Creating novel drugs is an extraordinarily hard and complex problem. One of the many challenges in drug design is the sheer size of the search space for novel chemical compounds. Vingyani has the right blend of deep science and advanced engineering with a focus on AI for drug discovery. With massive amounts of high quality data, we develop a variety of leading-edge machine learning methods. As witnessed in other industries, machine learning can make sense of vast amounts of high-dimensional data that are beyond human ability to interpret.
Working with Vingyani you have immediate access to a highly-qualified data science team to help maximize understanding and gain actionable results from your data. Our careers have been spent working in emerging areas with big, highly complex data. Choosing the right data analytics partner is as important as the data itself. We work collaboratively with you to develop new methods for unique data analysis challenges.
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 analyseand 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.
Vingyani is working on building an AI platform to find molecules that are active toward a biological target and at the same time have acceptable ADMET properties. The need for a paradigm shift in the drug discovery process is clearly evidenced by the time taken for a drug to reach final approval and the high failure rates. What is also well known, is the potential that AI has to step up to that challenge and provide the transformative power required to generate new drug-like molecules and much more.