Increasing market and regulatory demands for safety and efficacy are driving pharmaceutical R&D to improve upstream discovery efforts. A primary area of this improvement is focused on target identification, with growing amounts of data supporting early decision making. However, wrangling this massive pile of data to arrive at actionable insights remains a challenge.
Identifying potential targets for developing new drugs is the first major step in the arduous quest for curing a disease. With the high attrition rates involved in the drug development process, judicious science-based decisions are essential to minimize costly failures. Scientists use multiple approaches and technology platforms to understand the cellular, molecular, biochemical complexities associated with a disease. These approaches heavily rely on high throughput omics-datasets such as genome wide association scans, gene expression analysis from tissue to a single cell, miRNA analysis, proteomics, post-translational modifications, cellular imaging to name a few. For a holistic analysis, information from several multi-dimensional datasets (Omics, text and image analytics, public databases, systems biology) needs to be integrated. AI and machine learning technologies are gaining popularity in extracting knowledge from a multitude of resources and thus enabling precision medicine.
This talk will focus on BIOVIA’s tool sets that will empower scientists to harmonize and cogently retrieve the valuable information from numerous datasets.