novomcp-properties

Trained ML models for physicochemical properties: pKa, aqueous solubility, and bond dissociation energy (BDE). CPU-only.

Pre-reqs

• Docker
• ~4 GB RAM
• No GPU
• ~500 MB disk for weights (bundled)

Deploy

docker run -d \
  --name novomcp-properties \
  -p 8036:8036 \
  --restart unless-stopped \
  ghcr.io/NovoMCP/novomcp-properties:latest

Wire into the engine

export NOVOMCP_PROPERTIES_URL=http://localhost:8036

Verify

curl -s http://localhost:8036/health
# {"status":"healthy","models":["pka","solubility","bde"]}

Tools that light up

• predict_pka, acidic/basic ionization constants
• predict_solubility, LogS (log molar aqueous solubility) with temperature dependence
• predict_bde, bond dissociation energies for radical chemistry

Env vars

Var	Default	Purpose
PORT	8036	HTTP listen port
BATCH_SIZE	64	Molecules per batch

Notes

• pKa model: Chemprop trained on IUPAC dataset; benchmarked against SAMPL8.
• Solubility model: pre-trained on AqSolDB, fine-tuned on BigSolDB with temperature as an input feature.
• BDE model: alfabet pretrained network.
• All three are stateless, safe to scale horizontally behind a load balancer for high-throughput screening.