NEURAL NETWORKS : INDUSTRY USE CASE
Neural Network in simple words are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data.
An Neural Network has hundreds or thousands of artificial neurons called processing units, which are interconnected by nodes. These processing units are made up of input and output units. The input units receive various forms and structures of information based on an internal weighting system, and the neural network attempts to learn about the information presented to produce one output report.
Neural networks can be applied to real world problems in many ways. In fact, they have already been successfully applied in many industries. As neural networks are used to identify patterns in data, they are well best for prediction or forecasting like :
- Sales forecasting
- Customer research
- Data validation
- Risk management
- Target marketing
- Medicinal Enhancement
ATOMWISE : Industry Use Case
Atomwise uses Neural Networks to help discover new medicines and agricultural compounds. Its groundbreaking AtomNet technology reasons like a human chemist, using powerful deep learning algorithms and supercomputers to analyze millions of data about diseases.
Taken from company’s website “Atomwise is revolutionizing how drugs are discovered with AI. We invented the use of deep learning for structure-based drug discovery, today developing a pipeline of small-molecule drug candidates advancing into preclinical studies. Our AtomNet® technology has been used to unlock more undruggable targets than any other AI drug discovery platform. We are tackling over 600 unique disease targets across 775 collaborations spanning more than 250 partners around the world. To date, Atomwise has raised over $174 million from leading venture capital firms to advance our mission to make better medicines, faster.”
How does AtomNet works ?
AtomNet technology is the first drug discovery algorithm to use a deep convolutional neural network. This type of network came to prominence only a few years ago and has a unique property: it excels at understanding complex concepts as a combination of smaller and smaller pieces of information.
This is what AtomNet platform does : when different neurons on the network are examined we see something new: AtomNet platform has learned to recognize essential chemical groups like hydrogen bonding, aromaticity, and single-bonded carbons. The patterns it independently observed are so foundational that medicinal chemists often think about them, and they are studied in academic courses. Put simply, AtomNet technology is teaching itself college chemistry.
So AI and neural networks can be applied to a vast number of use cases.