Datasets:

liubuing
/

BioCandidateRanker-training-library

---
license: other
license_name: mixed-mit-cc0-cc-by-4.0
task_categories:
- other
tags:
- biology
- chemistry
- enzymes
- enzyme-kinetics
- kcat
- homology-split
- mmseqs2
- protein-language-model
- enzyme-engineering
size_category: 10K<n<100K
pretty_name: BioCandidateRanker Training Library (frozen splits, audits, and release archive)
---
# BioCandidateRanker Training Library

**中文说明见下方 [中文说明](#中文说明) 一节。**

This repository is the organized, license-compliant training library of the
[BioCandidateRanker](https://github.com/liubuing/BioCandidateRanker) project — a host-aware multimodal enzyme
candidate scoring research system whose first trained task predicts `log10(kcat)` from
protein sequence, substrate molecular graph, organism/EC/reaction context, and optional
FBA features.

It packages everything needed to **reproduce the exact training setup**: the frozen
homology-cold split assignments, ingestion audit manifests, source identity hashes,
external evaluation selections, and the official software release archive.

> **What is deliberately NOT here:** the UniKP/DLKcat measurement rows themselves
> (protein sequences + substrates + `kcat` values). The upstream compilation has no
> redistribution grant on record (`license: unknown`, record-level provenance
> corpus-level only), so the project's fail-closed license governance
> (`configs/data_license_inventory.json` in the archive) marks it
> `redistribution: not_verified` and blocks any measurement row from shipping in a
> public archive. See [Reconstructing the full training corpus](#reconstructing-the-full-training-corpus)
> to rebuild the corpus from the original public source and verify it bit-for-bit.
## Contents
```
data/
  homology_split.parquet          16,838 rows: source_row, sequence_id, cluster, split
  homology_split.json             original frozen split manifest (redacted paths)
  unikp_audit.json                ingestion audit of the development corpus (row counts,
                                  duplicates, conflicts, source SHA256; redacted path)
  unikp-km-training-blocker-audit.json      why governed Km training is blocked
  absolute-activity-training-blocker-audit.json
  full-homology-baseline/test_metrics.json  frozen baseline holdout metrics
  enzengdb-v1/                    EnzEngDB v1 selection, homology-cold audits,
                                  rank split, zero-shot transfer result, 3-seed summary
  lunzer-imdh/                    Lunzer IMDH selection, homology audit, frozen
                                  BioCandidateRanker and DLKcat evaluations
  temporal/                       temporal absolute-kinetics pool readiness audits,
                                  accepted-component FASTA and exact row mapping
  uncertainty-calibration-v2/grouped-cv.json
configs/
  unikp_source_manifest.json      source identity and adapter statistics
  data_license_inventory.json     the full license governance file governing this repo
  temporal_absolute_kinetics_protocol.json
release/
  software-implementation-v10-redacted.zip        official release archive (408 files)
  software-implementation-v10-redacted-manifest.json   per-file SHA256 manifest
RECONSTRUCTION.md                 step-by-step corpus reconstruction guide
```
## The frozen homology-cold split

The development corpus is split **protein-homology-cold** with MMseqs2 `linclust`
(30% min identity, 80% coverage, coverage mode 0, seed 42), producing 2,204 clusters.
Whole clusters are assigned to a partition, so no protein family leaks between
train and evaluation:

| split | rows |
|---|---|
| train | 13,470 |
| validation | 1,684 |
| test | 1,684 |

The split is keyed by `source_row` (the 0-based row index in the original
`Kcat_combination_0918_wildtype_mutant.json`), so it can be applied to any
byte-identical copy of the source file:

```python
from datasets import load_dataset
import json
split = load_dataset("<repo-id>", data_files="data/homology_split.parquet")["train"]
# row: {"source_row": 0, "sequence_id": "seq_...", "cluster": "seq_...", "split": "train"}
```

**Note:** the split manifest contains cluster/row/split assignments only — no
sequences and no `kcat` values — which is exactly why it is redistributable while
the corpus itself is not.

## Reconstructing the full training corpus

1. Obtain `Kcat_combination_0918_wildtype_mutant.json` from the original
   [UniKP repository](https://github.com/Luo-SynBioLab/UniKP) (the file is also the
   wildtype+mutant kcat compilation distributed with
   [DLKcat](https://zenodo.org/records/6438262)).
2. Verify the frozen source identity:

   | field | value |
   |---|---|
   | SHA256 | `13643b0b36374f8d3f64d8b014882cf1b3b58946eeaae2b9dcd59e8b2c2d6719` |
   | size | 12,132,719 bytes |
   | rows | 17,010 |

   The audit on this machine confirmed a byte-identical copy (2026-10-24).
3. Run the project's audit and training CLI (included in `release/*.zip`) as
   described in `RECONSTRUCTION.md`.

Full step-by-step instructions: **[RECONSTRUCTION.md](RECONSTRUCTION.md)**.

## External evaluation data included

| component | source | license | shipped content |
|---|---|---|---|
| EnzEngDB v1 | Zenodo DOI `10.5281/zenodo.17310823` | CC-BY-4.0 | selection manifests, homology-cold audit, rank split, evaluation results (the 30 MB source archive is **not** bundled — fetch it from the DOI) |
| Lunzer IMDH landscape | Dryad DOI `10.5061/dryad.7nd70` | CC0-1.0 | project-generated selection, homology audit, and frozen evaluations (source `.xls` not bundled) |
| Temporal absolute-kinetics pool | 18+ CC0 / CC-BY-4.0 deposits | CC0 / CC-BY-4.0 per source | readiness audits, accepted-component FASTA, row mapping (curation pool below its gates — never scored, **not** a benchmark) |

## Release archive

`release/software-implementation-v10-redacted.zip` is the official deterministic
release archive (`software-implementation-v10`): every Git-tracked source file plus
38 frozen evidence artifacts, built by `scripts/build_release_archive.py` with
license-ownership verification and absolute-path redaction. Its embedded and sidecar
manifests carry per-file SHA256 hashes; the archive SHA256 is
`34e42f5b211bd590b785abe3ff59ac2cfaaca801328ddef68c9b303044b394d8`.

The archive includes the trained baseline checkpoint
(`artifacts/full-homology-baseline/best.pt`), which the license inventory explicitly
permits as a derived artifact (weights are not a redistribution of measurements).

## License and attribution

- **This package's own content** (splits, audits, configs, code in the archive):
  MIT, © 2026 BioCandidateRanker authors.
- **EnzEngDB v1**: CC-BY-4.0 — cite the Zenodo deposit DOI `10.5281/zenodo.17310823`.
- **Lunzer IMDH landscape**: CC0-1.0 — Dryad DOI `10.5061/dryad.7nd70`.
- **UniKP/DLKcat corpus**: **not redistributed here.** If you reconstruct it, respect
  the upstream terms and cite the UniKP and DLKcat papers.

## Citation

If you use this split or the reconstruction protocol, please cite the upstream
sources and this dataset:

```bibtex
@software{biocandidateranker2026,
  title  = {BioCandidateRanker training library: frozen homology-cold splits and audits},
  author = {BioCandidateRanker authors},
  year   = {2026},
  note   = {software-implementation-v10 release archive}
}
```

## 中文说明

本仓库是 BioCandidateRanker 项目的**训练库合规整理包**,目标是让任何人都能精确重建训练设置:

- **包含**:冻结的 MMseqs2 同源冷划分(2,204 个簇,16,838 行 → train 13,470 / validation 1,684 / test 1,684,按原始文件行号索引)、摄入审计 manifest、源文件 SHA256 身份、EnzEngDB(CC-BY-4.0)与 Lunzer IMDH(CC0)的外部评估选择与结果、时间池就绪审计、以及官方 `software-implementation-v10` 全量发布档案(含基线模型权重 `best.pt`)。
- **不包含**:UniKP/DLKcat 训练语料的测量行本体(序列、底物、kcat 值)。项目许可治理文件 `configs/data_license_inventory.json` 将其标记为 `not_verified` 并明确禁止任何测量行进入公开档案——上游(BRENDA 编译)无逐条记录的再分发授权。请通过 `RECONSTRUCTION.md` 从原始公开来源获取并按 SHA256 校验。
- 训练语料原始出处:[UniKP (Luo-SynBioLab)](https://github.com/Luo-SynBioLab/UniKP)、[UniKP 论文 (Yu et al., Nat Commun)](https://pmc.ncbi.nlm.nih.gov/articles/PMC10713628)、[DLKcat 补充数据 (Zenodo)](https://zenodo.org/records/6438262)。

	---
	license: other
	license_name: mixed-mit-cc0-cc-by-4.0
	task_categories:
	- other
	tags:
	- biology
	- chemistry
	- enzymes
	- enzyme-kinetics
	- kcat
	- homology-split
	- mmseqs2
	- protein-language-model
	- enzyme-engineering
	size_category: 10K<n<100K
	pretty_name: BioCandidateRanker Training Library (frozen splits, audits, and release archive)
	---

	# BioCandidateRanker Training Library

	中文说明见下方 [中文说明](#中文说明) 一节。

	This repository is the organized, license-compliant training library of the
	[BioCandidateRanker](https://github.com/liubuing/BioCandidateRanker) project — a host-aware multimodal enzyme
	candidate scoring research system whose first trained task predicts `log10(kcat)` from
	protein sequence, substrate molecular graph, organism/EC/reaction context, and optional
	FBA features.

	It packages everything needed to reproduce the exact training setup: the frozen
	homology-cold split assignments, ingestion audit manifests, source identity hashes,
	external evaluation selections, and the official software release archive.

	> What is deliberately NOT here: the UniKP/DLKcat measurement rows themselves
	> (protein sequences + substrates + `kcat` values). The upstream compilation has no
	> redistribution grant on record (`license: unknown`, record-level provenance
	> corpus-level only), so the project's fail-closed license governance
	> (`configs/data_license_inventory.json` in the archive) marks it
	> `redistribution: not_verified` and blocks any measurement row from shipping in a
	> public archive. See [Reconstructing the full training corpus](#reconstructing-the-full-training-corpus)
	> to rebuild the corpus from the original public source and verify it bit-for-bit.

	## Contents

	```
	data/
	homology_split.parquet 16,838 rows: source_row, sequence_id, cluster, split
	homology_split.json original frozen split manifest (redacted paths)
	unikp_audit.json ingestion audit of the development corpus (row counts,
	duplicates, conflicts, source SHA256; redacted path)
	unikp-km-training-blocker-audit.json why governed Km training is blocked
	absolute-activity-training-blocker-audit.json
	full-homology-baseline/test_metrics.json frozen baseline holdout metrics
	enzengdb-v1/ EnzEngDB v1 selection, homology-cold audits,
	rank split, zero-shot transfer result, 3-seed summary
	lunzer-imdh/ Lunzer IMDH selection, homology audit, frozen
	BioCandidateRanker and DLKcat evaluations
	temporal/ temporal absolute-kinetics pool readiness audits,
	accepted-component FASTA and exact row mapping
	uncertainty-calibration-v2/grouped-cv.json
	configs/
	unikp_source_manifest.json source identity and adapter statistics
	data_license_inventory.json the full license governance file governing this repo
	temporal_absolute_kinetics_protocol.json
	release/
	software-implementation-v10-redacted.zip official release archive (408 files)
	software-implementation-v10-redacted-manifest.json per-file SHA256 manifest
	RECONSTRUCTION.md step-by-step corpus reconstruction guide
	```

	## The frozen homology-cold split

	The development corpus is split protein-homology-cold with MMseqs2 `linclust`
	(30% min identity, 80% coverage, coverage mode 0, seed 42), producing 2,204 clusters.
	Whole clusters are assigned to a partition, so no protein family leaks between
	train and evaluation:

	\| split \| rows \|
	\|---\|---\|
	\| train \| 13,470 \|
	\| validation \| 1,684 \|
	\| test \| 1,684 \|

	The split is keyed by `source_row` (the 0-based row index in the original
	`Kcat_combination_0918_wildtype_mutant.json`), so it can be applied to any
	byte-identical copy of the source file:

	```python
	from datasets import load_dataset
	import json

	split = load_dataset("<repo-id>", data_files="data/homology_split.parquet")["train"]
	# row: {"source_row": 0, "sequence_id": "seq_...", "cluster": "seq_...", "split": "train"}
	```

	Note: the split manifest contains cluster/row/split assignments only — no
	sequences and no `kcat` values — which is exactly why it is redistributable while
	the corpus itself is not.

	## Reconstructing the full training corpus

	1. Obtain `Kcat_combination_0918_wildtype_mutant.json` from the original
	[UniKP repository](https://github.com/Luo-SynBioLab/UniKP) (the file is also the
	wildtype+mutant kcat compilation distributed with
	[DLKcat](https://zenodo.org/records/6438262)).
	2. Verify the frozen source identity:

	\| field \| value \|
	\|---\|---\|
	\| SHA256 \| `13643b0b36374f8d3f64d8b014882cf1b3b58946eeaae2b9dcd59e8b2c2d6719` \|
	\| size \| 12,132,719 bytes \|
	\| rows \| 17,010 \|

	The audit on this machine confirmed a byte-identical copy (2026-10-24).
	3. Run the project's audit and training CLI (included in `release/*.zip`) as
	described in `RECONSTRUCTION.md`.

	Full step-by-step instructions: [RECONSTRUCTION.md](RECONSTRUCTION.md).

	## External evaluation data included

	\| component \| source \| license \| shipped content \|
	\|---\|---\|---\|---\|
	\| EnzEngDB v1 \| Zenodo DOI `10.5281/zenodo.17310823` \| CC-BY-4.0 \| selection manifests, homology-cold audit, rank split, evaluation results (the 30 MB source archive is not bundled — fetch it from the DOI) \|
	\| Lunzer IMDH landscape \| Dryad DOI `10.5061/dryad.7nd70` \| CC0-1.0 \| project-generated selection, homology audit, and frozen evaluations (source `.xls` not bundled) \|
	\| Temporal absolute-kinetics pool \| 18+ CC0 / CC-BY-4.0 deposits \| CC0 / CC-BY-4.0 per source \| readiness audits, accepted-component FASTA, row mapping (curation pool below its gates — never scored, not a benchmark) \|

	## Release archive

	`release/software-implementation-v10-redacted.zip` is the official deterministic
	release archive (`software-implementation-v10`): every Git-tracked source file plus
	38 frozen evidence artifacts, built by `scripts/build_release_archive.py` with
	license-ownership verification and absolute-path redaction. Its embedded and sidecar
	manifests carry per-file SHA256 hashes; the archive SHA256 is
	`34e42f5b211bd590b785abe3ff59ac2cfaaca801328ddef68c9b303044b394d8`.

	The archive includes the trained baseline checkpoint
	(`artifacts/full-homology-baseline/best.pt`), which the license inventory explicitly
	permits as a derived artifact (weights are not a redistribution of measurements).

	## License and attribution

	- This package's own content (splits, audits, configs, code in the archive):
	MIT, © 2026 BioCandidateRanker authors.
	- EnzEngDB v1: CC-BY-4.0 — cite the Zenodo deposit DOI `10.5281/zenodo.17310823`.
	- Lunzer IMDH landscape: CC0-1.0 — Dryad DOI `10.5061/dryad.7nd70`.
	- UniKP/DLKcat corpus: not redistributed here. If you reconstruct it, respect
	the upstream terms and cite the UniKP and DLKcat papers.

	## Citation

	If you use this split or the reconstruction protocol, please cite the upstream
	sources and this dataset:

	```bibtex
	@software{biocandidateranker2026,
	title = {BioCandidateRanker training library: frozen homology-cold splits and audits},
	author = {BioCandidateRanker authors},
	year = {2026},
	note = {software-implementation-v10 release archive}
	}
	```

	## 中文说明

	本仓库是 BioCandidateRanker 项目的训练库合规整理包,目标是让任何人都能精确重建训练设置:

	- 包含:冻结的 MMseqs2 同源冷划分(2,204 个簇,16,838 行 → train 13,470 / validation 1,684 / test 1,684,按原始文件行号索引)、摄入审计 manifest、源文件 SHA256 身份、EnzEngDB(CC-BY-4.0)与 Lunzer IMDH(CC0)的外部评估选择与结果、时间池就绪审计、以及官方 `software-implementation-v10` 全量发布档案(含基线模型权重 `best.pt`)。
	- 不包含:UniKP/DLKcat 训练语料的测量行本体(序列、底物、kcat 值)。项目许可治理文件 `configs/data_license_inventory.json` 将其标记为 `not_verified` 并明确禁止任何测量行进入公开档案——上游(BRENDA 编译)无逐条记录的再分发授权。请通过 `RECONSTRUCTION.md` 从原始公开来源获取并按 SHA256 校验。
	- 训练语料原始出处:[UniKP (Luo-SynBioLab)](https://github.com/Luo-SynBioLab/UniKP)、[UniKP 论文 (Yu et al., Nat Commun)](https://pmc.ncbi.nlm.nih.gov/articles/PMC10713628)、[DLKcat 补充数据 (Zenodo)](https://zenodo.org/records/6438262)。