The performance of several commercially available docking programs is compared in the context of virtual screening. Five different protein targets are used, each with several known ligands. The simulated screening deck comprised 1000 molecules from a cleansed version of the MDL drug data report and 49 known ligands. For many of the known ligands, crystal structures of the relevant protein-ligand complexes were available. We attempted to run experiments with each docking method that were as similar as possible. For a given docking method, hit rates were improved versus what would be expected for random selection for most protein targets. However, the ability to prioritize known ligands on the basis of docking poses that resemble known crystal structures is both method- and target-dependent.

The state of the art of various computational aspects of docking-based virtual screening of database of small molecules is presented. The review encompasses the different search algorithms and the scoring functions used in docking methods and their applications to protein and nucleic acid drug targets. Recent progress made in the development and application of methods to include target flexibility are summarized. The fundamental issues and challenges involved in comparing various docking methods are discussed. Limitations of current technologies as well as future prospects are presented.

A genetic algorithms (GA) based strategy is described for the identification or optimization of active leads. This approach does not require the synthesis and evaluation of huge libraries. Instead it involves iterative generations of smaller sample sets, which are assayed, and the “experimentally” determined biological response is used as an input for GA to rapidly find better leads. The GA described here has been applied to the identification of potent and selective stromelysin substrates from a combinatorial-based population of 206 or 64 000 000 possible hexapeptides. Using GA, we have synthesized less then 300 unique immobilized peptides in a total of five generations to achieve this end. The results show that each successive generation provided better and unique substrates. An additional strategy of utilizing the knowledge gained in each generation in a spin-off SAR activity is described here. Sequences from the first generations were evaluated for stromelysin and collagenase activity to identify stromelysin-selective substrates. GlyProSerThr-TyrThr with Tyr as the P1‘ residue is such an example. A number of peptides replacing Tyr with unusual monomers were synthesized and evaluated as stromelysin substrates. This led to the identification of Ser(OBn) as the best and most selective P1‘ residue for stromelysin.

We present ABCD, an integrated drug discovery informatics platform developed at Johnson & Johnson Pharmaceutical Research & Development, L.L.C. ABCD is an attempt to bridge multiple continents, data systems, and cultures using modern information technology and to provide scientists with tools that allow them to analyze multifactorial SAR and make informed, data-driven decisions. The system consists of three major components: (1) a data warehouse, which combines data from multiple chemical and pharmacological transactional databases, designed for supreme query performance; (2) a state-of-the-art application suite, which facilitates data upload, retrieval, mining, and reporting, and (3) a workspace, which facilitates collaboration and data sharing by allowing users to share queries, templates, results, and reports across project teams, campuses, and other organizational units. Chemical intelligence, performance, and analytical sophistication lie at the heart of the new system, which was developed entirely in-house. ABCD is used routinely by more than 1000 scientists around the world and is rapidly expanding into other functional areas within the J&J organization.