README.md

---
tags:
- chemistry
- cheminformatics
- drug-discovery
- organic-chemistry
- molecular-generation
- small-molecules
- SAVI
- huggingscience
- science
pretty_name: Synthetically Accessible Virtual Inventory (SAVI) 2020
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: product_name
    dtype: string
  - name: product_hashisy
    dtype: string
  - name: product_smiles
    dtype: string
  - name: formula
    dtype: string
  - name: weight
    dtype: float64
  - name: rotbonds
    dtype: int64
  - name: h_donors
    dtype: int64
  - name: h_acceptors
    dtype: int64
  - name: e_tpsa
    dtype: float64
  - name: fsp3
    dtype: float64
  - name: complexity
    dtype: float64
  - name: product_qed_score
    dtype: float64
  - name: benzenoid_index
    dtype: float64
  - name: charged_group_counts
    dtype: string
  - name: hydrogen_bond_center_count
    dtype: int64
  - name: heavy_atoms
    dtype: int64
  - name: stereo_count
    dtype: string
  - name: ring_system_count
    dtype: int64
  - name: e_nesssr
    dtype: int64
  - name: e_nsssr
    dtype: int64
  - name: product_aroring_count
    dtype: int64
  - name: product_aliring_count
    dtype: int64
  - name: stdinchi
    dtype: string
  - name: stdinchikey
    dtype: string
  - name: product_stereo_tauto_hash
    dtype: string
  - name: ro5_violations
    dtype: float64
  - name: ro3_violations
    dtype: float64
  - name: pains_filter_match_count
    dtype: int64
  - name: pains_filter_match_name
    dtype: string
  - name: genotoxic_alerts
    dtype: string
  - name: xlogp2
    dtype: float64
  - name: xlogp
    dtype: float64
  - name: bw_demerit_score
    dtype: int64
  - name: bw_demerit_components
    dtype: string
  - name: reaction_hashisy
    dtype: string
  - name: reaction_smiles
    dtype: string
  - name: transform_name
    dtype: string
  - name: transform_id
    dtype: int64
  - name: lhasa_score
    dtype: int64
  - name: min_delta
    dtype: int64
  - name: lhasa_scoring_history
    dtype: string
  - name: lhasa_reaction_conditions
    dtype: string
  - name: lhasa_reaction_actual_conditions
    dtype: string
  - name: lhasa_reaction_warnings
    dtype: string
  - name: reaction_delta_ring_count
    dtype: int64
  - name: reaction_delta_aroring_count
    dtype: int64
  - name: reaction_delta_aliring_count
    dtype: int64
  - name: rinchi
    dtype: string
  - name: rinchikey_long
    dtype: string
  - name: rinchikey_short
    dtype: string
  - name: rinchikey_web
    dtype: string
  - name: r1_hashisy
    dtype: string
  - name: r1_smiles
    dtype: string
  - name: r1_bbsource
    dtype: string
  - name: r1_ident
    dtype: string
  - name: r1_inchi
    dtype: string
  - name: r1_inchikey
    dtype: string
  - name: r1_protection_required
    dtype: int64
  - name: r1_protected
    dtype: int64
  - name: r1_url
    dtype: string
  - name: r2_hashisy
    dtype: string
  - name: r2_smiles
    dtype: string
  - name: r2_bbsource
    dtype: string
  - name: r2_ident
    dtype: string
  - name: r2_inchi
    dtype: string
  - name: r2_inchikey
    dtype: string
  - name: r2_protection_required
    dtype: int64
  - name: r2_protected
    dtype: int64
  - name: r2_url
    dtype: string
  - name: fmt_chunkid
    dtype: int64
  - name: score_class
    dtype: string
  - name: record_number
    dtype: int64
  splits:
  - name: train
    num_examples: 1526316392
  download_size: 178348923000
  dataset_size: 250000000000
size_categories:
- 1B<n<10B
---

# Synthetically Accessible Virtual Inventory (SAVI) 2020

## Dataset Description

### Dataset Summary

The Synthetically Accessible Virtual Inventory (SAVI) 2020 dataset is a comprehensive collection of over 1.5 billion computationally generated organic compounds designed to be easily and practically synthesizable. Created through expert-system type rules derived from established organic chemistry knowledge, SAVI represents one of the largest publicly available databases of synthetically accessible virtual compounds for drug discovery and chemical research.

The database was generated using 53 carefully selected chemical transformation rules encoded in the CHMTRN/PATRAN programming languages, originally developed for the LHASA (Logic and Heuristics Applied to Synthetic Analysis) retrosynthetic analysis system. These transforms were applied to approximately 152,532 commercially available building blocks from Enamine to create molecules through single-step, two-reactant synthesis pathways.

SAVI compounds are particularly valuable for virtual screening, drug discovery, and computational chemistry applications because each molecule comes with:
- A proposed single-step synthetic route
- Predicted synthetic accessibility scores
- Comprehensive molecular property annotations
- Quality assessments based on drug-likeness criteria

### Supported Tasks

- **Virtual Screening**: High-throughput computational screening of large chemical libraries for drug discovery
- **Chemical Space Exploration**: Systematic exploration of synthetically accessible chemical space
- **Lead Optimization**: Structure-activity relationship (SAR) studies using built-in compound series
- **Synthetic Route Planning**: Each compound includes reactants and transformation information for synthesis planning
- **Property Prediction**: Training machine learning models for molecular property prediction using the extensive annotations

## Getting Started

### Quick Start

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Metanova/SAVI-2020")

# View basic information
print(f"Dataset size: {len(dataset['train'])}")
print(f"Features: {dataset['train'].features}")

# Examine first few examples
print(dataset["train"][:5])
```

## Dataset Structure

### Data Instances

Each instance represents a computationally generated compound with comprehensive annotations:

```json
{
  "product_name": "61A77BD3826E0AD0_554BFAE008D564C8_6031_UN",
  "product_hashisy": "61A77BD3826E0AD0",
  "product_smiles": "O(C(=O)C1=C(OC=N1)C2=CC=CS2)CCC3OCCC3C",
  "formula": "C15H17NO4S",
  "weight": 307.3636,
  "rotbonds": 6,
  "h_donors": 0,
  "h_acceptors": 6,
  "e_tpsa": 89.8,
  "fsp3": 0.4667,
  "complexity": 367.5828,
  "product_qed_score": 0.7358,
  "pains_filter_match_count": 0,
  "bw_demerit_score": 35,
  "transform_name": "Mitsunobu Reaction",
  "transform_id": 6031,
  "r1_smiles": "OC(=O)C1=C(OC=N1)C2=CC=CS2",
  "r1_ident": "EN300-72222",
  "r2_smiles": "CC1CCOC1CCO",
  "r2_ident": "EN300-6499501",
  "score_class": "plus",
  "stdinchi": "InChI=1S/C15H17NO4S/c1-10-4-6-18-11(10)5-7-19-15(17)13-14(20-9-16-13)12-3-2-8-21-12/h2-3,8-11H,4-7H2,1H3",
  "stdinchikey": "InChIKey=FPQADJBFVMQUTQ-UHFFFAOYSA-N"
}
```

### Data Fields

| Field Name | Type | Description |
|------------|------|-------------|
| **Product Information** |
| `product_name` | `string` | Unique SAVI identifier with hash and transform information |
| `product_hashisy` | `string` | Hash identifier for the product structure |
| `product_smiles` | `string` | SMILES representation of the generated product |
| `formula` | `string` | Molecular formula (e.g., "C15H17NO4S") |
| `weight` | `float64` | Molecular weight in Daltons |
| `stdinchi` | `string` | Standard International Chemical Identifier |
| `stdinchikey` | `string` | Standard InChI Key for database searching |
| `product_stereo_tauto_hash` | `string` | Hash considering stereochemistry and tautomers |
| **Molecular Properties** |
| `rotbonds` | `int64` | Number of rotatable bonds (flexibility measure) |
| `h_donors` | `int64` | Number of hydrogen bond donors |
| `h_acceptors` | `int64` | Number of hydrogen bond acceptors |
| `e_tpsa` | `float64` | Topological Polar Surface Area in Ų |
| `fsp3` | `float64` | Fraction of sp³ hybridized carbons (3D character) |
| `complexity` | `float64` | Bertz/Hendrickson molecular complexity score |
| `heavy_atoms` | `int64` | Total number of non-hydrogen atoms |
| `hydrogen_bond_center_count` | `int64` | Count of hydrogen bonding centers |
| **Drug-likeness & Quality** |
| `product_qed_score` | `float64` | Quantitative Estimate of Drug-likeness (0-1, higher better) |
| `ro5_violations` | `float64` | Number of Lipinski's Rule of Five violations |
| `ro3_violations` | `float64` | Number of Rule of Three violations |
| `pains_filter_match_count` | `int64` | Number of PAINS (Pan Assay Interference) alerts |
| `pains_filter_match_name` | `string` | Names of matching PAINS patterns |
| `bw_demerit_score` | `int64` | Bruns & Watson demerits (lower better) |
| `bw_demerit_components` | `string` | Components contributing to B&W demerits |
| `genotoxic_alerts` | `string` | Genotoxic structural alerts |
| **Lipophilicity** |
| `xlogp2` | `float64` | Predicted partition coefficient (lipophilicity) |
| `xlogp` | `float64` | Alternative XlogP calculation |
| **Ring Analysis** |
| `ring_system_count` | `int64` | Number of ring systems |
| `product_aroring_count` | `int64` | Number of aromatic rings |
| `product_aliring_count` | `int64` | Number of aliphatic rings |
| `e_nesssr` | `int64` | Number of rings in smallest set of smallest rings |
| `e_nsssr` | `int64` | Number of smallest set of smallest rings |
| `benzenoid_index` | `float64` | Measure of benzenoid character |
| **Stereochemistry** |
| `stereo_count` | `string` | Stereochemistry counts (format: "chiral_centers undefined_stereo defined_stereo etc.") |
| `charged_group_counts` | `string` | Count of charged groups |
| **Reaction Information** |
| `reaction_hashisy` | `string` | Hash identifier for the reaction |
| `reaction_smiles` | `string` | Reaction SMILES showing transformation |
| `transform_name` | `string` | Name of the chemical transformation (e.g., "Mitsunobu Reaction") |
| `transform_id` | `int64` | Numerical identifier for the transformation |
| `lhasa_score` | `int64` | LHASA expert system score for reaction feasibility |
| `lhasa_scoring_history` | `string` | Detailed scoring breakdown |
| `lhasa_reaction_conditions` | `string` | Recommended reaction conditions |
| `lhasa_reaction_actual_conditions` | `string` | Actual conditions used in computation |
| `lhasa_reaction_warnings` | `string` | Any warnings about the reaction |
| `min_delta` | `int64` | Minimum score delta |
| `score_class` | `string` | Reaction success class ("plus", "neg0", "neg10", "neg20", "neg30") |
| **Ring Formation Analysis** |
| `reaction_delta_ring_count` | `int64` | Change in total ring count |
| `reaction_delta_aroring_count` | `int64` | Change in aromatic ring count |
| `reaction_delta_aliring_count` | `int64` | Change in aliphatic ring count |
| **Reaction Identifiers** |
| `rinchi` | `string` | Reaction InChI identifier |
| `rinchikey_long` | `string` | Long format Reaction InChI Key |
| `rinchikey_short` | `string` | Short format Reaction InChI Key |
| `rinchikey_web` | `string` | Web format Reaction InChI Key |
| **Reactant 1 (R1) Information** |
| `r1_hashisy` | `string` | Hash identifier for first reactant |
| `r1_smiles` | `string` | SMILES of first building block |
| `r1_bbsource` | `string` | Source of building block (e.g., "ENAMINE2019Q4") |
| `r1_ident` | `string` | Building block catalog identifier |
| `r1_inchi` | `string` | InChI of first reactant |
| `r1_inchikey` | `string` | InChI Key of first reactant |
| `r1_protection_required` | `int64` | Whether protecting groups were required (0/1) |
| `r1_protected` | `int64` | Whether reactant was protected (0/1) |
| `r1_url` | `string` | URL to purchase building block |
| **Reactant 2 (R2) Information** |
| `r2_hashisy` | `string` | Hash identifier for second reactant |
| `r2_smiles` | `string` | SMILES of second building block |
| `r2_bbsource` | `string` | Source of building block |
| `r2_ident` | `string` | Building block catalog identifier |
| `r2_inchi` | `string` | InChI of second reactant |
| `r2_inchikey` | `string` | InChI Key of second reactant |
| `r2_protection_required` | `int64` | Whether protecting groups were required (0/1) |
| `r2_protected` | `int64` | Whether reactant was protected (0/1) |
| `r2_url` | `string` | URL to purchase building block |
| **Technical Fields** |
| `fmt_chunkid` | `int64` | Data chunk identifier for processing |
| `record_number` | `int64` | Record number within dataset |

### Data Splits

The dataset contains approximately 1.53 billion unique compounds in a single training split.

## Applications and Use Cases

### Drug Discovery
- **Virtual Screening**: Large-scale computational screening for hit identification
- **Lead Optimization**: SAR exploration using built-in compound series
- **Chemical Space Navigation**: Systematic exploration of accessible chemical space

### Computational Chemistry
- **Property Prediction**: Training datasets for QSAR and machine learning models
- **Synthetic Route Planning**: Integration with retrosynthetic analysis tools
- **Chemical Space Analysis**: Diversity and novelty assessment studies

### Academic Research
- **Method Development**: Benchmarking virtual screening and ML approaches
- **Chemical Education**: Examples of structure-property relationships
- **Open Science**: Publicly available alternative to proprietary chemical libraries



**Original Paper:**
```
Patel, H., Ihlenfeldt, W.D., Judson, P.N. et al. 
SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules. 
Sci Data 7, 384 (2020). 
https://doi.org/10.1038/s41597-020-00727-4
```