Drug Design by Machine Learning: Support Vector Machines for Pharmaceutical Data Analysis

By Robert Burbidge, Matthew W. B. Trotter, Bernard F. Buxton, Sean B. Holden. Computers and Chemistry, 2001

656 citations recorded by the source corpus.

We show that the support vector machine (SVM) classification algorithm, a recent development from the machine learning community, proves its potential for structure-activity relationship analysis. In a benchmark test, the SVM is compared to several machine learning techniques currently used in the field. The classification task involves predicting the inhibition of dihydrofolate reductase by pyrimidines, using data obtained from the UCI machine learning repository. Three artificial neural networks, a radial basis function network, and a C5.0 decision tree are all outperformed by the SVM. The SVM is significantly better than all of these, bar a manually capacity-controlled neural network, which takes considerably longer to train.

Subfields: Analytical Chemistry, Computational Theory and Mathematics, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/ae73fa99d777efd07ed5a73cdc695191862f9d9e

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In Analytical Chemistry

In Computational Theory and Mathematics

In Spectroscopy

Rampi Ramprasad

Citations in the corpus

Machine learning in manufacturing: advantages, challenges, and applications (2016, background): “Burbidge, Trotter, Buxton, and Holden (2001) found SVM to be a ‘robust and highly accurate intelligent classification technique well suited for structure–activity relationship analysis.’”
Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders (2013, background): “Computationally, finding the best location for the decision plane is an optimization problem to facilitate a kernel function to create linear boundaries through non-linear transformations [36–38].”
Predicting defect-prone software modules using support vector machines (2008, background): “, 2005), cheminformatics and bioinformatics (Cai et al., 2002; Burbidge et al., 2001; Morris et al., 2001; Lin et al., 2003; Bao and Sun, 2002), and financial time series forecasting (Tay and Cao, 2001).”
EEG signal classification using PCA, ICA, LDA and support vector machines (2010, neutral): “Details are given (Abe, 2005; Burbidge et al., 1998; Burges, 1998; Cortes & Vapnik, 1995; Duda et al., 2001; Fielding, 2007; Van der Heijden, Duin, de Ridder, & Tax, 2004; Vapnik, 1995).”
A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data (2012, background): “SVM represents a class of statistical learning algorithms that have been widely used in bioinformatics and chemometrics due to its remarkable generalization performance in managing linearly non-separable problems [29–31].”
Financial time series forecasting using independent component analysis and support vector regression (2009, positive): “Due to the advantages of the generalization capability in obtaining a unique solution, the SVMs have drawn the attention of researchers and have been applied in many applications such as texture classification, image recognition, data mining and bioinformatics [6, 13, 22, 32, 35, 44, 46, 50].”