NewTesela AI is here — AI agents for library design, protocols & optimization. Try it free

AI-Powered Discovery

Discover Toolkit

With the Discover toolkit, teams can combine their knowledge and data with AI algorithms built to understand biology — leading to new, high performance bio-based products faster than ever before. Our AI models allow you to converge on an optimal product ten times faster than using traditional approaches.

TeselaGen Discover toolkit dashboard

Train Predictive Models

We implement predictive models that can be used to predict target values based on quantitative or qualitative inputs. These predictive models can be used to predict the phenotype of different biological products, such as the binding affinity of an antibody or the titer of a metabolite. Biological and chemical entities can be highly dimensional but can be mapped to an embedding space. We implement different embeddings to represent DNA molecules, proteins, and chemical compounds, to efficiently train predictive models.

Predictive modeling process diagram

Generate New Leads

Our generative models can be trained to learn the probability distribution of the molecules in a training dataset, and then used to sample it and generate new leads that follow the same distribution. We have shown that by using some generative models we can capture the desired properties of certain peptides and protein domains.

Generative model lead generation

Run Evolutive Models

We implement bayesian optimization techniques to recommend new experiments based on empirical data gathered in the lab. Our evolutive models can use other pre-trained predictive models as surrogate models. Evolutive models can also be used with pre-trained generative models, when exploring highly dimensional design spaces.

Bayesian optimization visualization

Deploy Your Own AI Models

Our platform implements advanced deep learning techniques to model biological entities, and to optimize synthetic constructs of DNA, proteins, and other biomolecules. Train our own off-the-shelf models, or integrate our operating system with your own algorithms.

AI model deployment interface

In collaboration with TeselaGen, at DTU we have used machine learning models to generate new design recommendations, enabling us to successfully forward engineer the aromatic amino acid metabolism in yeast.

Michael Krogh Jensen, PhD

Michael Krogh Jensen, PhD

Researcher, The Novo Nordisk Foundation Center for Biosustainability

Try Tesela AI for free

Design libraries, generate protocols, and optimize experiments with AI agents. No credit card required.