transformation products
GCI-TPs is a database of predicted transformation products (TPs) for compounds in the ZeroPM global chemical inventory, designed to help surface potentially persistent, mobile and toxic (PMT) or very persistent and very mobile (vPvM) TPs.
Why this matters
Novel entities are growing faster than the world can assess risk. Transformation products may be as concerning as (or more concerning than) their parent compounds.
Novel entities pressure
Global inventories contain hundreds of thousands of industrial chemicals and mixtures, far outstripping current risk assessment capacity.
PMT & vPvM concern
Persistent, mobile and toxic compounds can travel long distances in water systems and pose long-term ecosystem and human health risks.
Transformation gap
Many chemicals transform via biotic and abiotic processes. The resulting TPs are under-characterized, limiting monitoring and assessment.
From structures to networks
A quick interactive walkthrough of the pipeline that built GCI-TPs.
SMILES for global inventory compounds were retrieved (via PubChem) and standardized to consistent identifiers (InChI / InChIKey) using RDKit.
Salts/mixtures were split, elemental composition computed, and compounds outside the applicability domain removed (elements outside CHONPS + halogens, MW > 1000 Da, first-step errors).
BioTransformer environmental microbial and abiotic modules were run for three steps each. A parallel helper pipeline distributed workloads across HPC nodes with adaptive batching and recovery on memory limits.
A directed TP network was built using IKFB nodes and reaction edges. Dead-end products were defined as nodes that generate no further predicted TPs (and were not removed due to errors). Shortest path lengths from each starting compound were computed.
For dead-ends, mobility/persistence/toxicity were predicted using OPERA modules (KOC, CERAPP, CoMPARA) with applicability domain rules (AD index thresholds). Mobility thresholds follow EU-CLP criteria (log Koc ≤ 3, very mobile ≤ 2).
Coverage & comparison
Prediction complements experimental TP knowledge: overlap exists, but gaps remain in both reactions and chemical space.
What you can do on this site
Fast discovery workflows built on a graph database — optimized for exploration.
Search transformation products and precursors
Look up predicted TPs by identifier or structure, and browse dead-end products for candidate prioritization. Filters help you focus on mobility/toxicity evidence and prediction provenance.
See predicted transformation pathways
Visualize multi-generation pathways as a directed network from a precursor to its predicted products. This helps connect observed features in samples to plausible origins.
Prioritize PMT/vPvM candidates
Explore OPERA mobility and toxicity outputs with applicability-domain guidance. PMT prioritization is designed to highlight candidates for follow-up and experimental validation.