Inductive QSAR Descriptors. Distinguishing Compounds with Antibacterial Activity by Artificial Neural Networks

Cherkasov, Artem

doi:10.3390/i6010063

Cited by 32 publications

(36 citation statements)

References 37 publications

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“…These methods rely on the chemo-informatic method of quantitative structure-activity relationship (QSAR) modelling to relate the measured antimicrobial activity to the structural characteristics associated with the equivalent peptide sequences, as defined through the use of dozens of physico-chemical “descriptors” (including inductive parameters such as contact energy between neighbouring amino acids that assess how the properties of amino acids change along the length of the peptide) 35 . Using a test set of peptides derived from Bac2a peptide, novel peptides with significant activity against P.aeruginosa were used to predict structure-activity relationships and test the validity of QSAR descriptors 36,37 .…”

Section: Antimicrobial Peptidesmentioning

confidence: 99%

Peptide design for antimicrobial and immunomodulatory applications

2013

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The increasing threat of antibiotic resistance in pathogenic bacteria and the dwindling supply of antibiotics available to combat these infections poses a significant threat to human health throughout the world. Antimicrobial peptides have long been touted as the next generation of antibiotics capable of filling the anti-infective void. Unfortunately, peptide based antibiotics have yet to realize their potential as novel pharmaceuticals, in spite of the immense number of known antimicrobial peptide sequences and our improved understanding of their antibacterial mechanism of action. Recently, the immunomodulatory properties of certain antimicrobial peptides have become appreciated. The ability of small synthetic peptides to protect against infection in vivo has demonstrated that modulation of the innate immune response is an effective strategy to further develop peptides as novel anti-infectives. This review focuses on the screening methods that have been employed to assess novel peptide sequences for their antibacterial and immunomodulatory properties. It will also examine how we have progressed in our ability to identify and optimize peptides with desired biological characteristics and enhanced therapeutic potential. In addition, the current challenges to the development of peptides as anti-infectives are examined and the strategies being used to overcome these issues are discussed.

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Section: Antimicrobial Peptidesmentioning

confidence: 99%

Peptide design for antimicrobial and immunomodulatory applications

2013

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“…Global approaches to modelling aquatic toxicity include k nearest neighbours (kNN) [8,9] or nearest neighbours [10], support vector machines (SVM) [11], multilinear regression (MLR) [10,[12][13][14], MLR using only structurally similar chemicals from the training set [15], group contribution methods [16,17], partial least squares [18,19], artificial neural networks (ANNs) [12,20], associative neural networks (ASNN) [21] and hierarchical clustering (HC) [10,22]. The advantage of global methods is that machine learning allows the development of model(s), which do not require the determination of chemical class or MOA.…”

Section: Introductionmentioning

confidence: 99%

Comparison of global and mode of action-based models for aquatic toxicity

Martin

Young

Lilavois

et al. 2015

SAR and QSAR in Environmental Research

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The ability to estimate aquatic toxicity is a critical need for ecological risk assessment and chemical regulation. The consensus in the literature is that mode of action (MOA) based toxicity models yield the most toxicologically meaningful and, theoretically, the most accurate results. In this study, a two-step prediction methodology was developed to estimate acute aquatic toxicity from molecular structure. In the first step, one-against-the-rest linear discriminant analysis (LDA) models were used to predict the MOA. The LDA models were able to predict the MOA with 85.8-88.8% accuracy for broad and specific MOAs, respectively. In the second step, a multiple linear regression (MLR) model corresponding to the predicted MOA was used to predict the acute aquatic toxicity value. The MOA-based approach was found to yield similar external prediction accuracy (r(2) = 0.529-0.632) to a single global MLR model (r(2) = 0.551-0.562) fit to the entire training set. Overall, the global hierarchical clustering approach yielded a higher combination of accuracy and prediction coverage (r(2) = 0.572, coverage = 99.3%) than the other approaches. Utilizing multiple two-dimensional chemical descriptors in MLR models yielded comparable results to using only the octanol-water partition coefficient (log K(ow)).

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“…Table 1 features 50 'inductive' QSAR descriptors that can be calculated in the framework of equations (1)- (11). It should be noted that in a previous study [25], these molecular parameters allowed creation of the QSAR model enabling 93% correct recognition of low-molecular weight antibacterial compounds. (7) where charges get updated according to (6); an atomic hardness in (7) …”

Section: 'Inductive' Descriptors Overviewmentioning

confidence: 99%

Application of ‘Inductive’ QSAR Descriptors for Quantification of Antibacterial Activity of Cationic Polypeptides

Cherkasov

Jankovic²

2004

Molecules

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Abstract:On the basis of the inductive QSAR descriptors we have created a neural network-based solution enabling quantification of antibacterial activity in the series of 101 synthetic cationic polypeptides (CAMEL-s). The developed QSAR model allowed 80% correct categorical classification of antibacterial potencies of the CAMEL-s both in the training and the validation sets. The accuracy of the activity predictions demonstrates that a narrow set of 3D sensitive 'inductive' descriptors can adequately describe the aspects of intra-and intermolecular interactions that are relevant for antibacterial activity of the cationic polypeptides. The developed approach can be further expanded for the larger sets of biologically active peptides and can serve as a useful quantitative tool for rational antibiotic design and discovery.

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Inductive QSAR Descriptors. Distinguishing Compounds with Antibacterial Activity by Artificial Neural Networks

Cited by 32 publications

References 37 publications

Peptide design for antimicrobial and immunomodulatory applications

Peptide design for antimicrobial and immunomodulatory applications

Comparison of global and mode of action-based models for aquatic toxicity

Application of ‘Inductive’ QSAR Descriptors for Quantification of Antibacterial Activity of Cationic Polypeptides

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