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.
Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. 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.
‘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.
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.