From in silico target prediction to multi-target drug design: Current databases, methods and applications

Koutsoukas, Alexios; Simms, B. T.; Kirchmair, Johannes; Bond, Peter J.; Whitmore, Alan V.; Zimmer, Steven; Young, Malcolm P.; Jenkins, Jeremy L.; Glick, Meir; Glen, Robert C.; Bender, Andreas

doi:10.1016/j.jprot.2011.05.011

Cited by 248 publications

(189 citation statements)

References 158 publications

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“…Rationalizing mode-of-action hypotheses utilizing the wealth of experimental data available in the public domain is now possible with chemogenomics and cheminformatics applications (43). In this regard, we applied the well established Laplacianmodified naive Bayesian classifier as implemented by Koutsoukas et al (44) and predicted potential targets of CIMO.…”

Section: Cheminformatics-based Mode-of-action Rationalization For Cimmentioning

confidence: 99%

Development of a Novel Azaspirane That Targets the Janus Kinase-Signal Transducer and Activator of Transcription (STAT) Pathway in Hepatocellular Carcinoma in Vitro and in Vivo

Mohan

Bharathkumar

Bulusu

et al. 2014

Journal of Biological Chemistry

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153

120

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Background: Constitutive activation of STAT3 is associated with the progression of hepatocellular carcinoma (HCC), and abrogation of STAT3 signaling is a potential target for HCC treatment. Results: A novel azaspirane modulates the JAK-STAT pathway in HCC. Conclusion:The lead compound induces apoptosis by down-regulating STAT3 signaling. Significance: This investigation reports a novel inhibitor of the JAK-STAT pathway with the potential to target various cancers.

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Section: Cheminformatics-based Mode-of-action Rationalization For Cimmentioning

confidence: 99%

Development of a Novel Azaspirane That Targets the Janus Kinase-Signal Transducer and Activator of Transcription (STAT) Pathway in Hepatocellular Carcinoma in Vitro and in Vivo

Mohan

Bharathkumar

Bulusu

et al. 2014

Journal of Biological Chemistry

Self Cite

153

120

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“…Current estimates are that a drug might on average interact with about six different targets (7). It is therefore not surprising that the notion of polypharmacology is beginning to influence drug discovery and design strategies (8,9). It also provides the conceptual basis of drug repurposing (10,11), i.e., attempting to find novel targets and therapeutic applications for existing drugs; another muchdiscussed topic in pharmaceutical research and development.…”

Section: Introductionmentioning

confidence: 99%

How Promiscuous Are Pharmaceutically Relevant Compounds? A Data-Driven Assessment

Bajorath

2012

AAPS J

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Abstract. Given the increasing notion of target promiscuity of bioactive compounds and polypharmacological drug behavior, a detailed analysis of publicly available compound activity data from medicinal chemistry sources was carried out to determine and quantify the degree of promiscuity of active compounds across all known human target families. The results are surprising. Approximately 62% of currently available compounds with high-confidence activity data are only annotated with a single biological target, whereas 36% are known to act against multiple targets within the same family (i.e., closely related targets). However, only ∼2% of bioactive compounds are promiscuous across different target families. Thus, despite general data sparseness, these findings indicate that highly promiscuous bioactive compounds only rarely occur. Because pharmaceutically relevant active compounds represent the pool from which drug candidates emerge, one might extrapolate from these results and conclude that there is a low statistical probability to obtain drugs that act against multiple targets belonging to distinct families.

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“…Chemoinformatical fingerprint-based similarity tools are employed to identify close analogue compounds with a known mechanism of action. 90,91 Nevertheless, pharmacophore modeling may also be an option, rather than screening compounds with a pharmacophore query. The molecule itself may become the query and the aim is to identify the most likely pharmacophore model that fits the molecule.…”

Section: Pharmacophore-guided Drug Target Identificationmentioning

confidence: 99%

Pharmacophore modeling: advances, limitations, and current utility in drug discovery

Qing¹,

Lee²,

Raeymaecker³

et al. 2014

JRLCR

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Pharmacophore modeling is a successful yet very diverse subfield of computer-aided drug design. The concept of the pharmacophore has been widely applied to the rational design of novel drugs. In this paper, we review the computational implementation of this concept and its common usage in the drug discovery process. Pharmacophores can be used to represent and identify molecules on a 2D or 3D level by schematically depicting the key elements of molecular recognition. The most common application of pharmacophores is virtual screening, and different strategies are possible depending on the prior knowledge. However, the pharmacophore concept is also useful for ADME-tox modeling, side effect, and off-target prediction as well as target identification. Furthermore, pharmacophores are often combined with molecular docking simulations to improve virtual screening. We conclude this review by summarizing the new areas where significant progress may be expected through the application of pharmacophore modeling; these include protein-protein interaction inhibitors and protein design. Keywords: ADME-tox, computer-aided drug design, pharmacophore fingerprint, protein design, virtual screening What is computer-aided drug design?Drug design is an expensive and laborious process of developing new medicine. This process has its origin in herbal remedies dating back millennia.1 Only since the last century have drugs had a (semi)synthetic origin.2 The first hit compounds often lack both potency and safety, and must therefore be optimized. While historically this was a trial-and-error process, 3,4 soon rational strategies were developed to improve potency. 5,6 As with any data handling procedures, computers have become a more prominent and ubiquitous tool in drug discovery since the 1980s. The crossover between computational and pharmaceutical research is typically designated computer-aided drug design (CADD). 8,9 CADD covers a broad range of applications spanning the drug discovery pipeline, although these are highly clustered in the early phases. The main purpose of CADD is to speed up and rationalize the drug design process while reducing costs. 10 The aim of the earliest phase in drug discovery is to identify the first hit compounds, which is sometimes attempted by high-throughput screening (HTS), the testing of many thousands of compounds with a suitable activity assay. The in silico counterpart of in vitro HTS is referred to as virtual screening and aims at filtering libraries of molecules using computational methods to prioritize those most likely to be active for a given

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From in silico target prediction to multi-target drug design: Current databases, methods and applications

Cited by 248 publications

References 158 publications

Development of a Novel Azaspirane That Targets the Janus Kinase-Signal Transducer and Activator of Transcription (STAT) Pathway in Hepatocellular Carcinoma in Vitro and in Vivo

Development of a Novel Azaspirane That Targets the Janus Kinase-Signal Transducer and Activator of Transcription (STAT) Pathway in Hepatocellular Carcinoma in Vitro and in Vivo

How Promiscuous Are Pharmaceutically Relevant Compounds? A Data-Driven Assessment

Pharmacophore modeling: advances, limitations, and current utility in drug discovery

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