A simple pharmacophore point filter has been developed that discriminates between drug-like and nondrug-like chemical matter. It is based on the observation that nondrugs are often underfunctionalized. Therefore, a minimum count of well-defined pharmacophore points is required to pass the filter. The application of the filter results in 66-69% of subsets of the MDDR database to be classified as drug-like. Furthermore, 61-68% of subsets of the CMC database are classified as drug-like. In contrast, only 36% of the ACD are found to be drug-like. While these results are not quite as good as those obtained with recently described neural net approaches, the method used here has clear advantages. In contrast to a neural net approach and also in contrast to decision tree methods described recently, the pharmacophore filter has been developed by using "chemical wisdom" that is unbiased from fitting the structural content of specific drug databases to prediction models. Similar to decision tree methods, the pharmacophore point filter provides a detailed structural reason for the classification of each molecule as drug or nondrug. The pharmacophore point filter results are compared to neural net filter results. A statistically significant overlap between compounds recognized as drug-like validates both approaches. The pharmacophore point filter complements neural net approaches as well as property profiling approaches used as drug-likeness filters in compound library analysis and design.
We have used a Bayesian neural network to distinguish between drugs and nondrugs. For this purpose, the CMC acts as a surrogate for drug-like molecules while the ACD is a surrog...
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