Chemical data mining based on non-terminal vertex graph

2009

CBIJ

Recently, many concerns are paid for dual action drugs such as ACE/NEP dual inhibitors which have two different biological activities. To identify multiple active drugs by supervised learning approach, a multi-label classification technique is required. In the present work, we investigated the classification of antihypertensive drugs including ACE/NEP dual inhibitors using support vector machines (SVMs). Biological activity data of the drugs were taken from the MDDR database and they were employed for the computational trial for the training of the SVM classifiers. Structural feature representation of each drug molecule was based on topological fragment spectra (TFS) method. The obtained classifiers were tested for finding ACE/NEP dual inhibitors. The result suggests that the TFS-based SVM classifiers are useful for finding multiple active drugs such as ACE/NEP dual inhibitors.

Section: Predictive Ability Of Dual Inhibitors and Similaritymentioning

confidence: 99%

Identification of the Dual Action Antihypertensive Drugs Using TFS-Based Support Vector Machines

2009

CBIJ

“…Figure shows part of the results of the hAA2A ligand design, which were obtained by the current evolutionary approach. We investigated the individual scaffolds of the designed molecules in terms of chemical graph (CG) expression for nonterminal vertex graph (NTG) . An NTG is defined as a graph that has no terminal vertex and no isolated vertex.…”

Section: Resultsmentioning

confidence: 99%

“…We investigated the individual scaffolds of the designed molecules in terms of chemical graph (CG) expression for nonterminal vertex graph (NTG). 36 An NTG is defined as a graph that has no terminal vertex and no isolated vertex. In the design experiment, 100 molecules with 11 NTG scaffolds including molecules 3−6 are designed using the seed fragments derived from the reference molecule 1.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

De Novo Design of Drug-Like Molecules by a Fragment-Based Molecular Evolutionary Approach

Nagata

2014

J. Chem. Inf. Model.

This paper describes a similarity-driven simple evolutionary approach to producing candidate molecules of new drugs. The aim of the method is to explore the candidates that are structurally similar to the reference molecule and yet somewhat different in not only peripheral chains but also their scaffolds. The method employs a known active molecule of our interest as a reference molecule which is used to navigate a huge chemical space. The reference molecule is also used to obtain seed fragments. An initial set of individual structures is prepared with the seed fragments and additional fragments using several connection rules. The fragment library is preferably prepared from a collection of known molecules related to the target of the reference molecule. Every fragment of the library can be used for fragment-based mutation. All the fragments are categorized into three classes; rings, linkers, and side chains. New individuals are produced by the crossover and the fragment-based mutation with the fragment library. Computer experiments with our own fragment library prepared from GPCR SARfari verified the feasibility of our approach to drug discovery.

“…We investigated the molecular scaffolds embedded in the present data sets by means of nonterminal vertex graph (NTG) analysis. A NTG is defined as a vertex graph which does not have any terminal vertex nor any isolated vertex.…”

Section: Resultsmentioning

confidence: 99%

Predictive Activity Profiling of Drugs by Topological-Fragment-Spectra-Based Support Vector Machines

Fujishima

2008

J. Chem. Inf. Model.

Aiming at the prediction of pleiotropic effects of drugs, we have investigated the multilabel classification of drugs that have one or more of 100 different kinds of activity labels. Structural feature representation of each drug molecule was based on the topological fragment spectra method, which was proposed in our previous work. Support vector machine (SVM) was used for the classification and the prediction of their activity classes. Multilabel classification was carried out by a set of the SVM classifiers. The collective SVM classifiers were trained with a training set of 59,180 compounds and validated by another set (validation set) of 29,590 compounds. For a test set that consists of 9,864 compounds, the classifiers correctly classified 80.8% of the drugs into their own active classes. The SVM classifiers also successfully performed predictions of the activity spectra for multilabel compounds.