Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information

Hall, Lowell H.; Kier, Lemont B.

doi:10.1021/ci00028a014

Cited by 674 publications

(498 citation statements)

References 4 publications

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“…More precisely, we trained a classifier making use of actives, inactives and compounds with intermediate activity for one receptor and then test the performance of constructed decision boundary in separating active and inactive compounds with respect to the second receptor. In this experiment, we included one more fingerprint, Estate fingerprint (Est), which contains only 79 bits and is considered as a basic fingerprint representation [15].…”

Section: Chemical Space Explorationmentioning

confidence: 99%

SVM with a neutral class

Śmieja

Tabor

Spurek

2017

Pattern Anal Applic

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a learning framework. For instance, in the semi-supervised learning, the classifier is allowed to use unlabeled data from underlying classes for improving its classification accuracy [5,28]. In universum learning, we might use unlabeled data samples that do not belong to either classes [29,32]. Integrating pre-defined additional information into a learning framework would usually yield improvement in the classification results and obtaining better insight into data.In this paper, we support the above hypothesis and show that neutral instances can be easily handled with a use of Tri-Class SVM model [2]. Our motivation of including neutral class in a training comes from cheminformatics and computer-aided drug design, in which we focus on detecting compounds acting on a particular protein (biological receptor). A compound is considered active if its binding constant K i ∈ [0, +∞) (measured in a laboratory) is lower than a threshold a = 10 2 , while for inactive compounds a binding constant must be greater than b = 10 31 [31]. Consequently, we get a third class of compounds with an intermediate activity level such that K i ∈ [10 2 , 10 3 ], which forms a neutral class. Although it is a common practice to ignore this neutral class in the learning process [26], we show that its use allows to explore the chemical space better, see Fig. 1.Tri-Class SVM [2] is a generalization of classical SVM [8], which builds a single learning model for three-class problems and avoids pairwise coupling strategy. To use instances of neutral class in the learning process, we develop its two parameterizations: SVM {0} and SVM [−1,1] . In analogy to classical SVM, we look for such a hyperplane which maximizes the margin between positive and negative examples Abstract In many real binary classification problems, in addition to the presence of positive and negative classes, we are also given the examples of third neutral class, i.e., the examples with uncertain or intermediate state between positive and negative. Although it is a common practice to ignore the neutral class in a learning process, its appropriate use can lead to the improvement in classification accuracy. In this paper, to include neutral examples in a training stage, we adapt two variants of Tri-Class SVM (proposed by Angulo et al. in Neural Process Lett 23(1):89-101, 2006), the method designed to solve three-class problems with a use of single learning model. In analogy to classical SVM, we look for such a hyperplane, which maximizes the margin between positive and negative instances and which is localized as close to the neutral class as possible. In addition to original Angulo's paper, we give a new interpretation of the model and show that it can be easily implemented in the primal. Our experiments demonstrate that considered methods obtain better results in binary classification problems than classical SVM and semi-supervised SVM.

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Section: Chemical Space Explorationmentioning

confidence: 99%

SVM with a neutral class

Śmieja

Tabor

Spurek

2017

Pattern Anal Applic

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“…The E-state indices (Hall and Kier, 1995) encode information about both the topological environment and the electronic interaction of an atom due to all other atoms in the molecule. Increasing presence of these features in a molecule contribute more towards binding activity.…”

Section: Stepwise-mlr-derived Qsar Modelmentioning

confidence: 99%

Structural requirements for the binding affinity of some small, non–peptide C5a receptor antagonists

Xie¹,

Wang²,

Xie³

et al. 2011

Nature Precedings

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“…An important observation in generating QSAR models was the occurrence of S_aaCH, Shadow_XYfrac, Lowest Unoccupied Molecular Orbital (LUMO), and HydrogenBond Acceptors (Hbond Acceptor) as the frequent descriptors (Figure 2), indicating these four descriptors play an important role in determining the anti-HIV activity. The E-state indices [41,42] encode information about both the topological environment and the electronic interaction of an atom due to all other atoms in the molecule. S_aaCH is one descriptor of the E-state indices and represents the atomic type of ¼ CH À in aromatic ring.…”

Section: D-qsar Modelmentioning

confidence: 99%

Quantitative Structure–Activity Relationship Analysis of Some Thiourea Derivatives with Activities Against HIV‐1 (IIIB)

Chen

Xie

et al. 2009

QSAR Comb. Sci.

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Two-dimensional (2D) and Three-dimensional (3D) Quantitative Structure -Activity Relationship (QSAR) studies have been carried out on a series of 42 recently synthesized thiourea derivatives to find out the structural requirements of their protection of MT-4 cells against Human Immunodeficiency Virus (HIV)-1 (IIIB). The statistically significant 2D-QSAR model (r 2 ¼ 0.897) was developed by Genetic Function Approximation (GFA) when the number of descriptors in equation was set to four, indicating descriptors of S_aaCH, Shadow_XYfrac, Lowest Unoccupied Molecular Orbital (LUMO), and Hydrogen-Bond Acceptors (Hbond Acceptor) mainly control the bioactivity. The validation of the model was done by the full cross-validation tests, randomization tests, and external test set prediction. Molecular Field Analysis (MFA) investigated the substitutional requirements for the favorable receptor -drug interaction and constructed the best 3D-QSAR model using Genetic Partial Least Squares (G/PLS) method, showing that the electrostatic fields contribute significantly toward bioactivity. The results obtained by combining both methodologies give insight into the key features for designing more potent analogs against HIV-1(IIIB).

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Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information

Cited by 674 publications

References 4 publications

SVM with a neutral class

SVM with a neutral class

Structural requirements for the binding affinity of some small, non–peptide C5a receptor antagonists

Quantitative Structure–Activity Relationship Analysis of Some Thiourea Derivatives with Activities Against HIV‐1 (IIIB)

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