About PAVI
PAVI is an abbreviation of the Sanskrit words. Where P comes from ParigaNana which means
Estimation (or Computation),
A is from auSadha, means Drug and VI is from vijJAnavarNana, which means design. So, PAVI, is a
computation of
drugs and their design. The platform PAVIosd (OSD stands for Open Source Database)
is a database where the researcher will be sharing their data generated
from the computation (in silico) drug design methods.
- Digital technologies and in silico methodologies are changing the way of the drug discovery process. DeepTech such as artificial intelligence, Artificial Intelligence, blockchain, data analytics and physics-based methodologies are central to creating more agile research and development processes.
- These technologies all accelerate a specific early stage R&D process and late-stage clinical development and gain efficiency and cost advantages.
- PAVI is, therefore, going to take a centre stage, as scientists and researchers across the globe share their research and data generated from in silico methods to this open-source platform. Within a few years, we expect PAVI will grow and become the go-to tool for experimentalists before they embark on any drug discovery project.
- This platform will reduce redundancy in research, as many research groups in academia and industry perform the same calculations even they were done before and reported in the literature."
Objectives of PAVIosd
- Decrease the time by 2X for a drug to reach lab bench to bed-side
- Cut cost by 3X – by incorporating in silico drug design
- Open data for all – enhanced collaborations
- Skill building – enrichment of the open-source database by newly skilled computational drug developers
- Opensource of computational data – would help the experimentalists in academia and industry to fast track their project (reduction to time by 2X)
How to achieve?
- Collaborations between groups
- Open sourcing of data – need an opensource platform
- Computational (in silico) drug design – automated software suit
- Use of artificial intelligence (machine learning and deep learning) on the data
- Use of natural language processing for reading data, methods, protocols from published literatures
- Skilling scientists - create more work force
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