Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification.

We introduce a family of positive definite kernels specifically optimized for the manipulation of 3D structures of molecules with kernel methods. The kernels are based on the comparison of the three-points pharmacophores present in the 3D structures of molecules, a set of molecular features known to be particularly relevant for virtual screening applications. We present a computationally demanding exact implementation of these kernels, as well as fast approximations related to the classical fingerprint-based approaches. Experimental results suggest that this new approach outperforms state-of-the-art algorithms based on the 2D structure of molecules for the detection of inhibitors of several drug targets.

Section: Definition 8 (Two-points Tanimoto Kernel)mentioning

confidence: 99%

Section: Definition 8 (Two-points Tanimoto Kernel)mentioning

confidence: 99%

See 1 more Smart Citation

The Pharmacophore Kernel for Virtual Screening with Support Vector Machines

Mahé

Ralaivola

Stoven

et al. 2006

“…13 SVMs were originally developed for binary classification problems and have become popular in the chemoinformatics field. [16][17][18] In a typical SVM analysis, training compounds belonging to two different classes (e.g., active versus inactive) are projected into chemical reference space and a separating hyperplane is derived. Then, test compounds are evaluated in this reference space to predict their class labels dependent on which side of the hyperplane they fall.…”

Section: Introductionmentioning

confidence: 99%

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Geppert¹,

Horváth²,

Gärtner³

et al. 2008

Similarity searching using molecular fingerprints is computationally efficient and a surprisingly effective virtual screening tool. In this study, we have compared ranking methods for similarity searching using multiple active reference molecules. Different 2D fingerprints were used as search tools and also as descriptors for a support vector machine (SVM) algorithm. In systematic database search calculations, a SVM-based ranking scheme consistently outperformed nearest neighbor and centroid approaches, regardless of the fingerprints that were tested, even if only very small training sets were used for SVM learning. The superiority of SVM-based ranking over conventional fingerprint methods is ascribed to the fact that SVM makes use of information about database molecules, in addition to known active compounds, during the learning phase.

“…7,[11][12][13][14][15][16][17] However, even support vector machines that are known to be among the most accurate methods for classification tasks have so far not provided fully satisfactory results. [17][18][19][20] This can be, at least in part, explained by the implicit difficulties that arise from the data set.…”

Section: Introductionmentioning

confidence: 99%

Gradual in Silico Filtering for Druglike Substances

Schneider

Jäckels

Andrès

et al. 2008

The suitability of decision trees in comparison to support vector machines for the classification of chemical compounds into drugs and nondrugs was investigated. To account for the requirements upon screening virtual compound libraries, schemes for successive filtering steps with gradual increasing computational cost are outlined. The obtained prediction accuracy was similar between decision trees and support vector machine approaches for the applied compound data sets. By using rapidly computable variables such as druglikeness indices, XlogP, and the molar refractivity, at least 39% of the nondrugs can be filtered out, while retaining more than 83% of the actual drugs. Computationally more demanding descriptors such as specific substructure queries and quantum chemically derived variables can be postponed to subsequent classification schemes for the reduced set of compounds, whereby up to 92% of the nondrugs can be sorted out without loosing considerably more drugs. Using all available computed descriptors simultaneously in the first step did not yield significantly better results. Furthermore, the generated decision trees are used to derive guidelines for the design of druglike substances. The numerical margins found at the branching points suggest several criteria that separate drugs from nondrugs: a molecular weight higher than 230, a molar refractivity higher than 40, and the presence of one or more rings as well as one or more functional groups. Also reported are additionally required parameters to compute values for XlogP, SlogP, and the molar refractivity of boron and silicon containing compounds.