---
title: Chai-1 results
description: 'One top-ranked mmCIF structure with aggregate_score, ptm, iptm and per-chain plddt_mean — how to read each, and why none of them is an affinity or an RMSD.'
---

A finished Chai-1 job returns the top-ranked candidate structure as mmCIF text, three confidence numbers for the complex, and a per-chain breakdown. A `chai_results` JSON artifact carries the whole result object.

## Result fields

| Field | Meaning |
|---|---|
| `structure_cif` | The top-ranked candidate structure, as mmCIF text |
| `aggregate_score` | The score the candidates were ranked by; higher is better. The returned structure is the highest-scoring one |
| `ptm` | Predicted TM-score for the complex |
| `iptm` | Interface predicted TM-score. `null` when there is only one entity, since there is no interface to score |
| `chains` | One entry per chain: `chain_id`, `sequence`, `n_residues`, and `plddt_mean` |
| `num_chains` | Number of protein chains folded. A ligand is not counted here, so a one-protein, one-ligand complex reports `1` |
| `num_models` | The `num_models` value carried with the request |

`plddt_mean` is reported on a 0–100 scale, as a mean over that chain's atoms.

## How to read them

- **`plddt_mean` judges each chain's own fold.** A low value on one chain means that chain's geometry is unreliable, whatever the complex-level scores say.
- **`iptm` estimates how accurately the interface was modelled** — not whether the chains bind. It is the number to read for a contact question, and it has real predictive power for pose correctness, but a high `iptm` is a statement about the model's own confidence in its geometry, not evidence of an interaction. Good chains with a weak interface is the common failure mode; so is good chains with a confident interface in the wrong place. See [translatability](/docs/chai/translatability).
- **`aggregate_score` is a ranking quantity**, so it is meaningful between candidates within one run, not across targets or across jobs.
- **None of these is an affinity.** Chai-1 returns no binding-energy or affinity value. For a model-predicted affinity, [Boltz-2](/docs/boltz2) has an affinity head; for physics-based rescoring of a pose, use [MM-GBSA](/docs/mmgbsa).

## Reliability guard

If inference scores the chains but no structure file is written, the job fails rather than returning an empty `structure_cif` alongside real-looking confidence numbers. A `chai_predict` job that reports success has a structure with atoms in it.

## What is not returned

- **No pose ensemble.** One top-ranked structure per job.
- **No RMSD or DockQ to a reference.** There is no reference-structure input, so if you want an agreement number for your own target you have to compute it yourself against a deposited structure. [Translatability](/docs/chai/translatability) covers what the published figures are and which metric they use.
- **No physical-validity check.** Inspect the geometry before building on it.

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