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Registry

A living archive of tools Lazarus has brought back from the dead — each revived from its source repo into a callable, containerised brick with a verified sanity check, and (where a benchmark exists) a reproduced paper number.

25 revived tools. Pull any of them: lazarus pull <name>.

Tool Domain Era / stack Result From a URL
AHGestimation Hydrology R 0.997434 vs 0.9975 (continuity_exponent_sum) ✅
AiZynthFinder Retrosynthesis Py · ML smoke is_solved ≥ 1 ✅
Basset Genomics — chromatin accessibility 2016 · Lua Torch7 0.894 vs 0.895 (mean AUROC (164 targets)) ✅
CoCoNet Viral metagenome binning 2021 · Python · PyTorch smoke n_contigs_assigned ≥ 9 ✅
DeepFRI Protein function prediction Py · TensorFlow · protein LM 0.99999 vs 0.99824 (top1_score_GO:0005509) ✅
DeepLatentMicrobiome Microbiome — environment → OTUs 2021 · Python · TF/Keras 0.7368 vs 0.739 (mean per-sample Pearson r (n=373)) ✅
DESPASITO Thermodynamics Py · Numba smoke aard_vapor_pressure < 0.15 ✅
DiffDock Molecular docking 2023 · PyTorch diffusion · ESM-2 · GPU 0.375 vs 0.4 (top-1 success rate (<2Å)) ✅
dMaSIF Protein interface (surface, GPU) 2021 · Py3.6 · torch cu111 · PyKeOps · GPU smoke ROCAUC ≥ 0.65 —
DnaFeaturesViewer Sequence annotation plots Python · Biopython · matplotlib smoke feature_count ≥ 10 ✅
EquiBind Blind protein–ligand docking 2022 · PyTorch · DGL · SE(3) smoke ligand_centroid_distance_A < 10 ✅
EquiDock Rigid protein–protein docking 2022 · PyTorch · DGL · SE(3) smoke ligand_CA_RMSD_vs_reference_output < 2 ✅
fpocket Druggable pocket detection 2010 C · built on modern GCC smoke pockets ≥ 1 —
HiTEA Transposable-element insertions (Hi-C) 2020 · Perl + R · bedtools smoke num_candidate_insertions ≥ 1 ✅
MaSIF-site Protein interaction sites 2020 · Py3.6 · TF 1.12 · MSMS/APBS 0.82 vs 0.85 (median ROC-AUC) —
matador Materials science / DFT Py · materials informatics smoke hull_dist_max_abs_error_vs_reference < 0.001 ✅
MEIRLOP Genomics — motif enrichment Py 0.7814194753559087 vs 0.7814 (roc_auc) ✅
PRODIGY-CRYSTAL Structural biology — interface classification Py smoke bio_probability ≥ 0.79 ✅
PyAMG Numerical linear algebra Py · C++ smoke relative_residual < 1e-08 ✅
ScanNet Protein binding sites 2022 · Py3.6 · TF 1.14 · Keras smoke ROC_AUC ≥ 0.7 —
Scikit-Topt Topology optimization Py smoke compliance_reduction_ratio ≥ 0.1 ✅
Sequoya Multiple sequence alignment 2020 · Py3.6 · jMetalPy smoke sum_of_pairs_delta_vs_initial ≥ 0 —
SSALib Time-series analysis Py smoke reconstruction_relative_l2_error < 1e-06 ✅
trRosetta Protein structure prediction Py · TensorFlow 1 vs 1 (pearson_r of flattened <8A contact maps) ✅
W2W Urban climate / geospatial Py · geospatial smoke max_abs_diff_FRC_URB2D < 0.0001 ✅

AHGestimation ahg_estimate

Robust, mass-conserving estimation of at-a-station hydraulic geometry from river channel measurements.

  • Source: mikejohnson51/AHGestimation · MIT
  • Stack: R
  • Sanity check: continuity_exponent_sum_error < 0.05 · reproduced the paper: continuity_exponent_sum 0.997434 vs 0.9975
  • Revived: 17 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: AHGestimation: An R package for computing robust, mass preserving hydraulic geometries and rating curves (doi:10.21105/joss.06145)
lazarus pull ahg_estimate

AiZynthFinder aizynthfinder_retrosynthesis

Monte-Carlo tree search retrosynthetic route planning over reaction templates.

  • Source: MolecularAI/aizynthfinder · MIT
  • Stack: Py · ML
  • Sanity check: is_solved ≥ 1
  • Revived: 21 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. (doi:10.1186/s13321-020-00472-1)
lazarus pull aizynthfinder_retrosynthesis

Basset basset_predict

Predict DNaseI-hypersensitivity across 164 cell types from a 600 bp DNA sequence.

  • Source: davek44/Basset · MIT
  • Stack: 2016 · Lua Torch7
  • Sanity check: min_perseq_std ≥ 0.01 · reproduced the paper: mean AUROC (164 targets) 0.894 vs 0.895
  • Revived: 48 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: Kelley et al., Genome Research 2016 — Basset
lazarus pull basset_predict

CoCoNet coconet_binning

Bin assembled viral contigs into genomes from composition + coverage with a deep siamese network.

  • Source: Puumanamana/CoCoNet · Apache-2.0
  • Stack: 2021 · Python · PyTorch
  • Sanity check: n_contigs_assigned ≥ 9
  • Revived: 30 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: Arisdakessian et al., Bioinformatics 2021 — CoCoNet
lazarus pull coconet_binning

DeepFRI deepfri_mf

Predict protein GO-term / EC function from structure via a graph convolutional network + protein language model.

  • Source: flatironinstitute/DeepFRI · BSD-3-Clause
  • Stack: Py · TensorFlow · protein LM
  • Sanity check: top1_go_term_and_score ≥ 0.9 · reproduced the paper: top1_score_GO:0005509 0.99999 vs 0.99824
  • Revived: 21 autonomous agent-turns · from a bare URL (Scout-planned)
lazarus pull deepfri_mf

DeepLatentMicrobiome deeplatentmicrobiome_env2otu

Predict a 717-OTU rhizosphere microbiome from 3 environmental features (age, temperature, precipitation) via a pretrained latent-space encoder/decoder.

  • Source: jorgemf/DeepLatentMicrobiome · Apache-2.0
  • Stack: 2021 · Python · TF/Keras
  • Sanity check: pearson_r ≥ 0.65 · reproduced the paper: mean per-sample Pearson r (n=373) 0.7368 vs 0.739
  • Revived: 25 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: Garcia-Jimenez et al., Bioinformatics 2021 — Deep latent space model for the rhizosphere microbiome
lazarus pull deeplatentmicrobiome_env2otu

DESPASITO despasito_saft_gamma_mie_propane_saturation

SAFT-Gamma-Mie equation of state: parameterize and predict thermodynamic properties (e.g. vapor pressure).

  • Source: Santiso-Group/despasito · BSD-3-Clause
  • Stack: Py · Numba
  • Sanity check: aard_vapor_pressure < 0.15
  • Revived: 39 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: DESPASITO: A Python Package for SAFT EOS Parametrization and Thermodynamic Calculations (doi:10.21105/joss.07365)
lazarus pull despasito_saft_gamma_mie_propane_saturation

DiffDock diffdock_blind_docking

Blind docking — protein + ligand → ranked, confidence-scored 3D poses (diffusion model).

  • Source: gcorso/DiffDock · MIT
  • Stack: 2023 · PyTorch diffusion · ESM-2 · GPU · GPU
  • Sanity check: rmsd < 2.0 · reproduced the paper: top-1 success rate (<2Å) 0.375 vs 0.4
  • Revived: 57 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: Corso et al., ICLR 2023 — DiffDock
lazarus pull diffdock_blind_docking

dMaSIF dmasif_site

Differentiable molecular-surface interface prediction, built and run on GPU.

  • Source: FreyrS/dMaSIF · CC BY-NC-ND
  • Stack: 2021 · Py3.6 · torch cu111 · PyKeOps · GPU · GPU
  • Sanity check: ROCAUC ≥ 0.65
  • Revived: 51 autonomous agent-turns
  • Paper: Sverrisson et al., CVPR 2021 — dMaSIF
lazarus pull dmasif_site

ℹ️ The pinned image lazarus/dmasif:site-ready isn't published yet — pull fetches the contract (API + CLI + Dockerfile + smoke test) so it can be rebuilt.

DnaFeaturesViewer dnafeaturesviewer_genbank_plot

Render a GenBank record's annotated features as a linear feature map (PNG).

lazarus pull dnafeaturesviewer_genbank_plot

EquiBind equibind_blind_docking

Geometric deep learning for drug binding structure prediction — blind-dock a ligand into a protein in a single forward pass.

  • Source: HannesStark/EquiBind · MIT
  • Stack: 2022 · PyTorch · DGL · SE(3)
  • Sanity check: ligand_centroid_distance_A < 10
  • Revived: 32 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: Stärk et al., ICML 2022 — EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
lazarus pull equibind_blind_docking

EquiDock equidock_rigid_docking

SE(3)-equivariant end-to-end rigid protein–protein docking — predict the docked complex in one shot, no candidate sampling.

  • Source: octavian-ganea/equidock_public · MIT
  • Stack: 2022 · PyTorch · DGL · SE(3)
  • Sanity check: ligand_CA_RMSD_vs_reference_output < 2
  • Revived: 44 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: Ganea et al., ICLR 2022 — EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking
lazarus pull equidock_rigid_docking

fpocket fpocket2

Detect and rank druggable pockets on a protein structure (Voronoi / alpha-spheres).

  • Source: https://fpocket.sourceforge.net · MIT
  • Stack: 2010 C · built on modern GCC
  • Sanity check: pockets ≥ 1
  • Revived: 32 autonomous agent-turns
  • Paper: Le Guilloux et al., BMC Bioinformatics 2009 — fpocket
lazarus pull fpocket2

HiTEA hitea

Call non-reference transposable-element (Alu/L1/SVA) insertions from a Hi-C BAM; emits a candidate-insertions BED + an HTML report.

  • Source: parklab/HiTea · MIT
  • Stack: 2020 · Perl + R · bedtools
  • Sanity check: num_candidate_insertions ≥ 1
  • Revived: 14 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: Chu, Nielsen et al., Nucleic Acids Research 2021 — HiTEA
lazarus pull hitea

MaSIF-site masif_site

Predict per-residue protein-interaction-site probability from a molecular surface.

  • Source: LPDI-EPFL/masif · Apache-2.0
  • Stack: 2020 · Py3.6 · TF 1.12 · MSMS/APBS
  • Sanity check: roc_auc ≥ 0.8 · reproduced the paper: median ROC-AUC 0.82 vs 0.85
  • Revived: 18 autonomous agent-turns
  • Given back: masif PR #93
  • Paper: Gainza et al., Nature Methods 2020 — MaSIF
lazarus pull masif_site

matador matador_kp_convex_hull

Aggregate, query and analyse first-principles (DFT) crystal-structure calculations; build phase-stability convex hulls.

  • Source: ml-evs/matador · MIT
  • Stack: Py · materials informatics
  • Sanity check: hull_dist_max_abs_error_vs_reference < 0.001
  • Revived: 18 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: matador: a Python library for analysing, curating and performing high-throughput density-functional theory calculations (doi:10.21105/joss.02563)
lazarus pull matador_kp_convex_hull

MEIRLOP meirlop_motif_enrichment

Motif enrichment in ranked lists via logistic regression controlling for covariates.

  • Source: npdeloss/meirlop · MIT
  • Stack: Py
  • Sanity check: roc_auc ≥ 0.75 · reproduced the paper: roc_auc 0.7814194753559087 vs 0.7814
  • Revived: 22 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: MEIRLOP: improving score-based motif enrichment by incorporating sequence bias covariates. (doi:10.1186/s12859-020-03739-4)
lazarus pull meirlop_motif_enrichment

PRODIGY-CRYSTAL prodigy_cryst

Classify a protein-protein interface as biological or crystallographic from interfacial contacts.

  • Source: haddocking/interface-classifier · Apache-2.0
  • Stack: Py
  • Sanity check: bio_probability ≥ 0.79
  • Revived: 10 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: Distinguishing crystallographic from biological interfaces in protein complexes: role of intermolecular contacts and energetics for classification. (doi:10.1186/s12859-018-2414-9)
lazarus pull prodigy_cryst

PyAMG pyamg_ruge_stuben_solve

Algebraic multigrid solvers and preconditioners for large sparse linear systems.

  • Source: pyamg/pyamg · MIT
  • Stack: Py · C++
  • Sanity check: relative_residual < 1e-08
  • Revived: 11 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: PyAMG: Algebraic Multigrid Solvers in Python (doi:10.21105/joss.04142)
lazarus pull pyamg_ruge_stuben_solve

ScanNet scannet_ppi_binding_sites

Per-residue protein–protein binding-site probability from one structure (structure-only, no MSA).

  • Source: jertubiana/ScanNet · Apache-2.0
  • Stack: 2022 · Py3.6 · TF 1.14 · Keras
  • Sanity check: ROC_AUC ≥ 0.7
  • Revived: 19 autonomous agent-turns
  • Given back: ScanNet PR #16
  • Paper: Tubiana et al., Nature Methods 2022 — ScanNet
lazarus pull scannet_ppi_binding_sites

Scikit-Topt scikit_topt_oc

Structural topology optimization (optimality-criteria / MMA density methods) in Python.

  • Source: kevin-tofu/scikit-topt · Apache-2.0
  • Stack: Py
  • Sanity check: compliance_reduction_ratio ≥ 0.1
  • Revived: 26 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: Scikit-Topt: A Python Library for Algorithm Development in Topology Optimization (doi:10.21105/joss.09092)
lazarus pull scikit_topt_oc

Sequoya sequoya_nsgaii_msa

Multi-objective (NSGA-II) multiple sequence alignment optimizing sum-of-pairs and conserved columns.

  • Source: benhid/Sequoya · MIT
  • Stack: 2020 · Py3.6 · jMetalPy
  • Sanity check: sum_of_pairs_delta_vs_initial ≥ 0
  • Revived: None autonomous agent-turns
  • Paper: Benítez-Hidalgo et al. — Sequoya: Multiobjective Multiple Sequence Alignment in Python
lazarus pull sequoya_nsgaii_msa

ℹ️ The pinned image lazarus/sequoya:nsgaii-ready isn't published yet — pull fetches the contract (API + CLI + Dockerfile + smoke test) so it can be rebuilt.

SSALib ssalib_sst_ssa

Singular Spectrum Analysis: decompose a time series into trend/oscillatory/noise components and reconstruct.

  • Source: ADSCIAN/ssalib · BSD-3-Clause
  • Stack: Py
  • Sanity check: reconstruction_relative_l2_error < 1e-06
  • Revived: 24 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: SSALib: a Python Library for Time Series Decomposition using Singular Spectrum Analysis (doi:10.21105/joss.08600)
lazarus pull ssalib_sst_ssa

trRosetta trrosetta_predict

Predict inter-residue distance and orientation distributions from an MSA (feeds Rosetta folding).

  • Source: gjoni/trRosetta · MIT
  • Stack: Py · TensorFlow
  • Sanity check: pearson_r ≥ 0.99 · reproduced the paper: pearson_r of flattened <8A contact maps 1 vs 1
  • Revived: 14 autonomous agent-turns · from a bare URL (Scout-planned)
lazarus pull trrosetta_predict

W2W w2w_lcz_to_wrf

Inject WUDAPT Local Climate Zone maps into WRF geographic input for urban-canopy modelling.

  • Source: matthiasdemuzere/w2w · MIT
  • Stack: Py · geospatial
  • Sanity check: max_abs_diff_FRC_URB2D < 0.0001
  • Revived: 12 autonomous agent-turns · from a bare URL (Scout-planned)
  • Paper: W2W: A Python package that injects WUDAPT's Local Climate Zone information in WRF (doi:10.21105/joss.04432)
lazarus pull w2w_lcz_to_wrf