Screen compounds for Pan-Assay Interference patterns using PAINS_A, PAINS_B, and PAINS_C filters. Learn more
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PAINS screening
Screen compounds for Pan-Assay Interference patterns using PAINS_A, PAINS_B, and PAINS_C filters. Learn more
Configure inputs to begin
Set options on the left, then click “Submit job” — or start from an example.
PAINS screening
Screen compounds for Pan-Assay Interference patterns using PAINS_A, PAINS_B, and PAINS_C filters. Learn more
Configure inputs to begin
Set options on the left, then click “Submit job” — or start from an example.
PAINS screening
PAINS (Pan-Assay Interference Compounds) identifies small molecules with structural features that frequently cause false positives in biochemical assays. These problematic scaffolds interfere nonspecifically with diverse biological targets rather than binding selectively to one intended protein. Filtering PAINS from screening libraries prevents wasted effort chasing compounds that appear active but lack genuine therapeutic potential.
Baell and Holloway introduced PAINS filters in 2010 after analyzing high-throughput screening campaigns where certain compounds repeatedly appeared as hits across unrelated assays. These false actives share structural motifs that generate misleading signals through chemical reactivity, optical interference, or aggregation rather than specific target engagement.
ProteinIQ provides browser-based PAINS screening with all three filter families (PAINS_A, PAINS_B, PAINS_C) using RDKit's validated implementation.
| Input | Description |
|---|---|
Molecule | SMILES strings (one per line). Accepts plain SMILES or tab-separated format with compound names. Supports file upload (.smi, .csv, .txt) and PubChem batch fetching. |
Results indicate which compounds contain PAINS substructures:
| Column | Description |
|---|---|
Name | Compound identifier |
SMILES | Input structure |
PAINS alerts | Number of PAINS patterns detected |
Patterns | Descriptions of matched substructures (e.g., "ene_rhodanine", "hydroxyphenyl_hydrazone") |
Result | Pass (0 alerts), Fail (≥1 alert), or Error (invalid SMILES) |
Compounds flagged with any PAINS alerts warrant further scrutiny before advancing in screening pipelines.
PAINS generate false positives through multiple mechanisms unrelated to specific target binding:
The original filters comprise 480 substructures divided by statistical strength:
| Category | Substructures | Coverage | Reliability |
|---|---|---|---|
| PAINS_A | 16 patterns | 58% of flagged compounds | High confidence—supported by independent validation |
| PAINS_B | 55 patterns | 27% of flagged compounds | Moderate confidence—fewer training examples |
| PAINS_C | 409 patterns | 15% of flagged compounds | Low confidence—insufficient data for definitive classification |
Family A filters identify the most problematic chemotypes with ≥150 analogues in the training set. Common Family A patterns include alkylidene barbiturates, rhodanines, quinones, and hydroxyphenyl hydrazones. Family C filters flag rare substructures lacking robust statistical support and should be interpreted cautiously.
Zero alerts: Compound passes initial PAINS screening. Proceed with assay development and optimization.
One or more alerts: Exercise caution. Review matched patterns and consider:
Not all PAINS-flagged compounds behave promiscuously. Context matters—a rhodanine that fails in biochemical assays might succeed in phenotypic screens where reactivity is acceptable. However, most medicinal chemistry programs deprioritize PAINS early to avoid optimization dead ends.
PAINS filters remain controversial. Critics note that several FDA-approved drugs contain PAINS substructures, and some flagged scaffolds show genuine activity against specific targets. The original filters derive from a small set of assays (six AlphaScreen campaigns), potentially over-generalizing based on limited training data.
Family C filters particularly lack statistical support—15% of flagged compounds come from patterns with fewer than 15 examples. Using Family C as a hard filter risks discarding valid hit chemotypes.
Additionally, PAINS behavior depends on assay format and concentration. Compounds that interfere in AlphaScreen may perform cleanly in surface plasmon resonance or crystallography. Blanket rejection based solely on substructure matching ignores these nuances.
The filters serve best as triage tools rather than absolute rules. Flagged compounds deserve extra scrutiny, not automatic elimination.
Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns
Screen for lead-like compounds using stricter molecular descriptor criteria than Lipinski or Veber rules for early-stage drug discovery
Veber's Rule predicts oral bioavailability by evaluating molecular weight, LogP, hydrogen bond donors/acceptors, and rotatable bonds
Lipinski's Rule of Five predicts whether compounds will be orally bioavailable by evaluating molecular weight, LogP, hydrogen bond donors, and acceptors.
Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties from SMILES strings using machine learning models trained on Therapeutics Data Commons datasets.
Predict 22 ADMET properties from SMILES strings with the native Admetica Chemprop models from Datagrok.
Predict toxicity and synthetic accessibility of small molecules using machine learning. eToxPred combines toxicity risk assessment with synthetic accessibility scoring to help prioritize drug candidates.
Quantitative estimate for protein-protein interaction inhibitor potential. Evaluates drug-likeness for compounds targeting PPIs.
Screen compounds for structural toxicity alerts using PAINS, Brenk, and NIH filters. For focused screening, see PAINS Filter, Brenk Filter, or Veber's Rule.
AF2BIND predicts ligand-binding residues from a protein structure using AlphaFold2 pair representations and a 20-residue bait sequence.
PAINS (Pan-Assay Interference Compounds) identifies small molecules with structural features that frequently cause false positives in biochemical assays. These problematic scaffolds interfere nonspecifically with diverse biological targets rather than binding selectively to one intended protein. Filtering PAINS from screening libraries prevents wasted effort chasing compounds that appear active but lack genuine therapeutic potential.
Baell and Holloway introduced PAINS filters in 2010 after analyzing high-throughput screening campaigns where certain compounds repeatedly appeared as hits across unrelated assays. These false actives share structural motifs that generate misleading signals through chemical reactivity, optical interference, or aggregation rather than specific target engagement.
ProteinIQ provides browser-based PAINS screening with all three filter families (PAINS_A, PAINS_B, PAINS_C) using RDKit's validated implementation.
| Input | Description |
|---|---|
Molecule | SMILES strings (one per line). Accepts plain SMILES or tab-separated format with compound names. Supports file upload (.smi, .csv, .txt) and PubChem batch fetching. |
Results indicate which compounds contain PAINS substructures:
| Column | Description |
|---|---|
Name | Compound identifier |
SMILES | Input structure |
PAINS alerts | Number of PAINS patterns detected |
Patterns | Descriptions of matched substructures (e.g., "ene_rhodanine", "hydroxyphenyl_hydrazone") |
Result | Pass (0 alerts), Fail (≥1 alert), or Error (invalid SMILES) |
Compounds flagged with any PAINS alerts warrant further scrutiny before advancing in screening pipelines.
PAINS generate false positives through multiple mechanisms unrelated to specific target binding:
The original filters comprise 480 substructures divided by statistical strength:
| Category | Substructures | Coverage | Reliability |
|---|---|---|---|
| PAINS_A | 16 patterns | 58% of flagged compounds | High confidence—supported by independent validation |
| PAINS_B | 55 patterns | 27% of flagged compounds | Moderate confidence—fewer training examples |
| PAINS_C | 409 patterns | 15% of flagged compounds | Low confidence—insufficient data for definitive classification |
Family A filters identify the most problematic chemotypes with ≥150 analogues in the training set. Common Family A patterns include alkylidene barbiturates, rhodanines, quinones, and hydroxyphenyl hydrazones. Family C filters flag rare substructures lacking robust statistical support and should be interpreted cautiously.
Zero alerts: Compound passes initial PAINS screening. Proceed with assay development and optimization.
One or more alerts: Exercise caution. Review matched patterns and consider:
Not all PAINS-flagged compounds behave promiscuously. Context matters—a rhodanine that fails in biochemical assays might succeed in phenotypic screens where reactivity is acceptable. However, most medicinal chemistry programs deprioritize PAINS early to avoid optimization dead ends.
PAINS filters remain controversial. Critics note that several FDA-approved drugs contain PAINS substructures, and some flagged scaffolds show genuine activity against specific targets. The original filters derive from a small set of assays (six AlphaScreen campaigns), potentially over-generalizing based on limited training data.
Family C filters particularly lack statistical support—15% of flagged compounds come from patterns with fewer than 15 examples. Using Family C as a hard filter risks discarding valid hit chemotypes.
Additionally, PAINS behavior depends on assay format and concentration. Compounds that interfere in AlphaScreen may perform cleanly in surface plasmon resonance or crystallography. Blanket rejection based solely on substructure matching ignores these nuances.
The filters serve best as triage tools rather than absolute rules. Flagged compounds deserve extra scrutiny, not automatic elimination.
Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns
Screen for lead-like compounds using stricter molecular descriptor criteria than Lipinski or Veber rules for early-stage drug discovery
Veber's Rule predicts oral bioavailability by evaluating molecular weight, LogP, hydrogen bond donors/acceptors, and rotatable bonds
Lipinski's Rule of Five predicts whether compounds will be orally bioavailable by evaluating molecular weight, LogP, hydrogen bond donors, and acceptors.
Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties from SMILES strings using machine learning models trained on Therapeutics Data Commons datasets.
Predict 22 ADMET properties from SMILES strings with the native Admetica Chemprop models from Datagrok.
Predict toxicity and synthetic accessibility of small molecules using machine learning. eToxPred combines toxicity risk assessment with synthetic accessibility scoring to help prioritize drug candidates.
Quantitative estimate for protein-protein interaction inhibitor potential. Evaluates drug-likeness for compounds targeting PPIs.
Screen compounds for structural toxicity alerts using PAINS, Brenk, and NIH filters. For focused screening, see PAINS Filter, Brenk Filter, or Veber's Rule.
AF2BIND predicts ligand-binding residues from a protein structure using AlphaFold2 pair representations and a 20-residue bait sequence.