Therapeutic Discovery

What are Generative AI Models?

Generative AI models are a type of artificial intelligence that generate new data that resemble training data by understanding the underlying patterns.


Generative AI models are a type of artificial intelligence that can generate new data instances that resemble your training data. They work by understanding the underlying patterns in the input data and creating new data that mirrors those patterns.

One of the most common types of generative models is Generative Adversarial Networks (GANs). GANs consist of two parts: a generator that creates new data instances, and a discriminator that tries to distinguish between real and generated instances. The generator's goal is to fool the discriminator into thinking that the generated instances are real.


How Generative AI Models Work

The working process of generative AI models involves two primary steps: learning and generating.
Learning: In this phase, the model learns the distribution of the given data. It tries to understand the patterns, structures, and features in the data. This is achieved through a process known as training, where the model is exposed to vast amounts of data and adjusts its internal parameters to better predict the output.
Generating: Once the model has learned the data distribution, it can generate new data instances. Using random noise as input, the model applies the learned data distribution to create new data that mimics the original.

 

Generative AI Models in Antibody Therapeutics

The development of antibody therapeutics is a complex and time-consuming process that involves identifying suitable antibodies that can effectively target specific diseases. Generative AI models are playing an increasingly important role in this process.

Generative AI models can be trained on vast databases of existing antibodies. They learn the complex relationships and patterns between the structure of these antibodies and their effectiveness against various diseases. Once trained, these models can generate new antibody candidates that are likely to be effective against specific diseases based on the learned patterns.

For instance, generative AI models can be used to predict the structure of antibodies that could potentially bind to a specific antigen associated with a disease. This significantly speeds up the drug discovery process by reducing the need for extensive laboratory testing.

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