PAINS filter

What is PAINS filter?

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.

How to use PAINS filter online

ProteinIQ provides browser-based PAINS screening with all three filter families (PAINS_A, PAINS_B, PAINS_C) using RDKit's validated implementation.

Inputs

Results

Results indicate which compounds contain PAINS substructures:

Compounds flagged with any PAINS alerts warrant further scrutiny before advancing in screening pipelines.

How PAINS cause assay interference

PAINS generate false positives through multiple mechanisms unrelated to specific target binding:

• Chemical reactivity: Many PAINS contain electrophilic groups (Michael acceptors, quinones) that react with nucleophilic residues like cysteines and lysines. This nonspecific protein modification creates apparent activity that disappears in counter-assays or optimization.
• Redox cycling: Quinones and catechols undergo oxidation-reduction reactions that generate reactive oxygen species, leading to protein damage and artifacts in cell-based assays.
• Metal chelation: Hydroxyphenyl hydrazones and related scaffolds bind metal ions in proteins or assay buffers, disrupting catalytic sites and creating concentration-dependent interference.
• Aggregation: Rhodanines and similar structures form colloidal aggregates that sequester proteins nonspecifically. Aggregate-based inhibition reverses upon dilution or detergent addition, distinguishing it from genuine binding.
• Optical interference: Azo compounds and extended conjugated systems absorb at wavelengths used in fluorescence and absorbance assays, creating signal artifacts independent of biological activity.

PAINS filter categories

The original filters comprise 480 substructures divided by statistical strength:

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.

Interpreting results

Zero alerts: Compound passes initial PAINS screening. Proceed with assay development and optimization.

One or more alerts: Exercise caution. Review matched patterns and consider:

• Assay conditions: Reduce test concentrations below aggregation thresholds ($\leq$ 10 μM). Include detergent (0.01% Triton X-100) to disrupt aggregates.
• Counter-assays: Confirm hits in orthogonal assays using different detection methods (fluorescence polarization vs. AlphaScreen).
• Dose-response: Genuine binders show classical sigmoidal curves. PAINS often exhibit steep, atypical Hill slopes or non-saturating inhibition.
• Time dependence: Covalent PAINS show increasing inhibition over incubation time.

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.

Limitations

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.

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