Distinct biological network properties between the targets of natural products and disease genes.

We show that natural products target proteins with a high number of protein-protein functional interactions (high biological network connectivity) and that these protein targets have higher network connectivity than disease genes. This feature may facilitate disruption of essential biological pathways, resulting in competitor death. This result also suggests that additional sources of small molecules will be required to discover drugs targeting the root causes of human disease in the future.

ChemBank: a small-molecule screening and cheminformatics resource database.

ChemBank (http://chembank.broad.harvard.edu/) is a public, web-based informatics environment developed through a collaboration between the Chemical Biology Program and Platform at the Broad Institute of Harvard and MIT. This knowledge environment includes freely available data derived from small molecules and small-molecule screens and resources for studying these data. ChemBank is unique among small-molecule databases in its dedication to the storage of raw screening data, its rigorous definition of screening experiments in terms of statistical hypothesis testing, and its metadata-based organization of screening experiments into projects involving collections of related assays. ChemBank stores an increasingly varied set of measurements derived from cells and other biological assay systems treated with small molecules. Analysis tools are available and are continuously being developed that allow the relationships between small molecules, cell measurements, and cell states to be studied. Currently, ChemBank stores information on hundreds of thousands of small molecules and hundreds of biomedically relevant assays that have been performed at the Broad Institute by collaborators from the worldwide research community. The goal of ChemBank is to provide life scientists unfettered access to biomedically relevant data and tools heretofore available primarily in the private sector.

Connecting Small Molecules with Similar Assay Performance Profiles Leads to New Biological Hypotheses.

High-throughput screening allows rapid identification of new candidate compounds for biological probe or drug development. Here, we describe a principled method to generate "assay performance profiles" for individual compounds that can serve as a basis for similarity searches and cluster analyses. Our method overcomes three challenges associated with generating robust assay performance profiles: (1) we transform data, allowing us to build profiles from assays having diverse dynamic ranges and variability; (2) we apply appropriate mathematical principles to handle missing data; and (3) we mitigate the fact that loss-of-signal assay measurements may not distinguish between multiple mechanisms that can lead to certain phenotypes (e.g., cell death). Our method connected compounds with similar mechanisms of action, enabling prediction of new targets and mechanisms both for known bioactives and for compounds emerging from new screens. Furthermore, we used Bayesian modeling of promiscuous compounds to distinguish between broadly bioactive and narrowly bioactive compound communities. Several examples illustrate the utility of our method to support mechanism-of-action studies in probe development and target identification projects.

Master data management: getting your house in order.

The availability of high-throughput techniques combined with more exploratory and confirmatory studies in small-molecule science (e.g., probe- and drug-discovery) creates a significant need for structured approaches to data management. The probe- and drug-discovery scientific processes start and end with lower-throughput experiments, connected often by high-throughput cheminformatics, screening, and small-molecule profiling experiments. A rigorous and disciplined approach to data management ensures that data can be used to ask complex questions of assay results, and allows many questions to be answered computationally, without the need for significant manual effort. A structured approach to recording scientific experimental design and observations involves using a consistently maintained set of 'master data' or 'metadata'. Master data include sets of tightly controlled terminology used to describe an experiment, including both materials and methods. Master data can be used at the level of an individual laboratory or with a scope as extensive as a whole community of scientists. Consistent use of master data increases experimental power by allowing data analysis to connect all parts of the discovery life cycle, across experiments performed by different researchers and from different laboratories, thus decreasing the opportunity cost for making novel connections between results. Despite the promise of this increased experimental power, challenges remain in implementation and consistent use of master data management (MDM) techniques in the laboratory. In this paper, we discuss how specific MDM techniques can enhance the quality and utility of scientific data at a project, laboratory, and institutional level. We present a model for storage and exploitation of master data, practical applications of these techniques in the research context of small-molecule science, and specific benefits of MDM to small-molecule screening aimed at probe- and drug-discovery.