3D Ligand-Based Virtual Screening with Support Vector Machines

Computational models play an important role in early-stage drug discovery, in particular for lead identification and optimization. Starting from a list of molecules with experimentally determined binding affinity to a particular therapeutic target, as typically obtained by high-throughput screening (HTS), the goal of lead optimization is to find additional molecules with good binding affinity. The resulting leads are then further optimized, in particular to improve their pharmacokinetical and toxicological profiles, eventually leading to new candidate drugs. Lead identification and optimization are usually performed by screening large databanks of small molecules, e.g., created by combinatorial chemistry, to find active molecules. Since experimental screening remains costly and timeconsuming when large banks are concerned, and given the immensity of the space of small molecules which may be synthesized, in silico screening provides an interesting complementary approach to identify active molecules. An in silico screening is based on a model which can predict the activity of candidate molecules from their structure. Two general classes of models are often used. First, if the 3D structure of the target is known, then docking models predict whether AbsTRACT