In vitro affinity fingerprints

This approach was first introduced by Weinstein et al. [8,9] at the National Cancer Institute (NCI) and Kauvar et al. [10] at Terrapin and pursued by Dixon and Villar [11] at Telik. Here, an affinity fingerprint for a compound is defined as a vector of its binding affinities against a panel of uncorrelated reference proteins. The underlying assumption behind the affinity fingerprint idea is that binding properties of the ligands in the reference panel can be extrapolated in order to predict affinities to a new target of interest.

The researchers at Terrapin have subsequently used the affinity fingerprints much like the components of a multivariate QSAR calculation [10]. For each new target, a QSAR model is calculated based on a small training set. This equation, which can be viewed as the computational surrogate of the target, is then taken to predict affinities for the remainder of the data set.

The researchers in Weinstein's group at NCI have profiled more than 60 000 compounds by measuring their 50% growth-inhibitory concentration (GI50)

against a panel of 60 human cancer cell lines. They have used the resulting fingerprints in a neural network approach to classify compounds retrospectively with respect to their mechanisms of action [8]. Moreover, the activity patterns were applied in conjunction with structural descriptors for virtual screening of the complete NCI database in order to find new anticancer drugs

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