Molecular Graph Augmentation with Rings and Functional Groups

Grave, Kurt De; Costa, Fabrizio

doi:10.1021/ci9005035

Cited by 13 publications

(6 citation statements)

References 39 publications

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“…connected, fused, linked, and position on ring). DCA 18 could add vertices for all moieties to an atom-bond graph, connect the vertices and molecular structure elements with edges labelled by the substructure relationship, and then find correlation rules between the molecular structure information represented by the atom-bond graph and its experimental biological activity 63 . Users can set parameters for optimisation between model quality and run time.…”

Section: Methodsmentioning

confidence: 99%

Ligand-based virtual screening and inductive learning for identification of SIRT1 inhibitors in natural products

Sun

Zhou

Zhu

et al. 2016

Sci Rep

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Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide-dependent deacetylase, and its dysregulation can lead to ageing, diabetes, and cancer. From 346 experimentally confirmed SIRT1 inhibitors, an inhibitor structure pattern was generated by inductive logic programming (ILP) with DMax Chemistry Assistant software. The pattern contained amide, amine, and hetero-aromatic five-membered rings, each of which had a hetero-atom and an unsubstituted atom at a distance of 2. According to this pattern, a ligand-based virtual screening of 1 444 880 active compounds from Chinese herbs identified 12 compounds as inhibitors of SIRT1. Three compounds (ZINC08790006, ZINC08792229, and ZINC08792355) had high affinity (−7.3, −7.8, and −8.6 kcal/mol, respectively) for SIRT1 as estimated by molecular docking software AutoDock Vina. This study demonstrated a use of ILP and background knowledge in machine learning to facilitate virtual screening.

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Section: Methodsmentioning

confidence: 99%

Ligand-based virtual screening and inductive learning for identification of SIRT1 inhibitors in natural products

Sun

Zhou

Zhu

et al. 2016

Sci Rep

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“…Listing 4 shows a kLog script for the Bursi domain. Relevant predicates in the extensional database are a/2, b/3 (atoms and bonds, respectively, extracted from the chemical structure), sub/3 (functional groups, computed by DMax Chemistry Assistant [55,56]), fused/3, connected/4 (direct connection between two functional groups), linked/4 (connection between functional groups via an aliphatic chain). Aromaticity (used in the bond-type property of b/3) was also computed by DMax Chemistry Assistant.…”

Section: Predicting a Single Property Of One Interpretationmentioning

confidence: 99%

kLog: A language for logical and relational learning with kernels

Costa

Raedt

Grave

2014

Artificial Intelligence

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We introduce kLog, a novel approach to statistical relational learning. Unlike standard approaches, kLog does not represent a probability distribution directly. It is rather a language to perform kernel-based learning on expressive logical and relational representations. kLog allows users to specify learning problems declaratively. It builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, logic programming, and deductive databases. Access by the kernel to the rich representation is mediated by a technique we call graphicalization: the relational representation is first transformed into a graph -in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. kLog supports mixed numerical and symbolic data, as well as background knowledge in the form of Prolog or Datalog programs as in inductive logic programming systems. The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification. We also report about empirical comparisons, showing $ PF was a visiting professor at KU Leuven and FC a postdoctoral fellow at KU Leuven while this work was initiated * Corresponding author

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“…Hierarchical approaches In some cases, sets of nodes can be grouped together into larger entities, and working with such groups is more efficient than working with individual nodes. For example, in molecules, atoms are often grouped together in functional groups such as rings and chains [41], [11], [23]. Also, in traffic networks, streets may be grouped into districts or cities, an approach which is typically used in routing applications [33].…”

Section: Optimization Techniquesmentioning

confidence: 99%