Flexible QSAR: functional machine learning in computational chemistry

author: Ignasi Belda, Intelligent Pharma
published: Nov. 16, 2010,   recorded: September 2010,   views: 4106


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QSAR (Quantitative Structure-Activity Relationship) modelling is a usual step in drug discovery. QSAR methods use statistical and machine learning tools to draw out the significant relationships between the molecular structure of the drug candidates (the molecules) and its biological profile. To achieve such a goal, researchers usually describe the molecules with arrays of physico-chemical properties, such as total molecular charge, molecular weight, number of hydrogen bonds donors, etc. However, the predictive accuracy of statistical and machine learning tools in QSAR have been typically very low and more advanced tools are needed to achieve higher degrees of usage of QSAR drug discovery processes. For such a reason, at Intelligent Pharma, we have been researching in the field of functional data mining, that is, data mining of information described through functions, not only with fixed properties. By using functional data mining approaches, we can deal with physico-chemical parameters such as the volume of the molecules, which is a variable property that varies depending on the energy of the system and its flexibility. Therefore, more accurate predictive models can be built by using these approaches in the field of QSAR and drug discovery. The machine learning tool used in this research is support vector machines.

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