title: Pharmacophore features of potential drugs.
abstract: Drug discovery efforts rely increasingly on the identification of quality lead compounds through high-throughput synthesis and screening. However, large-scale random libraries have yielded only a low number of quality lead molecules. To address this shortcoming researchers have paid more attention to the concept of "drug-likeness" of molecules in combinatorial and screening libraries. Database profiling and analysis methods have been employed to identify the structural features of known drug molecules. Neural networks and machine learning methods help to distinguish between drugs and nondrugs. More recently, database-independent pharmacophore filters have been introduced that provide simple intuitive rules to classify potential drugs.
authors: Bayer Research Center, 400 Morgan Lane West Haven, CT 06516, USA. ingo.mugge.b@bayer.com
authors: Ingo
authors: I
authors: Muegge
mesh_terms: D020650:Combinatorial Chemistry Techniques; D016208:Databases, Factual; D015195:Drug Design; D006801:Humans; D015394:Molecular Structure