Whether from natural or artificial sources, odorants and flavorants enhance the appeal of a product. Judicious application of an appropriate odorant can turn a lack luster recipe into a winner. With the properties of these ingredients so notoriously complex, how can we best understand the stability of formulated products, the consumer appeal of the active odorants, or the absorption of actives by skin?
Ultimately, the sensory appeal of these ingredients depends on molecular attributes. Ideally, we’d like to be able to predict the sensory properties of any molecule; or to design a new molecule with a specific odor or flavor. Consumer goods companies use flavors and fragrances to leverage, enhance and expand their product appeal to drive competitive advantage. Yet the complexity of understanding key variables like formulation stability, toxicity, or how to ensure that the result will be attractive to consumers has traditionally required many experiments, which can ratchet up costs and delay time-to-market. To complicate matters, consumer product formulations are also subject to increasingly stringent regulatory control and greater public scrutiny.
Computational approaches, such as predictive modeling, provide a rational approach for the discovery of new compounds based on molecular attributes. But did you know that data integration and predictive analytics offer ways to improve and dramatically speed up the R&D discovery process on the basis of experimental data that you have in-house right now?
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Scientific Data integration may seem like a simple concept, but it’s actually quite complex because of the wide diversity of data formats, the sheer volume of information and the changing number of instruments that need to be accommodated; as well as by the many different locations, both internal and external to the R&D organization, where research intelligence may be hidden. Researchers can easily spend countless hours finding needed information, preparing data for analysis, then collating, formatting and distributing results—time that could be more profitably spent on product innovation.
An integrated approach to tracking organizational data and knowledge enables companies to build on the valuable, yet time consuming, research they’ve already invested in, rather than start from scratch with every project. When individual contributors can more easily archive, search, and compare information from past experiments with current projects, huge efficiency gains are made.
Tools like Accelrys Pipeline Pilot ™ provide a simple way to integrate data from multiple sources and make it available to end users in a convenient and familiar format, like a web browser. Easily configured, these reports also reduce the amount of IT effort required to generate custom reports.
Predictive analytics provides a way of extracting the most information you can from your experimental results. Data integration puts the information at your fingertips; predictive analytics lets you make extrapolations to virtual compounds, or mixtures, or formulations.
This technique is particularly useful for designing new formulations or specific ingredients on the basis of the available data. Models based on your data can be used to screen thousands of virtual formulations or chemical substances, with the goal of finding the optimum combination of ingredients. Only the most promising leads are then subjected to experimental screening, greatly reducing the number of laboratory experiments required—and saving a lot of time.
Rarely is it possible to optimize all the desirable features of your product. Trade offs need to be made among variables like cost, shelf life, consumer appeal, and so on. Predictive analytics delivers an unambiguous way to quantify these trade offs, and deliver a product that is best-suited to you customers.
Predictive science uses sophisticated theoretical methods to compute properties when experimental data sets are unavailable. Used widely in chemical and pharmaceutical research, software-enabled scientific modeling makes it possible for researchers to screen virtual leads for properties like odor intensity, shelf life, and toxicity – entirely without recourse to experimental data. Use this approach to help optimize a combinatorial library of leads; or to explore entirely new sets of compounds where experimental data is unavailable. With these kinds of tools, researchers can quickly narrow the search for promising leads before running costly and time consuming trial and error-type experiments in the lab.
Tools such as Accelrys Materials Studio simplify the task of predicting the properties of materials including molecules, polymers, and mixtures. Combined with Pipeline Pilot, these predictive tools can be customized to specific research projects and used for rapid screening of leads.
The combination of data integration, predictive analytics, and predictive science offered by Pipeline Pilot and Materials Studio represents a powerful set of tools that can drive dramatic innovation productivity and time to market improvements while helping to optimize product margins.
Accelrys’ applications allow companies to:
- Leverage years of existing research and intellectual property data.
- Quickly and easily extract knowledge, compare results, or perform scenario testing.
- Effectively link scientific intelligence with consumer behavior models.
- Normalize data to dramatically streamline the complex task of organizing and adding context to experiments and results.
- Leverage existing knowledge, and as a result reduce the number of experiments, or focus on experiments that have a greater chance of success.
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