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

Kawai, Kentaro; Takahashi, Yoshimasa

doi:10.1273/cbij.9.41

Cited by 13 publications

(12 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reactions were cleaned using the same protocol as applied to the USPD (24,606 were retained); reaction vectors were calculated; the reactions were classified using SHREC and all entries associated with a high prediction probability were retained, based on a minimum credibility score of 0.25 as described in Ghiandoni et al [13]. 16,582 entries remained at this stage. The reaction classes were represented at level-3 of the SHREC hierarchy and duplicates, that is, identical starting materials and identical class labels, were filtered out leaving 11,544 entries.…”

Section: Journal Of Medicinal Chemistry (Jmc) Class Predictionmentioning

confidence: 99%

“…Training the recommender is, therefore, configured as a multi-label classification problem [14]. Multi-label classification approaches have previously been used to predict the activity profiles of small molecules against a panel of protein targets [15][16][17][18][19], drug side-effects [20], and to identify possible plant sources for natural products [21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing reaction-based de novo design using a multi-label reaction class recommender

Ghiandoni

Bodkin

Chen

et al. 2020

J Comput Aided Mol Des

View full text Add to dashboard Cite

Reaction-based de novo design refers to the in-silico generation of novel chemical structures by combining reagents using structural transformations derived from known reactions. The driver for using reaction-based transformations is to increase the likelihood of the designed molecules being synthetically accessible. We have previously described a reaction-based de novo design method based on reaction vectors which are transformation rules that are encoded automatically from reaction databases. A limitation of reaction vectors is that they account for structural changes that occur at the core of a reaction only, and they do not consider the presence of competing functionalities that can compromise the reaction outcome. Here, we present the development of a Reaction Class Recommender to enhance the reaction vector framework. The recommender is intended to be used as a filter on the reaction vectors that are applied during de novo design to reduce the combinatorial explosion of in-silico molecules produced while limiting the generated structures to those which are most likely to be synthesisable. The recommender has been validated using an external data set extracted from the recent medicinal chemistry literature and in two simulated de novo design experiments. Results suggest that the use of the recommender drastically reduces the number of solutions explored by the algorithm while preserving the chance of finding relevant solutions and increasing the global synthetic accessibility of the designed molecules.

show abstract

Section: Journal Of Medicinal Chemistry (Jmc) Class Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing reaction-based de novo design using a multi-label reaction class recommender

Ghiandoni

Bodkin

Chen

et al. 2020

J Comput Aided Mol Des

View full text Add to dashboard Cite

show abstract

“… Chemical data analysis . MLL has also been applied to predict adverse drug reactions, to identify the drugs that have two or more different biological actions (drug discovery) and to detect contaminants in machine lubricants by using spectral images (vision‐based metal spectral analysis) Social network mining has become a new area of interest.…”

Section: Multi‐label Learningmentioning

confidence: 99%

Multi‐label learning: a review of the state of the art and ongoing research

Gibaja

Ventura

2014

WIREs Data Min & Knowl

160

122

View full text Add to dashboard Cite

Multi-label learning is quite a recent supervised learning paradigm. Owing to its capabilities to improve performance in problems where a pattern may have more than one associated class, it has attracted the attention of researchers, producing an increasing number of publications. This study presents an up-to-date overview about multi-label learning with the aim of sorting and describing the main approaches developed till now. The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed, pitfalls and guidelines, and ongoing research are presented.

show abstract

“…First applications of MLL were related to classification of text and multimedia [45,46], in which one document or picture could be simultaneously associated with several categories, and protein and gene function classification, in which a gene or protein can perform several functions [59]. Nowadays MLL has become a challenging research area with an increasing number of papers and domains of application such as drug discovery [24], social network mining [27], and direct marketing [61].…”

Section: Introductionmentioning

confidence: 99%

LAIM discretization for multi-label data

Cano

Luna

Gibaja

et al. 2016

Information Sciences

View full text Add to dashboard Cite

Multi-label learning is a challenging task in data mining which has attracted growing attention in recent years. Despite the fact that many multi-label datasets have continuous features, general algorithms developed specially to transform multi-label datasets with continuous attributes' values into a finite number of intervals have not been proposed to date. Many classification algorithms require discrete values as the input and studies have shown that supervised discretization may improve classification performance. This paper presents a Label-Attribute Interdependence Maximization (LAIM) discretization method for multi-label data. LAIM is inspired in the discretization heuristic of CAIM for single-label classification. The maximization of the label-attribute interdependence is expected to improve labels prediction in data separated through disjoint intervals. The main aim of this paper is to present a discretization method specifically designed to deal with multi-label data and to analyze whether this can improve the performance of multi-label learning methods. To this end, the experimental analysis evaluates the performance of 12 multi-label learning algorithms (transformation, adaptation, and ensemble-based) on a series of 16 multi-label datasets with and without supervised and unsupervised discretization, showing that LAIM discretization improves the performance for many algorithms and measures.

show abstract

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

Cited by 13 publications

References 23 publications

Enhancing reaction-based de novo design using a multi-label reaction class recommender

Enhancing reaction-based de novo design using a multi-label reaction class recommender

Multi‐label learning: a review of the state of the art and ongoing research

LAIM discretization for multi-label data

Contact Info

Product

Resources

About