White Papers

Increase Research IT Productivity While Dramatically Reducing
R&D Support Costs Through Improved Scientific Data Management

In today’s science-driven industries, R&D departments are increasingly looking to IT departments to help them meet demanding innovation schedules. Companies selling consumer packaged goods, chemicals, materials, and pharmaceuticals count on their R&D teams to develop the breakthrough drugs, new paints, or other products that will drive business growth. And, like every other organizational department today, R&D counts on IT to implement and support the systems and solutions that enable it to improve process efficiencies and slash operational expenses.

The Accelrys Scientific Informatics Platform, Pipeline Pilot™, gives IT departments in R&D-driven enterprises a real advantage in their ability to help scientists and engineers meet the competitive demands that drive business growth. It provides the ability to tie together all the different data sources and types of data needed to answer the questions that drive excellent product innovation. Pipeline Pilot is architectured so that it is easily deployed on a global scale by IT to enable R&D to quickly and easily access, analyze and report on any data, regardless of where it resides or what format it is in.

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R&D Support Costs Through Improved Scientific Data Management

Materials Studio^® Benchmark Guide For Intel^® Xeon^® Based Clusters

Materials scientists and modelers often require high performance computing to manage the advanced computation and data volumes necessary to generate results in a timely and efficient manner. The performance tests presented in this white paper demonstrate that the latest advancements from Intel and Accelrys are transforming materials modeling and delivering shorter time-to-solution. Collaboration between Accelrys and Intel delivers results that combine hardware and software excellence.

This document provides a guideline for performance measurement of Materials Studio CASTEP, DMol³, and ONETEP, running on a range of Intel-based servers. The performance for processors from the Intel Xeon Processor sequence, including the latest Nehalem processors, is compared to using several interconnects. Benchmark data for typical materials calculations, using MS CASTEP and MS DMol³ in Materials Studio 5.0 and MS ONETEP in Materials Studio 4.4, are provided.
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Facilitating Rapid Analysis and Decision Making in the Analytical Lab

Competitive success requires research and development labs to make fast, accurate decisions about compounds and synthesis routes. Increasing amounts of data available in different formats from wide ranging sources make data collection, analysis, reporting, and distribution increasingly cumbersome, limiting, and time consuming. Automating each step of the process provides some assistance, but only by a complete integration of activities and resulting data can labs make the kind of improvements required to expedite results.

The Analytical Data Management solution from Accelrys provides such an integrated approach to laboratory process flow and analysis automation. The Accelrys solution manages a broad range of information, beginning with outputs from laboratory equipment to industry standard data stores. It compares and analyzes results, linking with the sophisticated processes of best-of-breed analytical tools, generates reports tailored for each level of audience throughout the organization, and presents the resulting information in portals or dashboards to ensure consistency and currency. This solution is helping Research & Development (R&D) organizations to make better informed decisions, faster and cheaper.
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Environmental Chemistry and Toxicology

This paper describes the use of a centralized data management system that allows manufacturing organizations to effectively manage regulatory compliance. The Accelrys Environmental Chemistry and Toxicology (ECT) Solution is a customizable “scientifically-aware” platform to streamline monitoring, analysis, and reporting of chemical by-products for the purposes of regulatory compliance. This scientifically-aware solution inherently allows for QSAR analysis, chemical similarity searching, and other read-across techniques to maximize the value of all data. The scientific platform can easily be configured to automatically predict biodegradation products, physical properties, ADME and Toxicology properties, and many other descriptors. The solution can also be configured to automatically compare any of these properties to codified business rules, specific for each facility. These, and many other capabilities can automatically be presented on interactive dashboards or on REACH, MSDS or other formal documentation.
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Scientific Business Intelligence™ Solutions

One of the major challenges facing the scientific and clinical research communities is the inability to access, aggregate, and mine scientific data across an enterprise organization. Data remains locked in silos, causing productivity and decision making to suffer, and ultimately impeding an organization's research efficiency. By successfully harnessing disparate data sources and software applications, organizations position themselves to apply their data in an intelligent, focused manner, thereby improving productivity, decision making and research efficiency. However, such efficiencies have eluded many scientific and clinical research organizations because traditional business intelligence (BI) technologies cannot handle advanced scientific data processing and analysis. This lack of a readily available BI solution has forced organizations to make do with vendor-specific solutions, drain in-house development resources by building customized solutions, or struggle with sharing data among incompatible point products. Accelrys' proven history of delivering innovative modeling, simulation and informatics software for life and materials sciences, as well as its open and standards-based scientific operating platform, positions it to deliver scientifically-relevant BI solutions that meet the needs of the scientific and clinical research communities.
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Measuring the Return on Modeling and Simulation Tools in Pharmaceutical Development

Based on in-depth interviews with research scientists in pharmaceutical development, market research firm IDC concludes that there is a significant return on investment (ROI) to be realized from the use of modeling and simulation software tools. The research was based on conversations with researchers at major pharmaceutical companies and academia, and suggests a cumulative ROI of the order of $3–$10 for every $1 invested in these tools.
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Modeling and Simulation: The Return on Investment in Materials Science – an IDC Chemicals Industry White Paper

Market research firm IDC’s recent study concluded that for each dollar invested in modeling and simulation software and its support infrastructure, $3 to $9 was returned to the company in incremental revenue and costs savings.
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Toward Nanomaterials by Design: A Rational Approach for Reaping Benefits in the Short and Long Term

Scott Mize, President of the Foresight Institute (www.foresight.org), describes a rational approach to Nanomaterials by Design and explains why nanotechnology companies must adopt it to most effectively and efficiently discover, develop, and manufacture new products. At the core of Rational Nanomaterials Design are modeling, simulation, and informatics software tools, which have been demonstrated to reduce development costs, speed time to market, and enable designers to develop better materials with a greater focus on end-user application requirements.
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High Performance Computing with IBM

With the average cost of a new small molecule drug hovering at $800 million today vs. $200 million in 1987 [1], pharmaceutical and biotech companies are in urgent need of innovations to improve profitability. The problem may be attributed to the deteriorating rate of compounds in Phase I of clinical development reaching the market, which CMR International estimated to be at 23% in 1994-1996, 15% in 1995-1997, and 11% in 1996-1998 [2]. To improve this rate, pharmaceutical and biotech companies turn to informatics (bioinformatics and cheminformatics included), that help scientists run more virtual experiments, modeling and simulation than ever before. This, in turn, drives the need for scalable and efficient data management and analysis solutions.

This white paper discusses an implementation of a data pipelining analysis platform that caters to a large number of users in an environment with multiple, heterogeneous databases as described above. The work is a collaboration between SciTegic and IBM, and includes benchmarking results of this solution and its configuration. PDF format.
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Compound Acquisition using Pipeline Pilot

With the advent of high-throughput screening, life science companies have the capacity to routinely screen hundreds of thousands to millions of compounds in the laboratory in a very short time. An immediate problem is the difficulty in ensuring that a large, high quality collection of compounds is available to match the screening capacity - many smaller and newer companies do not posses a significant in-house compound collection. Even at the largest pharmaceutical companies, historical compound collections typically do not contain a sufficient number of compounds to keep the screens busy. In addition, these historical collections may suffer from issues of compound quality and diversity. Two approaches have been taken to fill the screening pipeline - the acquisition of compounds from external collections, and the use of high throughput combinatorial chemistry to produce large numbers of compounds. This application note focuses on using Pipeline Pilot in the compound acquisition process. PDF format.
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Enumerating Combinatorial Libraries using Pipeline Pilot

Combinatorial chemistry techniques are a routine part of lead generation and lead optimization. With the capability to synthesize large numbers of compounds and the use of automated synthesis, computational methods are an integral part of the combinatorial chemistry process. A number of computational tasks are important in the preparation of combinatorial libraries, including:

• Identification and selection of reagents
• Computational definition of virtual libraries for design, or real libraries for synthesis, and the enumeration of the compounds within those libraries
• Subset selection from a virtual library: diverse selections for lead generation or focused selection for lead optimization

This application note focuses on the first two tasks. PDF format.

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