NEXT.BIO 2025 - Reinventing pigments and dyes through AI

​​​​​In October 2025, A16Z #SFTechWeek will once again take over San Francisco. We are organizing NEXT.BIO, a world-class week long tech conference featuring the best startups, industry leaders and investors in AI x BIO across a series of Tech Events, a unique Hackathon and a DEMO Day 🚀 ​

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​​​​This event is one of our Tech Event series part of the full-week conference.

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​​​​🧩 Suggested Themes & Challenges:

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​What is are the biomaterials available datasets?

​1. Experimental / Properties Data

• ​Biomolecular Adsorption Database 2.0 (BAD2.0)
  A specialized database capturing protein adsorption onto biomaterial surfaces, with detailed descriptors like protein identity, surface properties, and environmental conditions. Over 865 adsorption records, fully quantitative and update from 2024. Wikipedia

• ​Global Clinical Trials Dataset Involving Engineered Biomaterials
  A structured dataset created from ClinicalTrials.gov, containing metadata such as biomaterial type, properties, application context, and trial attributes. Great for analysis of translational trends. Zenodo

• ​FTIR & Raman Datasets for Biomaterials with Drug Loading
  Specialized spectral datasets focusing on biomaterials after loading with carboplatin drug—helpful for chemical and structural fingerprinting. Figshare+1

• ​GelMA/Alginate Hydrogels Fabrication Dataset
  Published on Figshare in early 2025, this dataset pertains to blood vessel fabrication using hydrogels, with relevance to 3D biofabrication and property optimization. Figshare

​2. Text/NLP-Focused Corpora for Biomaterials Literature

​Developed under the Horizon Europe BIOMATDB initiative, these corpora are invaluable if you're building NLP tools for extracting biomaterial-related information from scientific papers.

• ​BIOMAT-NER: Contains annotations of biomaterial types, chemical substances, and trade names across ~4,553 training documents, plus validation and test sets. Zenodo+1

• ​BIOMAT-MONER: Focused on "manufactured object" entities—tools, devices, implants—with 750 train and 100 validation documents. Zenodo

• ​BIOMAT-CellNER: Annotated for cell types and cell lines interacting with biomaterials, similar train/validation size. Zenodo

• ​BIOMAT-AnatNER: Focused on anatomical structures (tissues, organs) in biomaterials context. Zenodo

​3. Computational & Structural Materials Data

• ​Open Materials 2024 (OMat24)
  A massive open dataset with over 110 million DFT-calculated inorganic material structures. It’s paired with pre-trained models (EquiformerV2) capable of predicting properties like stability and formation energy—ideal for ML-driven materials discovery. arXivReddit

• ​"Awesome-Biomaterials-DataScience" GitHub List
  A living resource aggregating databases and tools—including DEBBIE (Experimental Biomaterials), MatWeb, Spider Silkome database, and more. Not a single dataset, but a goldmine for discovering resources. GitHub