title: Prediction of 'drug-likeness'.
abstract: Recent developments in combinatorial chemistry and high-throughput screening have dramatically increased the scale on which drug discovery programs are carried out. Along with these advances has come a need for automated methods of determining which compounds from a library should be synthesized and screened. These methods range from simple counting schemes to sophisticated machine learning techniques such as neural networks. While many of these methods have performed well in validation studies, the field is still in its formative stage. This paper reviews a number of computational techniques for identifying drug-like molecules and examines challenges facing the field.
authors.affiliation: Vertex Pharmaceuticals, 130 Waverly Street, 02139, Cambridge, MA 02139, USA. pwalters@vpharm.com
authors.forename: W Patrick
authors.initials: WP
authors.lastname: Walters
authors.forename: Mark A
authors.initials: MA
authors.lastname: Murcko
mesh_terms: D001185:Artificial Intelligence; D020650:Combinatorial Chemistry Techniques; D019295:Computational Biology; D016208:Databases, Factual; D008958:Models, Molecular; D010600:Pharmacology / Q000379:methods*; D012107:Research Design