Lead-likeness filter
Screen for lead-like compounds using stricter molecular descriptor criteria for early optimization.
What is lead-likeness?
Lead-likeness refers to molecular properties that make compounds suitable as starting points for optimization in drug discovery. Unlike drug-likeness filters that assess final candidates, lead-likeness applies stricter thresholds because molecular properties typically increase during optimization—compounds gain molecular weight, become more lipophilic, and add rotatable bonds as medicinal chemists improve potency and selectivity.
The lead-likeness filter evaluates five molecular descriptors: molecular weight ( Da), lipophilicity (), hydrogen bond donors (), hydrogen bond acceptors (), and rotatable bonds (). These criteria are deliberately more conservative than Lipinski's Rule of Five (MW ≤ 500, LogP ≤ 5) or Veber's Rule to allow optimization headroom.
Why stricter criteria for leads?
Studies of medicinal chemistry programs show that final drug candidates are consistently larger and more lipophilic than the initial hits. The median molecular weight increase during optimization is approximately 70–100 Da, while LogP tends to increase by 1–2 units. Starting with smaller, less lipophilic leads preserves chemical space for structural modifications without violating drug-likeness boundaries.
Compounds that barely pass Lipinski's rules have limited room for optimization. Adding pharmacophores to improve target affinity often requires additional aromatic rings, alkyl chains, or functional groups—all of which increase MW and lipophilicity. Lead-likeness filters prevent "molecular obesity" by enforcing stricter initial boundaries.
How to use lead-likeness filter online
ProteinIQ runs the lead-likeness filter directly in the browser with instant results for batch compound screening. Enter SMILES strings in the text area (one per line) or upload a file. Compound names can be included using tab-separated format, or fetch compounds directly from PubChem.
Input
| Input | Accepted formats |
|---|---|
Molecule | SMILES strings (one per line), tab-separated name-SMILES pairs, PubChem CID batch fetcher |
| File formats | .txt, .smi, .csv, .smiles (up to 50 MB) |
Results
The filter calculates five descriptors and counts violations. Compounds pass only if all five criteria are satisfied (zero violations).
| Column | Description |
|---|---|
Name | Compound identifier |
SMILES | Input structure |
MW [Da] | Molecular weight. Limit: ≤ 350 Da. |
LogP | Octanol-water partition coefficient. Limit: ≤ 3. |
HBD | Hydrogen bond donors (OH, NH groups). Limit: ≤ 5. |
HBA | Hydrogen bond acceptors (N, O atoms). Limit: ≤ 8. |
Rot. Bonds | Rotatable single bonds (flexibility). Limit: ≤ 8. |
Violations | Number of criteria failures (0–5) |
Result | Pass (0 violations) or Fail (1+ violations) |
Interpreting violations
| Violations | Interpretation |
|---|---|
| 0 | Lead-like. Suitable for hit-to-lead optimization with room for structural modifications. |
| 1 | Borderline. May still be viable depending on which criterion failed. MW or LogP violations are more concerning than rotatable bonds. |
| 2+ | Non-lead-like. Limited optimization space. Consider for fragment-based approaches or re-scaffold. |
Comparison with other rules
Lead-likeness sits between fragment-likeness and drug-likeness on the molecular complexity spectrum.
| Rule | MW | LogP | HBD | HBA | RotBonds | Purpose |
|---|---|---|---|---|---|---|
| Fragment-like | < 300 | < 3 | ≤ 3 | ≤ 3 | ≤ 3 | Fragment-based drug discovery |
| Lead-likeness | ≤ 350 | ≤ 3 | ≤ 5 | ≤ 8 | ≤ 8 | Hit-to-lead optimization |
| Veber's Rule | ≤ 430 | ≤ 5 | ≤ 5 | ≤ 10 | ≤ 10 | Oral bioavailability |
| Lipinski's Ro5 | ≤ 500 | ≤ 5 | ≤ 5 | ≤ 10 | — | Drug-like oral drugs |
The tighter LogP threshold (3 vs. 5) is particularly important. Lipophilicity correlates strongly with off-target binding, toxicity, and poor solubility. Starting at LogP ≤ 3 reduces attrition during later stages.
Applications
Virtual screening
Filtering compound libraries before docking or phenotypic screening enriches for tractable leads. Removing compounds with MW > 350 or LogP > 3 improves hit quality and reduces false positives from aggregators or promiscuous binders.
Hit triaging
After high-throughput screening, lead-likeness helps prioritize hits for follow-up. Compounds passing the filter have better developability profiles and are less likely to fail due to physicochemical liabilities.
Scaffold selection
When choosing between multiple chemotypes with similar potency, lead-like scaffolds offer more optimization flexibility. A 320 Da compound with LogP 2.5 provides significantly more room for SAR exploration than a 450 Da compound with LogP 4.
Limitations
Lead-likeness filters are guidelines, not absolute rules. Many successful drugs originated from leads that violated these criteria—particularly natural product derivatives and macrocycles. Biological targets also matter: CNS drugs benefit from stricter criteria (MW < 400, LogP < 3), while antibacterial agents targeting efflux-prone organisms often require higher MW.
The filter does not assess structural alerts (PAINS, reactive groups), target-specific properties, or synthetic accessibility. Compounds that pass may still be unsuitable if they contain problematic substructures or are synthetically intractable.
Related tools
- Lipinski's Rule of 5: Drug-likeness filter with more permissive thresholds
- Veber's Rule: Oral bioavailability prediction based on rotatable bonds and polar surface area
- Molecular Descriptors: Calculate comprehensive physicochemical properties
- QEPPI: Specialized filter for protein-protein interaction inhibitors
- PAINS Filter: Screen for pan-assay interference compounds
- Brenk Filter: Identify toxic and pharmacokinetically problematic fragments