
- Description
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Biomap is a cutting-edge biotech/tech company at the forefront of AI-driven design for biologics. Leveraging the power of large language models (LLMs) and neural networks, Biomap is transforming the development of biologics for a wide range of applications, including:
Healthcare: Designing novel therapeutic proteins and antibodies with enhanced efficacy, safety, and targeted delivery, addressing unmet medical needs in various disease areas.
Environment: Engineering enzymes and biocatalysts for sustainable solutions in bioremediation, biofuel production, and waste management, contributing to a healthier planet.
New materials: Creating bio-based materials with superior properties for diverse applications such as textiles, packaging, and construction, promoting a circular economy.
Biomap's core technologies:Large Language Models (LLMs): Harnessing the vast knowledge embedded in scientific literature and biological databases to generate innovative ideas and hypotheses for biologic design.
Neural Networks: Employing advanced machine learning algorithms to analyze complex biological data, predict protein structures and functions, and optimize biologic properties.
By combining AI expertise with deep domain knowledge in biology and chemistry, Biomap is accelerating the discovery and development of biologics that have the potential to revolutionize healthcare, environmental sustainability, and materials science. - Number of employees
- 51 - 200 employees
- Company website
- https://www.biomap.com
- Categories
- Healthcare Biotechnology Machine learning Artificial intelligence
- Industries
- Hospital, health, wellness & medical It & computing Science Technology
Recent projects
Automatic Identification of Epitopes for Drug Design
The main goal for the project is to develop a versatile Python package aimed at automating epitope identification on pharmaceutical targets. This package will empower drug discovery researchers by streamlining the complex process of analyzing protein structures, predicting potential epitope regions, and assessing their druggability potential.
Fragment Assembly Library Designer
Automated Fragment Library Design: Enable users to easily generate diverse fragment libraries based on customizable parameters such as fragment length, chemical diversity, and desired scaffolds. Efficient Assembly Planning: Optimize the assembly process by suggesting the most efficient assembly methods and primer designs for specific library configurations. Integrated Data Management: Provide a centralized database to store and manage fragment sequences, assembly information, and screening results. User-Friendly Interface: Create an intuitive graphical user interface (GUI) or command-line interface (CLI) that simplifies the library design and assembly workflow. Data Analysis and Visualization: Incorporate tools for visualizing library diversity, analyzing screening results, and identifying promising fragment hits.