An integrated data management framework for drug discovery--from data capturing to decision support.

Drug discovery is a highly complex process requiring scientists from wide-ranging disciplines to work together in a well-coordinated and streamlined fashion. While the process can be compartmentalized into well-defined functional domains, the success of the entire enterprise rests on the ability to exchange data conveniently between these domains, and integrate it in meaningful ways to support the design, execution and interpretation of experiments aimed at optimizing the efficacy and safety of new drugs. This, in turn, requires information management systems that can support many different types of scientific technologies generating data of imposing complexity, diversity and volume. Here, we describe the key components of our Advanced Biological and Chemical Discovery (ABCD), a software platform designed at Johnson & Johnson to bring coherence in the way discovery data is collected, annotated, organized, integrated, mined and visualized. Unlike the Gordian knot of one-off solutions built to serve a single purpose for a single set of users that one typically encounters in the pharmaceutical industry, we sought to develop a framework that could be extended and leveraged across different application domains, and offer a consistent user experience marked by superior performance and usability. In this work, several major components of ABCD are highlighted, ranging from operational subsystems for managing reagents, reactions, compounds, and assays, to advanced data mining and visualization tools for SAR analysis and interpretation. All these capabilities are delivered through a common application front-end called Third Dimension Explorer (3DX), a modular, multifunctional and extensible platform designed to be the "Swiss-army knife" of the discovery scientist.

Efficient substructure searching of large chemical libraries: the ABCD chemical cartridge.

Efficient substructure searching is a key requirement for any chemical information management system. In this paper, we describe the substructure search capabilities of ABCD, an integrated drug discovery informatics platform developed at Johnson & Johnson Pharmaceutical Research & Development, L.L.C. The solution consists of several algorithmic components: 1) a pattern mapping algorithm for solving the subgraph isomorphism problem, 2) an indexing scheme that enables very fast substructure searches on large structure files, 3) the incorporation of that indexing scheme into an Oracle cartridge to enable querying large relational databases through SQL, and 4) a cost estimation scheme that allows the Oracle cost-based optimizer to generate a good execution plan when a substructure search is combined with additional constraints in a single SQL query. The algorithm was tested on a public database comprising nearly 1 million molecules using 4,629 substructure queries, the vast majority of which were submitted by discovery scientists over the last 2.5 years of user acceptance testing of ABCD. 80.7% of these queries were completed in less than a second and 96.8% in less than ten seconds on a single CPU, while on eight processing cores these numbers increased to 93.2% and 99.7%, respectively. The slower queries involved extremely generic patterns that returned the entire database as screening hits and required extensive atom-by-atom verification.

Advanced biological and chemical discovery (ABCD): centralizing discovery knowledge in an inherently decentralized world.

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.

A cluster-based strategy for assessing the overlap between large chemical libraries and its application to a recent acquisition.

We report on the structural comparison of the corporate collections of Johnson & Johnson Pharmaceutical Research & Development (JNJPRD) and 3-Dimensional Pharmaceuticals (3DP), performed in the context of the recent acquisition of 3DP by JNJPRD. The main objective of the study was to assess the druglikeness of the 3DP library and the extent to which it enriched the chemical diversity of the JNJPRD corporate collection. The two databases, at the time of acquisition, collectively contained more than 1.1 million compounds with a clearly defined structural description. The analysis was based on a clustering approach and aimed at providing an intuitive quantitative estimate and visual representation of this enrichment. A novel hierarchical clustering algorithm called divisive k-means was employed in combination with Kelley's cluster-level selection method to partition the combined data set into clusters, and the diversity contribution of each library was evaluated as a function of the relative occupancy of these clusters. Typical 3DP chemotypes enriching the diversity of the JNJPRD collection were catalogued and visualized using a modified maximum common substructure algorithm. The joint collection of JNJPRD and 3DP compounds was also compared to other databases of known medicinally active or druglike compounds. The potential of the methodology for the analysis of very large chemical databases is discussed.

Comparison of automated docking programs as virtual screening tools.

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.

Docking: successes and challenges.

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.

Decrypting the biochemical function of an essential gene from Streptococcus pneumoniae using ThermoFluor technology.

The protein product of an essential gene of unknown function from Streptococcus pneumoniae was expressed and purified for screening in the ThermoFluor affinity screening assay. This assay can detect ligand binding to proteins of unknown function. The recombinant protein was found to be in a dimeric, native-like folded state and to unfold cooperatively. ThermoFluor was used to screen the protein against a library of 3000 compounds that were specifically selected to provide information about possible biological functions. The results of this screen identified pyridoxal phosphate and pyridoxamine phosphate as equilibrium binding ligands (K(d) approximately 50 pM, K(d) approximately 2.5 microM, respectively), consistent with an enzymatic cofactor function. Several nucleotides and nucleotide sugars were also identified as ligands of this protein. Sequence comparison with two enzymes of known structure but relatively low overall sequence homology established that several key residues directly involved in pyridoxal phosphate binding were strictly conserved. Screening a collection of generic drugs and natural products identified the antifungal compound canescin A as an irreversible covalent modifier of the enzyme. Our investigation of this protein indicates that its probable biological role is that of a nucleoside diphospho-keto-sugar aminotransferase, although the preferred keto-sugar substrate remains unknown. These experiments demonstrate the utility of a generic affinity-based ligand binding technology in decrypting possible biological functions of a protein, an approach that is both independent of and complementary to existing genomic and proteomic technologies.