Industry Offerings

Scientific Innovation solution

The pharmaceutical and biotech industry continuously enhances its research process by introducing new methodologies or scientific hypotheses to discover novel drug candidates. Changes in the scientific process make it imperative to integrate, visualize, analyze, and share complex biological and chemical information.

The Infosys Scientific Innovation solution addresses the need for scientists to manage information for drug discovery better. Our solution framework is designed to support systems for early stage ideation, design of projects, and undertaking experiments to test hypotheses. The framework includes reusable components such as visualization, search, integration, and analytical components to address the challenges of pharmaceutical information. It reduces the time spent by scientists on ‘data dredging’ and interpreting findings.

Research challenges

Heterogeneous data formats
The experimental results stored in heterogeneous data formats make scientific analysis inefficient and time consuming. Scientists spend more time in identifying and converting data into requisite formats before it is fit for analysis. Our solution helps scientists arrive at research milestones faster.

Large volumes of multidimensional data

Research laboratories generate a huge volume of scientific data to extract insights and inferences. The analysis and annotation of extensive research information becomes labor-intensive without the assistance of graphical and visualization tools. Our solution helps you achieve milestones and deliver results.

Biology and chemistry research work in silos

Traditional chemistry research and emerging biology research function in their respective silos. There is lack of collaboration of results in repeated experiments and research redundancies. Our solution bridges research information among research groups.

Multiple workflows in practices

Distributed laboratories practice similar but not exactly the same methods related to research. It can hinder distribution, reuse, and adoption of best practices across the enterprise. Our solution harmonizes methods and adopts common standards across groups.

Solution framework

Infosys has developed a service-oriented knowledge collaboration platform with role-based access to better manage and predict scientific innovation.

Our solution aggregates various data services and warehouses and semantically joins them. We have built adapters to cross-connect data of various formats and developed a middleware service layer. We have harmonized key functional and transactional processes to be reused by scientists from various disciplines.

The Web parts are designed to execute a composition of scientific tasks. They are seamlessly accessible through an interface presented by Microsoft Office SharePoint Server (MOSS). One of the Web parts includes visual rendering of sensitivity analysis on TIBCO Spotfire graphs. The components help scientists manage scientific information from ideating to developing a proof-of-concept.

The key components of our solution framework include:

  • Idea Engine: Inventory management of ideas and associated hypothesis
  • Visual Query Builder: Semantically informed searches across multiple disciplines
  • Biological Registration: Register, modify, search, track, and request biological entities
  • Kinase Wiki: Thematic reference knowledge page that can be collaboratively improved
  • Visual Analytics: Visual rendering of data to extract insights from assay results
  • Ligand Identification and Matching tool: Virtual predictive screening of chemical homologues
  • Electronic Whiteboard: Allows scientists to develop ideas in a free-flowing approach
  • Grid Workflow Manager (GridWorM): Reusable computing workflow to distribute batch of routine analytical tasks

Solution benefits

The Infosys Scientific Innovation solution offers several benefits:

  • Data is accessible to scientists virtually from any source in a comprehensible format
  • Intuitive and guided visualizations make it easier to extract insights from large volumes of data
  • Teams contribute their knowledge to reference a common thematic repository
  • Reusable methods and workflows are accessible to groups of users for reuse and configuration
  • Combines various moving parts in science as a composite set of Web parts in a monolithic interface
  • Experimental results can be monitored from a program performance
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