DeepLife Digital Twin: A New Paradigm in Drug Discovery
Drug discovery is an incredibly complex, time-consuming, and costly process, where one major challenge is the sheer complexity of biological systems. Despite advances in technology, it is still exceedingly difficult to emulate human physiology accurately in a lab setting. Culturing cells and tissue for testing is not only expensive but also often does not represent the full intricacies of whole-organ systems or inter-organ interactions. Additionally, wet lab experiments require extensive time for preparation, execution, and validation. From isolating target compounds to running assays and analyzing results, each step can take weeks or even months to complete. These inefficiencies are exacerbated by the necessity of repeating experiments to validate results, further draining resources, with the vast majority of potential drugs failing during the preclinical and clinical trial stages.
With these challenges in mind, we developed DeepLife Digital Twin of Cells, integrating vast and complex datasets while providing precise and explicable simulations of cellular behavior, serving as a virtual lab for scientific inquiry.
Employing algorithms to mirror complex biological processes, DeepLife's Digital Twin of Cells functions as a numerical representation of cells, able to mimic intricate cellular events that guide scientific decision-making. Built on DeepLife's OMICS Catalog and atlases, which provide an extensive repository of single-cell RNA sequencing data covering a plethora of cell types and tissues, this Digital Twin utilizes state-of-the-art large language models (LLM) for processing and interpreting vast biological datasets. Beyond scRNA, the Digital Twin is set to expand and encapsulate additional data modalities, including genomics, proteomics, and metabolomics, enabling unparalleled simulation of cellular states with each omics layer processed using our advanced LLM.
The Digital Twin acts as a virtual lab, removing the need for years of iterative biological experiments. Leveraging a detailed characterization of individual cell types and their functional interactions within biological systems to gain a deep contextual understanding, we are able to simulate how cells respond to various perturbations effectively. Unlike previous models that rely on simplified relationships, DeepLife’s Digital Twin takes a nuanced approach to represent more complex interactions within cells, providing a detailed and dynamic simulation that predicts how cells will respond to a variety of perturbations, such as a gene knockout for example.
DeepLife’s Digital Twin technology serves as a foundation model, a pre-trained AI-based simulation that mimics the biological behavior of real-world cells under varying conditions. In essence, this Digital Twin aims to significantly accelerate and reduce the risks in the drug development process, providing a digital counterpart to conventional in-vitro experiments. As an extension of our foundation model, we can fine-tune the model using your own use-case specific data, tailoring the digital twin to specific therapeutic areas and research questions. This enables scientists to virtually test hypotheses and simulate experiments using their own data, dramatically accelerating R&D processes.
The Digital Twin technology offers a multi-faceted utility across various aspects of drug discovery, each tailored to address specific challenges and boost efficiencies.
From a fundamental science perspective, DeepLife’s technology elucidates biological pathways and disease modalities, offering insights to unveil new mechanisms of action with an unprecedented depth of analytical power.
By integrating the OMICS Catalog with the predictive power of the Digital Twin, we are redefining standards for cellular research and drug discovery. As our technology matures, the Digital Twin will continue to evolve, enhancing our grasp on cellular interactions and expediting therapy development.
For further engagement and to explore potential partnerships, contact us.
