• Improving the understanding of critical properties of antibodies as early as possible in development and formulation will impact speed and total cost to market. When 3D structures are not available from X-RAY or NMR, 3D in silico models generated are the solution.

  • The diversity of the pathogens implicated in NTDs, the relative complexity of their life cycles and their poor tractability in the lab have resulted in a lack of potential therapeutic targets and candidates. By repurposing existing high throughput screening (HTS) data for Ebola and Chagas Disease to create machine learning models which could screen libraries for potential active compounds, especially compounds previously approved by the US FDA for other diseases, they were able to provide safe and efficacious treatments for those who need them most.

  • Learn how having the right predictive sciences capabilities integrated into R&D work flows delivers a scientific decision support environment that reduces time and expense, improves quality, enhances collaboration and accelerates innovation in bringing new drugs to market.

  • A multinational pharmaceutical company with world-wide commercial operations provides contract manufacturing, product development and clinical services to third-party customers. Geographically-dispersed and disconnected business units demanded virtual collaborations that were not easy to support, and many processes throughout the company were manual and disconnected, hindering productivity and collaboration. The company implemented a solution to enable them to deliver high quality, compliant products in an efficient manner.