Different requirements for predictive models at different stages of drug design cycle
In a typical qSAR, it is usually assumed that sensitivity and specificity of the predictive model are equally important. However, in a drug discovery project, these accuracies may have different importance at different stages of the design cycle. For example, in the initial target and hit identification phase, it may be more important not to miss potential leads. Thus, it is more important to have a predictive model which has very high sensitivity (small number of false negatives) and reasonably good specificity. At later stages, it becomes increasingly important to focus on a manageable number of candidates. Thus a predictive model with very high specificity (small number of false positives) and reasonably good sensitivity may become more important. Hence it is important to be able to modify the modeling method or predictive model so as to meet this two types of requirements.
For SVM classification systems, there are two possible approaches for modifications to suit these different needs. The first approach uses different training error penalties for compounds in positive and negative classes. For example, a higher training error penalty for compounds in positive class and lower training error penalty for compounds in negative class can be used to increase the sensitivity of the SVM classification systems. The second approach adds a correction factor to the SVM decision function. A positive or negative correction factor will improve the sensitivity or specificity of the SVM classification system respectively.
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