Augmenting Drug Discovery with Artificial Intelligence

We’re reinventing the way treatments for diseases are discovered.

There have been tremendous advances in understanding the biology of disease, but this explosion of knowledge has far outpaced the human ability to consume and make sense of it. This has created a challenge for scientists working to translate biomedical data into new treatments. The future of medicine will rely on artificial intelligence, because biology is too complex for humans to understand. 


We explore what the algorithm behind voice and image recognition can do for drug discovery.  We apply sophisticated algorithms to analyse and mine different data sources to predict molecular behavior and suitability as lead compounds. We call this as Molecular Machine Learning. Molecular ML is  attempting to build machine-learning architectures and code-bases for learning to predict properties of molecules.

Artificial Intelligence for Omics

 ‘Omic’ technologies are primarily aimed at the universal detection of genes (genomics), mRNA (transcriptomics), proteins (proteomics) and metabolites (metabolomics) in a specific biological sample. We apply AI to sift through large amounts of these heterogeneous data and identify patterns that may not have been previously apparent.

Digital Diagnosis: The Algorithm will See You

What happens when the diagnostics is automated? The E-Doctor will see you now! Vingyani is looking for partners and investors to develop the E-Doctor. Vingyani also would like to explore the possibility to work with the start ups who are already working on this project. As we are a deep learning CRO, we can partner with you to develop your project.   As AI evolves, the doctor’s role will inevitably be reimagined as computers make more decisions and patients gain more power. Data won’t just be shared with doctors, but with patients as well, fueling a movement in which patients are better-informed and unrestrained by geography.