m3t

The M3TRIQ CLI — protein-ligand analysis from the terminal.

Query self-hosted databases (ChEMBL, FooDB, ZINC), predict ADMET properties, run molecular docking, and submit molecular dynamics simulations — all from the command line.

Install

npm install -g m3triq

Setup

m3t config --key <your-api-key>
m3t projects                  # list your projects
m3t use <project-id>          # set active project

Databases

ChEMBL (2.4M compounds, self-hosted)

m3t chembl search aspirin
m3t chembl info CHEMBL25
m3t chembl targets PDE3B
m3t chembl binders EGFR --max-value 1000 --save "EGFR Binders"
m3t chembl activities CHEMBL25 --type IC50
m3t chembl similar "CC(=O)Oc1ccccc1C(=O)O" --threshold 0.7
m3t chembl sql "SELECT chembl_id, pref_name FROM molecule_dictionary WHERE pref_name ILIKE '%caffeine%'"

FooDB (food compounds, self-hosted)

m3t foodb search tomato --limit 20
m3t foodb info caffeine
m3t foodb export banana --name "Banana Compounds"
m3t foodb random --count 50 --group Fruits

ZINC (14M purchasable compounds, self-hosted)

m3t zinc search --subset drug-like --mw-min 200 --mw-max 500
m3t zinc info ZINC000000000001
m3t zinc random --count 20 --subset lead-like

ADMET Prediction (41 properties)

Self-hosted ADMET-AI model. Predicts absorption, distribution, metabolism, excretion, and toxicity.

m3t admet predict "CC(=O)Oc1ccccc1C(=O)O"
m3t admet batch "CCO" "CC(=O)Oc1ccccc1C(=O)O" "CN1C=NC2=C1C(=O)N(C(=O)N2C)C"
m3t admet compare "CCO" "CCCO" --name1 Ethanol --name2 Propanol

Molecular Docking

# Single compound
m3t dock gnina "CCO" 5NJ8 --cx 10 --cy 20 --cz 30
m3t dock vina "CCO" 5NJ8 --cx 10 --cy 20 --cz 30
m3t dock diffdock "CCO" 5NJ8

# Batch (CSV with smiles column)
m3t batch gnina compounds.csv 5NJ8 --cx 10 --cy 20 --cz 30

Molecular Dynamics

GPU-accelerated simulations to validate docking poses.

m3t md run --protein 5NJ8 --ligand-smiles "CCO" --mode quick
m3t md run --diffdock-job <job-id> --mode standard
m3t md results <job-id>

Structure Prediction

m3t predict esmfold MKFLILLFNILCL...        # Fast (~10s, max 1024aa)
m3t predict alphafold2 MKFLILLFNILCL...     # Accurate (~15-20min, max 2048aa)

# ESMFold2-Fast — monomer or multi-chain complex (self-hosted A100, max 2048aa total).
# Beats AlphaFold3 on antibody-antigen DockQ from single sequence; returns pLDDT/pTM/ipTM.
m3t predict esmfold2 --sequence MKFLILLFNILCL...              # monomer
m3t predict esmfold2 --chain A:EVQL... --chain B:DIQM...      # complex (antibody-antigen, PPI)
m3t predict esmfold2-batch inputs.json                        # fold N complexes (results in input order)

# Boltz-2 — biomolecular complex (protein + DNA/RNA + ligand) with binding affinity
m3t predict boltz2 --protein MKFL... --ligand "CC(=O)O" --rna GGUC...

Protein Embeddings

# ESMC-6B per-residue embeddings + pseudo-perplexity (lower = more "natural" sequence)
m3t embed esmc --sequence MKFLILLFNILCL...           # single sequence
m3t embed esmc --sequence SEQ1 --sequence SEQ2       # batch (repeatable, max 32)

Protein Design

m3t design rfantibody 6VXX --hotspots A100,A105 --type nanobody --designs 3

Other Commands

m3t jobs                          # list recent jobs
m3t job <id>                      # job status & results
m3t data                          # list project datasets
m3t dataset <id> --limit 20      # view dataset rows
m3t run script.py --packages pandas,httpx --save "Results"

JSON Mode

Pipe-friendly output for scripting:

m3t --json chembl binders PDE3B | jq '.[].canonical_smiles'
m3t --json zinc search --subset drug-like | jq length

Project-per-folder

Like .git, each directory can be tied to a different M3TRIQ project:

cd ~/research/egfr && m3t use <project-id>    # writes .m3triq here
cd ~/research/pde3b && m3t use <project-id>   # different project
m3t use --global <id>                          # fallback

Links

• M3TRIQ Platform
• Documentation