We are at a pivotal moment where AI can transform the future of biologics discovery. By optimizing data for AI-driven workflows, your organization can unlock new possibilities in therapeutic development.
The ENPICOM Platform seamlessly integrates AI, data management, and machine learning (ML) frameworks to foster collaboration among lab scientists, data scientists, and ML experts to drive innovation in biologics research.
Integrating AI into research pipelines can involve many hurdles, depending on the workflows associated with each organization’s specific drug discovery applications.
Effective API solutions facilitate the seamless integration of data from multiple sources, automating processes and enabling advanced data analysis with deep-learning models.
Many therapeutic discovery processes suffer from slow pipelines, inefficient resource distribution, and difficulties in adopting new technologies. One of the main reasons is the lack of workflow customization solutions. In this article, we will discuss the challenges and benefits of implementing tailored workflows and introduce our Last Mile Service.
Reducing barriers for researchers to access highly curated data eliminates repetitive analysis steps and facilitates innovation and progress. We had the honor of delving into this subject with Dr. Felix Breden, Professor Emeritus at Simon Fraser University, and a distinguished member of the AIRR Community.
ENPICOM secures additional funding to expand AI development and welcomes antibody discovery expert Dr. Colby Souders as a new investor.
The main purpose of InSilicoSeq 2.0 is to generate synthetic datasets that imitate real-world scenarios so that scientists and programmers can validate and optimize bioinformatics tools, statistical algorithms, and analytical pipelines.
ENPICOM partners with Erasmus Medical Center to develop nanobodies against cancer using their immune repertoire data analysis solutions.
Carterra and ENPICOM collaborate to streamline the analysis of sequencing and antibody screening data, focusing on identifying improved clinical-grade antibody candidates.
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