Cruz
URL: http://www.escholarship.org/uc/item/9976p4ch
► To develop more effective therapies to treat human diseases, a better method of finding the biological targets and modes of action of new compounds is…
(more)
▼ To develop more effective therapies to treat human diseases, a better method of finding the biological targets and modes of action of new compounds is needed. Target predictions have traditionally been made by comparing a new compound's molecular structure to that of known compounds. In many cases this method does not accurately predict a chemical's function since "small chemical changes in an active molecule can render it either nearly or completely inactive or increase its activity dramatically" (Eckert and Bajorath, 2007). Further, prediction by structural comparison has limited application; it can only be used on chemicals with established structures and only identifies new compounds that are structurally similar to known compounds.A majority of existing drugs have been discovered by identifying the active ingredient of traditional medicines. More recent techniques of drug discovery screen a library of compounds for effectiveness in treating a single disease. However, this method requires re-screening the library when searching for treatments for other diseases; a critical barrier to expediting and scaling drug discovery. Screening efficiency is particularly important since advances in robotic chemical synthesis and the search for natural products from the oceans are rapidly increasing the size of drug candidate libraries.In contrast to current approaches which screen compounds for treatments for single disease; my research focused on creating screening methods that deliver a library of chemical fingerprints which can be used to find potential drug candidates for a multiplicity of diseases.My work produced three screening methods that generate fingerprints useful for predicting a compound's mode of action: cytological profiling, D- Map, and BioSpace. All of these showed positive results towards solving the screening bottleneck. Finally, combining these approaches to integrate these various fingerprints could increase prediction accuracy of screening methods.
Subjects/Keywords: Bioinformatics; Bioinformatics; Cytological Profiling; Drug Target Prediction; High-throughput Screening; HTS
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APA ·
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MLA ·
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Manager
APA (6th Edition):
Woehrmann, M. H. (2015). Predicting the Mode of Action of Bioactive Compounds via High Throughput Screening and Computational Algorithms. (Thesis). University of California – Santa Cruz. Retrieved from http://www.escholarship.org/uc/item/9976p4ch
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Woehrmann, Marcos H. “Predicting the Mode of Action of Bioactive Compounds via High Throughput Screening and Computational Algorithms.” 2015. Thesis, University of California – Santa Cruz. Accessed April 23, 2021.
http://www.escholarship.org/uc/item/9976p4ch.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Woehrmann, Marcos H. “Predicting the Mode of Action of Bioactive Compounds via High Throughput Screening and Computational Algorithms.” 2015. Web. 23 Apr 2021.
Vancouver:
Woehrmann MH. Predicting the Mode of Action of Bioactive Compounds via High Throughput Screening and Computational Algorithms. [Internet] [Thesis]. University of California – Santa Cruz; 2015. [cited 2021 Apr 23].
Available from: http://www.escholarship.org/uc/item/9976p4ch.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Woehrmann MH. Predicting the Mode of Action of Bioactive Compounds via High Throughput Screening and Computational Algorithms. [Thesis]. University of California – Santa Cruz; 2015. Available from: http://www.escholarship.org/uc/item/9976p4ch
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Indiana University
4.
Seal, Abhik.
RANDOM WALK APPLIED TO HETEROGENOUS DRUG-TARGET NETWORKS FOR PREDICTING BIOLOGICAL OUTCOMES
.
Degree: 2016, Indiana University
URL: http://hdl.handle.net/2022/20765
► Prediction of unknown drug target interactions from bioassay data is critical not only for the understanding of various interactions but also crucial for the development…
(more)
▼ Prediction of unknown
drug target interactions from bioassay data is critical not only for the understanding of various interactions but also crucial for the development of new drugs and repurposing of old ones. Conventional methods for
prediction of such interactions can be divided into 2D based and 3D based methods. 3D methods are more CPU expensive and require more manual interpretation whereas 2D methods are actually fast methods like machine learning and similarity search which use chemical fingerprints. One of the problems of using traditional machine learning based method to predict
drug-
target pairs is that it requires a labeled information of true and false interactions. One of the major problems of supervised learning methods is selection on negative samples. Unknown
drug target interactions are regarded as false interactions, which may influence the predictive accuracy of the model. To overcome this problem network based methods has become an effective tool in predicting the
drug target interactions overcoming the negative sampling problem. In this dissertation study, I will describe traditional machine learning methods and 3D methods of pharmacophore modeling for
drug target prediction and will show how these methods work in a
drug discovery scenario. I will then introduce a new framework for
drug target prediction based on bipartite networks of
drug target relations known as Random Walk with Restart (RWR). RWR integrates various networks including drug–
drug similarity networks, protein-protein similarity networks and
drug-
target interaction networks into a heterogeneous network that is capable of predicting novel
drug-
target relations. I will describe how chemical features for measuring
drug-drug similarity do not affect performance in predicting interactions and further show the performance of RWR using an external dataset from ChEMBL database. I will describe about further implementations of RWR approach into multilayered networks consisting of biological data like diseases, tissue based gene expression data, protein- complexes and metabolic pathways to predict associations between human diseases and metabolic pathways which are very crucial in
drug discovery. I have further developed a software tool package netpredictor in R (standalone and the web) for unipartite and bipartite networks and implemented network-based predictive algorithms and network properties for
drug-
target prediction. This package will be described.
Advisors/Committee Members: Wild, David J (advisor).
Subjects/Keywords: Random walk;
drug target;
link prediction;
disease;
metabolic pathway;
R;
Shiny
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APA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Seal, A. (2016). RANDOM WALK APPLIED TO HETEROGENOUS DRUG-TARGET NETWORKS FOR PREDICTING BIOLOGICAL OUTCOMES
. (Thesis). Indiana University. Retrieved from http://hdl.handle.net/2022/20765
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Seal, Abhik. “RANDOM WALK APPLIED TO HETEROGENOUS DRUG-TARGET NETWORKS FOR PREDICTING BIOLOGICAL OUTCOMES
.” 2016. Thesis, Indiana University. Accessed April 23, 2021.
http://hdl.handle.net/2022/20765.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Seal, Abhik. “RANDOM WALK APPLIED TO HETEROGENOUS DRUG-TARGET NETWORKS FOR PREDICTING BIOLOGICAL OUTCOMES
.” 2016. Web. 23 Apr 2021.
Vancouver:
Seal A. RANDOM WALK APPLIED TO HETEROGENOUS DRUG-TARGET NETWORKS FOR PREDICTING BIOLOGICAL OUTCOMES
. [Internet] [Thesis]. Indiana University; 2016. [cited 2021 Apr 23].
Available from: http://hdl.handle.net/2022/20765.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Seal A. RANDOM WALK APPLIED TO HETEROGENOUS DRUG-TARGET NETWORKS FOR PREDICTING BIOLOGICAL OUTCOMES
. [Thesis]. Indiana University; 2016. Available from: http://hdl.handle.net/2022/20765
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
King Abdullah University of Science and Technology
5.
Olayan, Rawan S.
Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches.
Degree: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 2017, King Abdullah University of Science and Technology
URL: http://hdl.handle.net/10754/626424
► Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of…
(more)
▼ Computational
drug repurposing aims at finding new medical uses for
existing drugs. The identification of novel
drug-
target interactions (DTIs) can be a useful part of such a task. Computational determination of DTIs is a convenient
strategy for systematic screening of a large number of drugs in the attempt to
identify new DTIs at low cost and with reasonable accuracy. This necessitates
development of accurate computational methods that can help focus on the
follow-up experimental validation on a smaller number of highly likely targets for
a
drug. Although many methods have been proposed for computational DTI
prediction, they suffer the high false positive
prediction rate or they do not predict the effect that drugs exert on targets in DTIs.
In this report, first, we present a comprehensive review of the recent progress in
the field of DTI
prediction from data-centric and algorithm-centric perspectives.
The aim is to provide a comprehensive review of computational methods for
identifying DTIs, which could help in constructing more reliable methods. Then,
we present DDR, an efficient method to predict the existence of DTIs. DDR
achieves significantly more accurate results compared to the other state-of-theart methods. As supported by independent evidences, we verified as correct 22 out of the top 25 DDR DTIs predictions. This validation proves the practical utility of DDR, suggesting that DDR can be used as an efficient method to identify
5 correct DTIs. Finally, we present DDR-FE method that predicts the effect types of a
drug on its
target. On different representative datasets, under various test
setups, and using different performance measures, we show that DDR-FE
achieves extremely good performance. Using blind test data, we verified as
correct 2,300 out of 3,076 DTIs effects predicted by DDR-FE. This suggests that DDR-FE can be used as an efficient method to identify correct effects of a
drug on its
target.
Advisors/Committee Members: Bajic, Vladimir B. (advisor), Moshkov, Mikhail (committee member), Laleg-Kirati, Taous-Meriem (committee member), Christoffels, Alan (committee member).
Subjects/Keywords: drug–target interaction prediction; link prediction; Bioinformatics; chemoinformatics; Machine Learning; graph mining
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APA ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Olayan, R. S. (2017). Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/626424
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Olayan, Rawan S. “Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches.” 2017. Thesis, King Abdullah University of Science and Technology. Accessed April 23, 2021.
http://hdl.handle.net/10754/626424.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Olayan, Rawan S. “Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches.” 2017. Web. 23 Apr 2021.
Vancouver:
Olayan RS. Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2017. [cited 2021 Apr 23].
Available from: http://hdl.handle.net/10754/626424.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Olayan RS. Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches. [Thesis]. King Abdullah University of Science and Technology; 2017. Available from: http://hdl.handle.net/10754/626424
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of California – San Francisco
6.
Yera, Emmanuel Ramon.
Rationalizing Drug Pharmacology based on Computational Methods.
Degree: Biological and Medical Informatics, 2013, University of California – San Francisco
URL: http://www.escholarship.org/uc/item/3mf2z296
► Large-scale experimental determination of the protein targets of small molecules is both time-consuming and costly. Computational methods can be used to predict interactions between small…
(more)
▼ Large-scale experimental determination of the protein targets of small molecules is both time-consuming and costly. Computational methods can be used to predict interactions between small molecule and targets, which can help experimentalists find new therapeutic targets or off-targets responsible for undesired side-effects. A data fusion framework for combining multiple similarity computations and a novel method for drawing relationships between drugs based on their clinical effect was developed. Small molecules may be quantitatively compared based on 2D topological structural considerations, based on 3D characteristics directly related to binding, and based on their clinical effects. Given a new molecule along with a set of molecules sharing some biological effect, a single score based on comparison to the known set is produced, reflecting either 2D similarity, 3D similarity, clinical effects similarity or their combination. The methods were systematically applied to a large set of FDA approved drugs (nearly two-thirds).For prediction of off-target effects, the performance of 3D-similarity over either 2D or clinical effects similarity alone was substantial, and there was added benefit from combining all of the methods. In addition to assessing predictive accuracy of the different similarity methods, the relationship between chemical similarity and pharmacological novelty was studied with regards to protein target modulation and clinical effects. Drug pairs that shared high 3D similarity but low 2D similarity (i.e. having different underlying scaffolds) were shown to be much more likely to exhibit pharmacologically relevant differences in terms of specific target modulation and differences in clinical effects.
Subjects/Keywords: Bioinformatics; drug discovery; drug effects; molecular similarity; natural language processing; target prediction
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APA ·
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Manager
APA (6th Edition):
Yera, E. R. (2013). Rationalizing Drug Pharmacology based on Computational Methods. (Thesis). University of California – San Francisco. Retrieved from http://www.escholarship.org/uc/item/3mf2z296
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Yera, Emmanuel Ramon. “Rationalizing Drug Pharmacology based on Computational Methods.” 2013. Thesis, University of California – San Francisco. Accessed April 23, 2021.
http://www.escholarship.org/uc/item/3mf2z296.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Yera, Emmanuel Ramon. “Rationalizing Drug Pharmacology based on Computational Methods.” 2013. Web. 23 Apr 2021.
Vancouver:
Yera ER. Rationalizing Drug Pharmacology based on Computational Methods. [Internet] [Thesis]. University of California – San Francisco; 2013. [cited 2021 Apr 23].
Available from: http://www.escholarship.org/uc/item/3mf2z296.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Yera ER. Rationalizing Drug Pharmacology based on Computational Methods. [Thesis]. University of California – San Francisco; 2013. Available from: http://www.escholarship.org/uc/item/3mf2z296
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
7.
Zoufir, Azedine.
Computational analyses of small molecules activity from phenotypic screens.
Degree: PhD, 2019, University of Cambridge
URL: https://www.repository.cam.ac.uk/handle/1810/293251
► Drug discovery is no longer relying on the one gene-one disease paradigm nor on target-based screening alone to discover new drugs. Phenotypic-based screening is regaining…
(more)
▼ Drug discovery is no longer relying on the one gene-one disease paradigm nor on target-based screening alone to discover new drugs. Phenotypic-based screening is regaining momentum to discover new compounds since those assays provide an environment closer to the physiological state of the disease and allow to better anticipate off-target effects and other factors that can limit the efficacy of the drugs. However, uncovering the mechanism of action of the compounds active in those assays relies on in vitro techniques that are expensive and time- consuming. In silico approaches are therefore beneficial to prioritise mechanism of action hypotheses to be tested in such systems.
In this thesis, the use of machine learning algorithms for in silico ligand-target prediction for target deconvolution in phenotypic screening datasets was investigated. A computational workflow is presented in Chapter 2, that allows to improve the coverage of mechanism of action hypotheses obtained by combining two conceptually different target prediction algorithms.
These models rely on the principle that two structurally similar compounds are likely to have the same target. In Chapter 3 of this thesis, it was shown that structural similarity and the similarity in phenotypic activity are correlated, and the fraction of phenotypically similar compounds that can be expected for an increase in structural similarity was subsequently quantified. Morgan fingerprints were also found to be less sensitive to the dataset employed in these analyses than two other commonly used molecular descriptors.
In Chapter 4, the mechanism of action hypotheses obtained through target prediction was compared to those obtained by extracting experimental bioactivity data of compounds active in phenotypic assays. It was then showed that the mechanism of action hypotheses generated from these two types of approach agreed where a large number of compounds were active in the phenotypic assay. When there were fewer compounds active in the phenotypic assay, target prediction complemented the use of experimental bioactivity data and allowed to uncover alternative mechanisms of action for compounds active in these assays.
Finally, the in silico target prediction workflow described in Chapter 2 was applied in Chapter 5 to deconvolute the activity of compounds in a kidney cyst growth reduction assay, aimed at discovering novel therapeutic opportunities for polycystic kidney disease. A metric was developed to rank predicted targets according to the activity of the compounds driving their prediction. Gene expression data and occurrences in the literature were combined with the target predictions to further narrow down the most probable mechanisms of action of cyst growth reducing compounds in the screen. Two target predictions were proposed as a potential mechanism for the reduction of kidney cyst growth, one of which agreed with docking studies.
Subjects/Keywords: Drug discovery; Cheminformatics; Chemoinformatics; Phenotypic Screening; Target Prediction; Structural Bioinformatics; Machine Learning; Bayesian Statistics; Self Organising Maps; Polycystic Kidney Disease
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zoufir, A. (2019). Computational analyses of small molecules activity from phenotypic screens. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/293251
Chicago Manual of Style (16th Edition):
Zoufir, Azedine. “Computational analyses of small molecules activity from phenotypic screens.” 2019. Doctoral Dissertation, University of Cambridge. Accessed April 23, 2021.
https://www.repository.cam.ac.uk/handle/1810/293251.
MLA Handbook (7th Edition):
Zoufir, Azedine. “Computational analyses of small molecules activity from phenotypic screens.” 2019. Web. 23 Apr 2021.
Vancouver:
Zoufir A. Computational analyses of small molecules activity from phenotypic screens. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2021 Apr 23].
Available from: https://www.repository.cam.ac.uk/handle/1810/293251.
Council of Science Editors:
Zoufir A. Computational analyses of small molecules activity from phenotypic screens. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/293251
8.
Zoufir, Azedine.
Computational analyses of small molecules activity from phenotypic screens.
Degree: PhD, 2019, University of Cambridge
URL: https://doi.org/10.17863/CAM.40403
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782760
► Drug discovery is no longer relying on the one gene-one disease paradigm nor on target-based screening alone to discover new drugs. Phenotypic-based screening is regaining…
(more)
▼ Drug discovery is no longer relying on the one gene-one disease paradigm nor on target-based screening alone to discover new drugs. Phenotypic-based screening is regaining momentum to discover new compounds since those assays provide an environment closer to the physiological state of the disease and allow to better anticipate off-target effects and other factors that can limit the efficacy of the drugs. However, uncovering the mechanism of action of the compounds active in those assays relies on in vitro techniques that are expensive and time- consuming. In silico approaches are therefore beneficial to prioritise mechanism of action hypotheses to be tested in such systems. In this thesis, the use of machine learning algorithms for in silico ligand-target prediction for target deconvolution in phenotypic screening datasets was investigated. A computational workflow is presented in Chapter 2, that allows to improve the coverage of mechanism of action hypotheses obtained by combining two conceptually different target prediction algorithms. These models rely on the principle that two structurally similar compounds are likely to have the same target. In Chapter 3 of this thesis, it was shown that structural similarity and the similarity in phenotypic activity are correlated, and the fraction of phenotypically similar compounds that can be expected for an increase in structural similarity was subsequently quantified. Morgan fingerprints were also found to be less sensitive to the dataset employed in these analyses than two other commonly used molecular descriptors. In Chapter 4, the mechanism of action hypotheses obtained through target prediction was compared to those obtained by extracting experimental bioactivity data of compounds active in phenotypic assays. It was then showed that the mechanism of action hypotheses generated from these two types of approach agreed where a large number of compounds were active in the phenotypic assay. When there were fewer compounds active in the phenotypic assay, target prediction complemented the use of experimental bioactivity data and allowed to uncover alternative mechanisms of action for compounds active in these assays. Finally, the in silico target prediction workflow described in Chapter 2 was applied in Chapter 5 to deconvolute the activity of compounds in a kidney cyst growth reduction assay, aimed at discovering novel therapeutic opportunities for polycystic kidney disease. A metric was developed to rank predicted targets according to the activity of the compounds driving their prediction. Gene expression data and occurrences in the literature were combined with the target predictions to further narrow down the most probable mechanisms of action of cyst growth reducing compounds in the screen. Two target predictions were proposed as a potential mechanism for the reduction of kidney cyst growth, one of which agreed with docking studies.
Subjects/Keywords: Drug discovery; Cheminformatics; Chemoinformatics; Phenotypic Screening; Target Prediction; Structural Bioinformatics; Machine Learning; Bayesian Statistics; Self Organising Maps; Polycystic Kidney Disease
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APA (6th Edition):
Zoufir, A. (2019). Computational analyses of small molecules activity from phenotypic screens. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.40403 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782760
Chicago Manual of Style (16th Edition):
Zoufir, Azedine. “Computational analyses of small molecules activity from phenotypic screens.” 2019. Doctoral Dissertation, University of Cambridge. Accessed April 23, 2021.
https://doi.org/10.17863/CAM.40403 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782760.
MLA Handbook (7th Edition):
Zoufir, Azedine. “Computational analyses of small molecules activity from phenotypic screens.” 2019. Web. 23 Apr 2021.
Vancouver:
Zoufir A. Computational analyses of small molecules activity from phenotypic screens. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2021 Apr 23].
Available from: https://doi.org/10.17863/CAM.40403 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782760.
Council of Science Editors:
Zoufir A. Computational analyses of small molecules activity from phenotypic screens. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://doi.org/10.17863/CAM.40403 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782760
Universitat Pompeu Fabra
9.
Martínez-Jiménez, Francisco, 1988-.
Structural study of the therapeutic potential of protein-ligand interactions.
Degree: Departament de Ciències Experimentals i de la Salut, 2016, Universitat Pompeu Fabra
URL: http://hdl.handle.net/10803/565402
► La mayor´ıa de las funciones celulares est´an dirigidas por peque˜nas mol´eculas que selectivamente se unen a sus prote´ınas diana. Es tal su importancia que la…
(more)
▼ La mayor´ıa de las funciones celulares est´an dirigidas por peque˜nas mol´eculas
que selectivamente se unen a sus prote´ınas diana. Es tal su importancia que la
intervenci´on farmacol´ogica de prote´ınas mediante peque˜nas mol´eculas es frecuentemente
usada para tratar m´ultiples enfermedades. A continuaci´on presento
a una tesis que utiliza un estudio tridimensional de las interacciones
entre peque˜nas mol´eculas y prote´ınas para mejorar su relevancia terap´eutica.
Espec´ıficamente, presento nAnnolyze, un m´etodo que predice interacciones
prote´ına-ligando estructuralmente detalladas y a nivel de proteoma. El m´etodo
ejemplifica su aplicabilidad a trav´es de la predicci´on de dianas terap´euticas humanas
para todas las peque˜nas mol´eculas usadas como f´armacos aprobados por
la FDA. Una segunda aplicaci´on de nAnnolyze en Mycobacterium tuberculosis
identific´o las prote´ınas diana para dos conjuntos de compuestos con actividad
contra dicha bacteria. Finalmente, la tesis describe un modelo computacional
que predice mutaciones asociadas a c´ancer con alta probabilidad de conferir
resistencia a una terapia dirigida. Adem´as, para aquellas mutaciones identificadas
como responsables de producir resistencia, el modelo tambi´en sugiere
terapias alternativas predichas como no resistentes.
III
Advisors/Committee Members: [email protected] (authoremail), true (authoremailshow), Marti-Renom, Marc A (director), true (authorsendemail).
Subjects/Keywords: Ligand-target prediction; Structural bioinformatics; Tuberculosis drug discovery; Drug resistance; Targeted cancer therapy; Pequeñas moléculas; Bioinformática estructural; Tuberculosis; Resistencia a tratamiento; Terapias dirigidas; 615
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APA (6th Edition):
Martínez-Jiménez, Francisco, 1. (2016). Structural study of the therapeutic potential of protein-ligand interactions. (Thesis). Universitat Pompeu Fabra. Retrieved from http://hdl.handle.net/10803/565402
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Martínez-Jiménez, Francisco, 1988-. “Structural study of the therapeutic potential of protein-ligand interactions.” 2016. Thesis, Universitat Pompeu Fabra. Accessed April 23, 2021.
http://hdl.handle.net/10803/565402.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Martínez-Jiménez, Francisco, 1988-. “Structural study of the therapeutic potential of protein-ligand interactions.” 2016. Web. 23 Apr 2021.
Vancouver:
Martínez-Jiménez, Francisco 1. Structural study of the therapeutic potential of protein-ligand interactions. [Internet] [Thesis]. Universitat Pompeu Fabra; 2016. [cited 2021 Apr 23].
Available from: http://hdl.handle.net/10803/565402.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Martínez-Jiménez, Francisco 1. Structural study of the therapeutic potential of protein-ligand interactions. [Thesis]. Universitat Pompeu Fabra; 2016. Available from: http://hdl.handle.net/10803/565402
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Leiden University
10.
Taneja, A.
PKPD relationships and dose rationale in analgesic drug development : towards the prediction of target engagement.
Degree: 2013, Leiden University
URL: http://hdl.handle.net/1887/22300
► Chronic pain is a significant health problem that greatly impacts the quality of life of individual patients and imparts high costs to society. Despite intense…
(more)
▼ Chronic pain is a significant health problem that greatly impacts the quality of life of individual patients and imparts high costs to society. Despite intense research effort and progress in our understanding of the mechanistic and molecular basis of pain, chronic pain remains a significant clinical problem that has few effective therapies Throughout the various chapters we have highlighted some important conceptual and experimental flaws in the way that pain signalling and pharmacological activity are characterised and translated across species and disease conditions. The common denominator of the work presented here is the requirement for accurate characterisation of exposure-response relationships, without which the dose rationale for the progression of a molecule cannot justified, whether drugs are aimed at symptomatic relief, disease modification or prophylaxis. In addition to a comprehensive review of the mechanisms underlying pain signalling and symptoms, the work developed here focuses on three different aspects of research underpinning the use of pharmacokinetic-pharmacodynamic relationships. First, we have explored the requirements for the characterisation of behavioural measures of pain during the early screening of candidate molecules, shedding light onto the shortcomings of experimental protocols commonly used in preclinical research. Then we introduced the prerequisites for the parameterisation of pain behaviour to ensure accurate translation of the pharmacological properties across species as well as for bridging across different phases of development. Lastly, an attempt was made to model clinical response in chronic inflammatory pain and to establish correlations between symptom improvement and the underlying pharmacological effects using biomarkers. In addition our work showed how clinical trial simulations can be used as a design tool, enabling the evaluation of a variety of scenarios that disentangle the contribution of pharmacology from the confounding effects of placebo and disease dynamics.
Advisors/Committee Members: Danhof, M., Della Pasqua, O.E., Leiden University.
Subjects/Keywords: Translation; Biomarkers; Chronic pain; Neuropathic pain; Prediction; Engagement; Target; Drug development; Analgesic; Relationships; PKPD
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APA ·
Chicago ·
MLA ·
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CSE |
Export
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APA (6th Edition):
Taneja, A. (2013). PKPD relationships and dose rationale in analgesic drug development : towards the prediction of target engagement. (Doctoral Dissertation). Leiden University. Retrieved from http://hdl.handle.net/1887/22300
Chicago Manual of Style (16th Edition):
Taneja, A. “PKPD relationships and dose rationale in analgesic drug development : towards the prediction of target engagement.” 2013. Doctoral Dissertation, Leiden University. Accessed April 23, 2021.
http://hdl.handle.net/1887/22300.
MLA Handbook (7th Edition):
Taneja, A. “PKPD relationships and dose rationale in analgesic drug development : towards the prediction of target engagement.” 2013. Web. 23 Apr 2021.
Vancouver:
Taneja A. PKPD relationships and dose rationale in analgesic drug development : towards the prediction of target engagement. [Internet] [Doctoral dissertation]. Leiden University; 2013. [cited 2021 Apr 23].
Available from: http://hdl.handle.net/1887/22300.
Council of Science Editors:
Taneja A. PKPD relationships and dose rationale in analgesic drug development : towards the prediction of target engagement. [Doctoral Dissertation]. Leiden University; 2013. Available from: http://hdl.handle.net/1887/22300
. 
Open Access Theses and Dissertations
