I wrote a GPU-less billion-vector DB for molecule search (live demo)
I wrote a GPU-less billion-vector DB for molecule search (live demo)
Input a SMILES string (or pick one molecule from the examples) and it returns up to 100k molecules closest in 3-D shape or electrostatic similarity – from 10+ billion scale databases — typically in under 5-10 s. *Why it might interest HN* * Entire index lives on disk — no GPU at query-time, less than ~10 GB RAM total. * Built from scratch (no FAISS index / Milvus / Pinecone). * Index-build cost: one Nvidia T4 (~ 300USD) for one 5.5B database. * Open to anyone, predict ADMET, export results as CSV/SDF. Full write-up & benchmarks (DUD-E, LIT-PCBA, SVS) in the pre-print: https://chemrxiv.org/engage/chemrxiv/article-details/6725091...
Share cardActual performance
Launch Intel predictions
Analyze your own launch →Correct prediction on native model
Similar products
Memora – A Vector DB with Multistage Reranking
I put PubMed in a vector DB
Move data from any vector DB to any other vector DB
XTrace – Encrypted vector DB (search embeddings without exposing them)
Client-Side HN Search – A Demo of Querying a DB Without a DB Server
Aggregate search DB for arbitration outcomes
Live demo of WebEngage
Marqo – Vectorless Vector Search
SQLite-Vector – Vector search for SQLite, now Apache 2.0
GPU accelerated marine & lakes vector maps