Modelling Structure‐Activity Relationships

Bravi, Gianpaolo; Gancia, Emanuela; Green, Darren V. S.; Hann, Mike

doi:10.1002/9783527613083.ch5

Cited by 13 publications

(21 citation statements)

References 74 publications

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“…This is frequently pursued through iterative Structure-Activity Relationship (SAR) studies where individual moieties of a ligand are optimised sequentially. 8,9 A major simplification implicit in this approach is the additivity of the binding free energy, i.e. the assumption that binding free energies can be decomposed into a sum of independent components ascribed to specific parts of the system.…”

Section: Introductionmentioning

confidence: 99%

Elucidation of Nonadditive Effects in Protein–Ligand Binding Energies: Thrombin as a Case Study

Calabró

Woods²,

Powlesland

et al. 2016

J. Phys. Chem. B

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Section: Introductionmentioning

confidence: 99%

Elucidation of Nonadditive Effects in Protein–Ligand Binding Energies: Thrombin as a Case Study

Calabró

Woods²,

Powlesland

et al. 2016

J. Phys. Chem. B

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“…The solution to the ranked-retrieval problem relies on the well known fact that chemical structure of a compound relates to its activity (SAR) [9]. As such, effective solutions can be devised that rank the compounds on the database based on how structurally similar they are to the query.…”

Section: Problem Statement and Motivationmentioning

confidence: 99%

“…The similarity between chemical compounds is usually computed by first transforming them into a suitable descriptorspace representation [8,9]. A number of different approaches have been developed to represent each compound by a set of descriptors.…”

Section: Descriptor Spaces For Ranked-retrievalmentioning

confidence: 99%

“…This approach relies on the well-known fact that compounds sharing key structural features will most likely have similar activity against a biomolecular target. This is referred to as the structure activity relationship (SAR) [9]. The similarity between the compounds is usually computed by first representing their molecular graph as a vector in a particular descriptor-space and then using a variety of vectorbased methods to compute their similarity [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Methods for Effective Virtual Screening and Scaffold-Hopping in Chemical Compounds

Wale¹,

Karypis²,

Watson³

2007

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. AbstractMethods that can screen large databases to retrieve a structurally diverse set of compounds with desirable bioactivity properties are critical in the drug discovery and development process. This paper presents a set of such methods, which are designed to find compounds that are structurally different to a certain query compound while retaining its bioactivity properties (scaffold hops). These methods utilize various indirect ways of measuring the similarity between the query and a compound that take into account additional information beyond their structure-based similarities. Two sets of techniques are presented that capture these indirect similarities using approaches based on automatic relevance feedback and on analyzing the similarity network formed by the query and the database compounds. Experimental evaluation shows that many of these methods substantially outperform previously developed approaches both in terms of their ability to identify structurally diverse active compounds as well as active compounds in general.

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“…Specifically, if A is the set of compounds that have been retrieved thus far, then the next compound c next that is selected is given by (2) This compound is added to A, removed from the database, and the overall process is repeated until the desired number of compounds has been retrieved. Depending on whether the indirect similarities are computed on the NG or MG graph, we will refer to this retrieval strategy as BestSimNG or BestSimMG, respectively.…”

Section: Best-sim Retrievalmentioning

confidence: 99%

Indirect Similarity Based Methods for Effective Scaffold-Hopping in Chemical Compounds

Wale

Watson

Karypis

2008

J. Chem. Inf. Model.

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Methods that can screen large databases to retrieve a structurally diverse set of compounds with desirable bioactivity properties are critical in the drug discovery and development process. This paper presents a set of such methods that are designed to find compounds that are structurally different to a certain query compound while retaining its bioactivity properties (scaffold hops). These methods utilize various indirect ways of measuring the similarity between the query and a compound that take into account additional information beyond their structure-based similarities. The set of techniques that are presented capture these indirect similarities using approaches based on analyzing the similarity network formed by the query and the database compounds. Experimental evaluation shows that most of these methods substantially outperform previously developed approaches both in terms of their ability to identify structurally diverse active compounds as well as active compounds in general.

show abstract

Modelling Structure‐Activity Relationships

Cited by 13 publications

References 74 publications

Elucidation of Nonadditive Effects in Protein–Ligand Binding Energies: Thrombin as a Case Study

Elucidation of Nonadditive Effects in Protein–Ligand Binding Energies: Thrombin as a Case Study

Methods for Effective Virtual Screening and Scaffold-Hopping in Chemical Compounds

Indirect Similarity Based Methods for Effective Scaffold-Hopping in Chemical Compounds

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