A novel approach for Big Data Analytics for applications in drug design and drug side effect predictions.
An unprecedented explosion in the amount of data generated at every stage of drug design and discovery has been witnessed in the past few years. The objective of our project is to investigate the possible opportunities associated with the substantial rise in data volume due to the increasing data sources. Being able to cope with the challenges of integrating big data from various sources such as Next-Generation Sequencing, virtual compounds, and clinical notes, and of uncovering hidden patterns in such big data can break critical bottlenecks and lead to a potentially huge impact on humankind.
- Special Features and Advantages
The evolutionary approach to evolving design.
Cloud computing and big data analytics techniques to evaluate the feasibility of the design.
Algorithm substantiates the relationship between drug structure and side effects.
Potential for new drug development with minimal cost and time.
Screens large-scale candidate compounds and predicts drug-target interactions in the initial phase.
Predicts drug side-effects and re-purposes approved drug based on massive clinical notes in the next phase.
Implements next-generation sequencing for personalized medicine in the third phrase.