Vector Space Biosciences Announces New Tools for Developing Countermeasures Associated to Stressors During Spaceflight

Vector Space Biosciences
4 min readAug 18, 2022


SAN FRANCISCO, CA, August 18, 2022 — (BUSINESSWIRE)- To establish a lunar base or go to Mars, understanding how to protect and repair the human body during spaceflight is imperative. Countermeasures against diseases associated to stressors during spaceflight, such as microgravity and radiation, need to be developed quickly. Vector Space Biosciences has developed a platform of computational solutions designed to accelerate the development of countermeasures like these including new applications connected to drug repurposing, therapeutic target identification, molecular design and nutrigenomic cocktail development which translate to marketable therapeutic applications including precision medicine for all humankind.

Today, research, development and commercialization, in the area of biotechnology and pharmaceutical development, is accelerated with computational models in artificial intelligence (AI) and machine learning (ML). Language models represent the tip-of-the-spear in today’s AI and ML pipelines. Language models, specifically biological language modeling, can be applied to non-human language such as gene, protein and molecular sequences such as amino acids as shown by the recent breakthrough in predicting the way a protein folds made by AlphaFold 2 and its variants. Language models such as BERT, GPT-3 and DALL-E are advancing AI in new ways.

As the new space race heats up, more humans will be traveling to space than ever before. Spaceflight causes many changes in human health. Humans cannot be sent to space safely without understanding how to protect the human body from Galactic Cosmic Rays (GCRs) and High-energy and high-charge particles (HZEs). Using scientific data engineering pipelines, language modeling, datasets and visualization to gain a better understanding of Bragg peak analysis related to DNA damage and repair pathways along with high/low LET (Linear Energy Transfer) radiation, telomere elongation/shortening, chromosomal translocations and dysregulated gene expression, combined with multi-omics research efforts, remain key. This requires running a wide variety of experiments in space from low Earth orbit (LEO) to deep space biology experiments on Artemis I targeted for launch August 29th 2022.

Vector Space Biosciences maintain a network of scientific labs and collaborators with a multi-omic focus on stressors related to the tumor microenvironment (TME), brain extracellular matrix (ECM, matrisome), exosomes, mitochondrial stress, muscle atrophy (atrogenes) and human aging. This is done by collaborating on redesigning experiments that have been run on the ground to run during spaceflight, initially at low Earth orbit. The goal is to maintain regular launch intervals for experiments while collecting data which is transmitted back to Earth. Each experiment and launch is funded by public and private entities, individuals and members of Vector Space Biosciences’ DAO (Decentralized Autonomous Organization), SpaceBioDAO.

Data collected is analyzed using Vector Space Biosciences’ scientific data engineering pipelines using ensembled data sources, language models, vector representations, datasets and visualizations trained to detect hidden relationships between genes, proteins, pathways, drug compounds, micronutrients, phytochemicals, molecular sequences and signatures.

Every 24hrs about 4000 new peer reviewed scientific papers are submitted to the National Library of Medicine’s PubMed database. About 1500 of those are new submissions with the remaining being modifications or retractions. Real-time data sources like these result in datasets which can be updated to reflect new changes in context-dependent relationships between genes, proteins, drug compounds and molecular sequences on a daily basis. Vector Space Biosciences enables researchers in space biosciences, biotechnology and pharmaceutical development to capture changes in these relationships in real-time.

A new API (Application Programming Interface) is now available for Protein-to-Protein and Protein-to-Drug Compound interaction prediction networks. The platform is designed to accelerate the generation of new hypotheses, insights, interpretations and discoveries for researchers in space biosciences and is available via subscription to biotechnology and pharmaceutical development companies. Initially, this includes the release of the Protein Network API. Subscriptions come with support maintenance contracts for pipeline customization. Licensing and revenue sharing agreements will be made available for custom data engineering pipelines which generate datasets that can be used to power internal artificial intelligence and machine learning pipelines used for proteomic research or drug repuposing and discovery.

About Vector Space Biosciences, Inc.

Vector Space Biosciences, Inc., parent company of Vectorspace AI, develops countermeasures against diseases associated to stressors connected to protecting and repairing the human body during spaceflight using a network of scientific data engineering pipelines resulting in real-time datasets which power Machine Learning (ML) and Artificial Intelligence (AI) operations in space biosciences, biotechnology and pharmaceutical development. Working with leading scientific labs in the areas of human aging, cancer, and nutrigenomics, our goal is to accelerate the process of new hypothesis generation and novel discoveries in space biosciences, including materials sciences in the area of nanotechnology and nanomedicines. Using language modeling related to Natural Language Processing and Understanding (NLP/NLU), our platform is capable of producing more than 100,000,000,000 different real-time datasets for the purpose of accelerating discoveries. Innovations in space biosciences result in products and services for all industries, including the financial markets, more importantly, new forms of precision medicine for all humankind.

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Vector Space Biosciences

Accelerating discovery through advanced language modeling for hidden relationship detection in biological data.