Use Cases & Examples in 2023

Today, we have the capability to manipulate biology using large language models and generative AI models for predicting protein structures and characteristics, besides using them in the healthcare industry. This allows us to use generative AI in life sciences, for example to create innovative protein-based and small molecule treatments.

According to CB Insights, venture capitalists and investors invested $2.6 billion in 2022 into 110 generative AI-focused startups in the U.S.¹ Moreover, acknowledging generative AI as one of the future tech trends, Gartner states that use of generative AI in drug development will reduce the drug discovery costs and timeline.²

As researchers work on the impacts of generative AI in life sciences, it is useful to know how it can be applied to the field. In this article, we provide 10 use cases of generative AI in life sciences such as biology, and provide 3 real life examples.

10 Use Cases of Generative AI in Life Sciences

Generative AI has found numerous applications in life sciences, helping to drive research and development, optimize processes, and generate new insights. Some use cases include:

1- Novel molecule generation 

Generative models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), can be used to design novel drug-like molecules with desired properties, such as high binding affinity to a target protein or low toxicity.

2- Protein sequence design 

Generative models can create novel protein sequences with specific functionalities or properties, which can be useful in protein engineering, enzyme design, and the development of novel therapeutics.

3- Synthetic gene design

Generative AI can be employed to design synthetic gene sequences for applications in synthetic biology, such as creating new biosynthetic pathways or optimizing gene expression for biomanufacturing purposes.

4- Data augmentation for model training

Generative models can generate synthetic data to augment existing datasets, helping to improve the performance of AI models in tasks like the analysis of medical images, drug discovery, and accurate diagnosis.

5- Imputation of missing data

Generative AI can help fill in missing medical data in life science datasets, allowing researchers to work with more complete and reliable information for downstream analysis and modeling.

6- Virtual patient generation

Generative models can be used to create synthetic patient and healthcare data, which can be useful for training AI models, simulating clinical trials, or studying rare diseases without access to large real-world datasets.

7- Single-cell RNA sequencing (scRNA-seq) data denoising

Single-cell RNA sequencing (scRNA-seq) is a powerful technique used to study the gene expression profiles of individual cells. Single-cell RNA sequencing (scRNA-seq) data denoising refers to the process of removing noise, or unwanted variations, from the raw gene expression data obtained through scRNA-seq experiments.

Denoising scRNA-seq data is crucial for obtaining accurate and reliable gene expression profiles of individual cells, which in turn enables the correct identification of cell types, differentiation trajectories, and other biologically meaningful insights. Generative models can be used to denoise scRNA-seq data, improving the accuracy of downstream analysis, such as cell-type identification and gene expression quantification.

8- Image-to-image translation

Generative AI models can be employed to convert one type of biological image to another, such as transforming fluorescence microscopy images into electron microscopy images, which can help researchers gain insights from different imaging modalities.

9- Text-to-image generation

Generative models can be used to generate images of biological structures or processes based on textual descriptions, which can be helpful in visualizing complex phenomena or generating data for hypothesis testing.

10- Simulating biological processes

Generative AI can be employed to create realistic simulations of biological processes, such as cellular signaling or metabolic pathways, helping researchers to better understand these biological systems and predict their behavior under different conditions.

3 Real Life Examples

Biomatter

Biomatter, a company specializing in synthetic biology, employs ProteinGAN, a GPU-powered algorithm, on their Intelligent Architecture™ platform, which combines generative AI and physical modeling (Figure 1). This approach enables them to create entirely novel and functional enzymes.

Figure 1. The working principle of the Intelligent Architecture platform

Source: Biomatter

Evozyne

Evozyne integrates engineering and deep learning techniques to develop highly functional synthetic proteins. They utilize the NVIDIA BioNeMo framework to expedite the creation of ProT-VAE, a transformer-based model specifically designed for protein engineering.

The company works on many bioengineering use cases, such as:

• Gene optimization
• Gene editing
• Generating therapeutic antibodies

Figure 2. Evolutionary data-driven protein engineering by Evozyne

Source³: Science

Peptilogics

The Nautilus™ generative AI platform by Peptilogics facilitates peptide drug design and lead optimization across various therapeutic fields and biological targets. Powered by Peptilogics’ custom-built N4 supercomputer, which utilizes NVIDIA GPUs, Nautilus incorporates the company’s proprietary peptide representation and generative algorithms along with computational chemistry and biophysics. This integration helps to decrease the expenses, duration, and risks associated with drug design.

Figure 3. The discovery process of Peptilogics

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1. “AI Becomes Silicon Valley’s Next Buzzy Bandwagon as Crypto Boom Fizzles.” The Wall Street Journal, 18 February 2023, https://www.wsj.com/articles/ai-silicon-valley-crypto-boom-blockchain-artificial-intelligence-59622e9c. Accessed 20 March 2023.
2. “Generative AI Use Cases for Industries and Enterprises.” Gartner, 26 January 2023, https://www.gartner.com/en/articles/beyond-chatgpt-the-future-of-generative-ai-for-enterprises?source=BLD-200123&utm_medium=social&utm_source=bambu&utm_campaign=SM_GB_YOY_GTR_SOC_BU1_SM-BA-SWG. Accessed 20 March 2023.
3. “An evolution-based model for designing chorismate mutase enzymes.” Science, https://www.science.org/doi/10.1126/science.aba3304.