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SAVI-2020

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metadata
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 

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:

{
  "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