Drug design by machine learning: support vector machines for pharmaceutical data analysis

Burbidge, Robert; Trotter, Matthew; Buxton, Bernard F.; Holden, Sean B.

doi:10.1016/s0097-8485(01)00094-8

Cited by 619 publications

(413 citation statements)

References 14 publications

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“…Finally, we consider additional "two-points pharmacophore" versions of the kernels (15), (16) and (17), based on pairs, instead of triplets, of atoms (19). Letting T 2 be the set of all possible two-points pharmacophores, that is, pairs of atom types together with the bin index of the edge connecting them, and φ 2pt 0 (m) = (φ t,0 (m)) t∈T2 and φ 2pt 1 (m) = (φ t,1 (m)) t∈T2 be the mappings of the molecule m to T 2 , corresponding to φ 3pt 0 (m) and φ 3pt 1 (m), we define the three following kernels.…”

Section: Definition 5 (Three-points Tanimoto Kernel) For a Pair Of Mmentioning

confidence: 99%

“…The Gram matrix is updated according to the spectrum (15), binary (16) or Tanimoto (17) definition of the kernel, and we introduce a n × n Gram matrix K, initialized to zero, together with a binary variable kernelType that can take the values 'spectrum', 'binary' or 'Tanimoto'.…”

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

confidence: 99%

“…Note however that in the case of the Tanimoto kernel type, this procedure computes the 'raw' kernel that actually corresponds to the binary kernel (16). The matrix K must be further normalized according to defintion 5, that is :…”

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

confidence: 99%

See 2 more Smart Citations

The Pharmacophore Kernel for Virtual Screening with Support Vector Machines

Mahé

Ralaivola

Stoven

et al. 2006

J. Chem. Inf. Model.

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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.

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Section: Definition 5 (Three-points Tanimoto Kernel) For a Pair Of Mmentioning

confidence: 99%

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

confidence: 99%

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The Pharmacophore Kernel for Virtual Screening with Support Vector Machines

Mahé

Ralaivola

Stoven

et al. 2006

J. Chem. Inf. Model.

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“…SVMs have also been applied in chemistry, for example, the prediction of retention index of protein, and other QSAR studies. [13][14][15][16][17][18][19][20][21] Compared with traditional regression and neural networks methods, SVMs have some advantages, including global optimum, good generalization ability, simple implementation, few free parameters, and dimensional independence. [22][23][24] The flexibility in classification and ability to approximate continuous function make SVMs very suitable for QSAR and QSPR studies.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression

Yao

Panaye

Doucet

et al. 2004

J. Chem. Inf. Comput. Sci.

220

113

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Support vector machines (SVMs) were used to develop QSAR models that correlate molecular structures to their toxicity and bioactivities. The performance and predictive ability of SVM are investigated and compared with other methods such as multiple linear regression and radial basis function neural network methods. In the present study, two different data sets were evaluated. The first one involves an application of SVM to the development of a QSAR model for the prediction of toxicities of 153 phenols, and the second investigation deals with the QSAR model between the structures and the activities of a set of 85 cyclooxygenase 2 (COX-2) inhibitors. For each application, the molecular structures were described using either the physicochemical parameters or molecular descriptors. In both studied cases, the predictive ability of the SVM model is comparable or superior to those obtained by MLR and RBFNN. The results indicate that SVM can be used as an alternative powerful modeling tool for QSAR studies.

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“…Recent research, however, has suggested that the SVM is superior to the neural network (13)(14)(15). To verify this, and to test our method, we used a multilayer propagation (MLP) neural network model, as well as an SVM, to classify tumors in our experiments.…”

mentioning

confidence: 99%

Support Vector Machines for Diagnosis of Breast Tumors on US Images

et al. 2003

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Rationale and Objectives. Breast cancer has become the leading cause of cancer deaths among women in developed countries. To decrease the related mortality, disease must be treated as early as possible, but it is hard to detect and diagnose tumors at an early stage. A well-designed computer-aided diagnostic system can help physicians avoid misdiagnosis and avoid unnecessary biopsy without missing cancers. In this study, the authors tested one such system to determine its effectiveness. Materials and Conclusion.The SVM proved helpful in the imaging diagnosis of breast cancer. The classification ability of the SVM is nearly equal to that of the neural network model, and the SVM has a much shorter training time (1 vs 189 seconds). Given the increasing size and complexity of data sets, the SVM is therefore preferable for computer-aided diagnosis.

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Drug design by machine learning: support vector machines for pharmaceutical data analysis

Cited by 619 publications

References 14 publications

The Pharmacophore Kernel for Virtual Screening with Support Vector Machines

The Pharmacophore Kernel for Virtual Screening with Support Vector Machines

Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression

Support Vector Machines for Diagnosis of Breast Tumors on US Images

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