Screen for lead-like compounds using stricter molecular descriptor criteria for early optimization. Learn more
Configure inputs to begin
Set options on the left, then click “Submit job” — or start from an example.
Lead-likeness screening
Screen for lead-like compounds using stricter molecular descriptor criteria for early optimization. Learn more
Configure inputs to begin
Set options on the left, then click “Submit job” — or start from an example.
Lead-likeness screening
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.
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.
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 | 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) |
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) |
| 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. |
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.
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.
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.
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.
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.
Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns
Screen compounds for Pan-Assay Interference patterns that cause false positives in biological assays
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.
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.
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.
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 | 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) |
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) |
| 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. |
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.
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.
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.
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.
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.
Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns
Screen compounds for Pan-Assay Interference patterns that cause false positives in biological assays
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.