unimol_tools is a Python package for property prediction with Uni-Mol in molecule, materials and protein.
Why a #lmdb replacement hasn't happened in c++?
https://www.reddit.com/r/cpp_questions/comments/1sr4s3h/why_a_lmdb_replacement_hasnt_happened_in_c/
reddit illustrates two kinds of programmers, one who thinks that terse docs are broken, and one who knows small simple APIs don't need voluminous docs
File Access Policy Daemon https://github.com/linux-application-whitelisting/fapolicyd/blob/main/README.md
Built on #LMDB
SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes
Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology.
introducing SPATIA, a multi-level generative and predictive model that learns unified, spatially aware representations by fusing morphology, gene expression, and spatial context from the cell to the tissue level. -- built on #LMDB https://arxiv.org/abs/2507.04704v3
Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology. Image-based spatial transcriptomics technologies now provide high-resolution measurements of cell images and gene expression profiles, but existing methods typically analyze these modalities in isolation or at limited resolution. We address the problem by introducing SPATIA, a multi-level generative and predictive model that learns unified, spatially aware representations by fusing morphology, gene expression, and spatial context from the cell to the tissue level. SPATIA also incorporates a spatially conditioned generative framework with confidence-aware OT reweighting and morphology-profile alignment for modeling target-state morphology distributions. Specifically, we propose a confidence-aware flow matching objective that reweights weak optimal-transport pairs based on uncertainty. We further apply morphology-profile alignment to encourage biologically meaningful image generation, enabling the modeling of microenvironment-dependent phenotypic transitions. We assembled a multi-scale dataset consisting of 25.9 million cell-gene pairs across 17 tissues. We benchmark SPATIA against 18 models across 12 tasks, spanning categories such as phenotype generation, annotation, clustering, gene imputation, and cross-modal prediction. SPATIA achieves improved performance over state-of-the-art models, improving generative fidelity by 8% and predictive accuracy by up to 3%.
#LMDB embedded key-value store bindings for Kit
This guy has vibe-coded an SQL layer on top of #LMDB. Can't imagine it's any good, since it's actually over his Java interface to LMDB. I guess he never heard of SQLightning or #LumoSQL https://www.linkedin.com/posts/crossondavid_what-on-earth-is-the-world-coming-to-it-activity-7470569305588531200-hIzU
I mean, the point of LMDB was to eliminate useless abstraction layers between the app and the OS. But sure, go ahead and throw an entire JVM on top of it, before adding the parts you actually want to use.
What on earth is the world coming to? It only took me a few hours with Claude Code (with OPUS 4.8, Fable was not yet available) to add almost full SQL support to my LMDB based database (https://lnkd.in/ez2TDZkF) ! Even complex queries are working... A real game-changer, it was a so smooth session :) OK it became easy because I made this possible thanks to the right foundations, orchestrations, and various preliminary "experiment" ;)
betex
Deterministic, event-sourced matching engine for exchange odds and binary prediction markets.
https://crates.io/crates/betex built on #LMDB
Howard Chu @ Symas
