Generative AI in Life Sciences: Use Cases & Examples in 2025

Large language models, as part of generative AI, can predict protein structures and characteristics for use in healthcare and other industries. Generative AI in life sciences is transforming the field, driving advancements in drug discovery, personalized medicine, and diagnostics.

Explore 10 use cases of generative AI in life sciences, such as biology, and 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 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 developing novel therapeutics.

Real-life example: Google Deepmind released AlphaProteo to design novel proteins that are likely to bind to target molecules.¹

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 improve AI models’ performance 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. This, 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 create realistic simulations of biological processes, such as cellular signaling or metabolic pathways, helping researchers better understand these biological systems and predict their behavior under different conditions.

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 designed explicitly 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

Source: 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

Applications of Generative AI in Life Sciences

1. Drug Discovery and Design

Molecule Generation: Generative AI can design new molecules by analyzing chemical and biological datasets. These models predict molecular structures that meet specific criteria, such as binding affinity, solubility, or toxicity. This process accelerates the traditionally lengthy and expensive drug discovery timeline, enabling the quicker identification of potential drug candidates.

Target Identification: Generative AI helps researchers identify potential targets for drug development by analyzing protein structures and mapping interactions. This capability reduces the time spent on early-stage research by narrowing down viable targets for further study.

2. Protein Structure Prediction

AI models like AlphaFold, developed by DeepMind, have revolutionized protein structure prediction. Using generative techniques, these models predict the 3D shapes of proteins based on their amino acid sequences. This understanding is crucial for designing therapies for diseases associated with specific proteins, thereby accelerating research in enzyme design and vaccine development.

3. Genomics and Personalized Medicine

DNA and RNA Sequence Generation: Generative AI can simulate genetic sequences, enabling researchers to explore genetic variations, potential mutations, and their impact on diseases. These simulations are beneficial in genetic engineering and evolutionary studies.

Personalized Treatment Plans: Generative AI analyzes large datasets of patient-specific information to create tailored treatment recommendations. For example, it can identify the most effective drug based on an individual’s genetic makeup or predict disease progression to optimize care strategies.

4. Synthetic Biology

Generative AI in life sciences is crucial in designing synthetic organisms or modifying biological systems for specific purposes. For example, it can engineer microbes that produce biofuels, develop crops with higher yields, or create synthetic cells for pharmaceutical production. This has broad implications for healthcare, agriculture, and environmental sustainability.

5. Medical Imaging and Diagnostics

Generative AI in life sciences models can create high-quality synthetic medical images, such as CT scans or MRIs, which train diagnostic models. These synthetic datasets help overcome the challenge of limited labeled medical data and improve diagnostic accuracy in identifying conditions like cancer, heart disease, or neurological disorders.

6. Clinical Trials Optimization

Patient Recruitment: Generative AI in life sciences analyzes health records, genetic data, and patient demographics to identify suitable candidates for clinical trials, ensuring that trials are diverse and representative.

Simulation Models: Generative AI predicts the potential outcomes of new therapies by creating virtual trials, allowing researchers to refine trial designs and reduce costs. This approach facilitates the assessment of drug efficacy and safety without the immediate need for large-scale human trials.

FAQ

External Links

• 1. AlphaProteo generates novel proteins for biology and health research - Google DeepMind.