Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.
Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.
tool_type
python
primary_tool
RDKit
Version Compatibility
Reference examples tested with: RDKit 2024.09+ (Open3DAlign and USRCAT); official ShaEP syntax checked against ShaEP 1.4.2; ROCS/FastROCS/ROCS X are commercial OpenEye products.
Before using code patterns, verify installed versions match. If versions differ:
Python: pip show <package> then help(module.function) to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Shape Similarity
Search for compounds with similar 3D shape (and optionally chemical features) to a query molecule. Shape-based screening complements 2D fingerprint search: it can find scaffold-hopped compounds that ECFP4 misses (different scaffolds with similar shape). ROCS (OpenEye) is the industry-standard commercial tool; Open3DAlign (RDKit), USRCAT (Schreyer & Blundell 2012), and ShaEP are open-source alternatives. Modern best practice combines shape with color (chemical-feature similarity) via Tanimoto-Combo: matches share both shape and pharmacophore feature distribution.
For 2D fingerprint similarity, see chemoinformatics/similarity-searching. For pharmacophore search (discrete feature constraints), see chemoinformatics/pharmacophore-modeling. For 3D conformer generation, see chemoinformatics/conformer-generation.
Shape Method Taxonomy
Tool
Speed
Approach
Open-source
Fails when
ROCS / FastROCS (OpenEye)
Hardware/database/conformer-dependent; vendor reports millions of conformers/s for FastROCS
Gaussian shape + color
No
License and prepared database
ROCS X
Trillion-scale reaction/synthon space on Orion
FastROCS plus Bayesian-bandit sampling
No
Commercial cloud workflow
USRCAT
Very fast alignment-free descriptor comparison
Moment-based + atom types
Yes
Coarse approximation
Open3DAlign (RDKit)
Medium
MMFF atom-type/charge-weighted alignment
Yes
Requires compatible typed 3D structures
ShaEP
Benchmark on actual conformers/hardware
Field-based (shape + ESP)
Free binary; inspect license
Requires valid 3D structures and charges for ESP
ESPSim
Benchmark on actual workload
Electrostatic + shape
Yes
Limited public benchmarks
Phase-Shape (Schrödinger)
commercial
Shape + pharmacophore
No
Commercial
USR (original)
Very fast alignment-free comparison
Moment-based only
Yes
No atom-type information
Decision: Select a shape method by matched retrieval/enrichment performance, conformer preparation, throughput, licensing, and score semantics. USRCAT is useful as a fast prefilter; Open3DAlign provides an open alignment method; ROCS/FastROCS provide commercial shape/color workflows.
Decision Tree by Scenario
Scenario
Method
Notes
Large prepared library
USRCAT pre-filter + Open3DAlign rescore
Choose rescore budget from measured retrieval saturation
Production VS for scaffold hop
ROCS + color (commercial)
Industry standard
Scaffold hopping prospective
Open3DAlign with conformer ensemble
Shape + flexibility
Bioisostere replacement
ROCS color with neutral scoring
Pharmacophore-equivalent matches
Patent space carve-out
Shape constraint + 2D dissimilarity
Combine shape + dissimilar scaffold
Library diversity assessment
USRCAT k-nearest neighbor
Fast
Crystal-bound conformer template
Open3DAlign starting from co-crystal pose
Bioactive shape
Cross-target screening
Shape + pharmacophore feature
Combined screen
Tanimoto-Combo Scoring (ROCS Standard)
TanimotoCombo = Tanimoto_shape + Tanimoto_color
Tanimoto_shape: volume overlap normalized
Tanimoto_color: pharmacophore feature overlap
Each component is normalized from 0 to 1, so TanimotoCombo ranges from 0 to 2. It is a sum, not an average. Select follow-up thresholds from a relevant benchmark or enrichment study; a single cutoff is not portable across query preparation, color-force-field settings, and library composition.
USRCAT (Ultra-Fast Shape Recognition + Atom Types)
USRCAT (Schreyer & Blundell 2012) extends Ultrafast Shape Recognition (USR) with atom-type information. Each molecule is represented as a 60-dimensional moment vector (12 moments × 5 atom types).
Goal: Encode a molecule into the 60-D USRCAT moment vector and score similarity against another molecule for alignment-free shape search.
Approach: Parse the SMILES, add hydrogens, generate one 3D conformer with ETKDGv3, compute RDKit USRCAT descriptors, and compare descriptor vectors with RDKit's USR score.
from rdkit.Chem import rdMolDescriptors
mol = Chem.MolFromSmiles('CCO')
mol = Chem.AddHs(mol)
AllChem.EmbedMolecule(mol, AllChem.ETKDGv3())
descriptors = rdMolDescriptors.GetUSRCAT(mol)
# Returns numpy array of 60 floats: 12 USR moments x 5 atom types# (all atoms, hydrophobic, aromatic, acceptor, donor)
similarity = rdMolDescriptors.GetUSRScore(desc1, desc2)
Speed: Descriptor calculation is linear in atoms and comparison is fixed-length, without pairwise alignment. Benchmark end-to-end throughput on the prepared conformer library before choosing a scale cutoff.
Limit: USRCAT is a coarse approximation. Predictive for analog identification; less precise for scaffold hopping.
Open3DAlign (RDKit)
Open3DAlign uses MMFF atom types and partial charges to find an atom-based 3D alignment:
Goal: Align a target molecule onto a query in 3D and score volume overlap with Open3DAlign.
Approach: Build 3D structures for query and target, run GetO3A, and call Align() to transform the probe in place. Score() is the unnormalized O3A objective, not a shape Tanimoto or ROCS TanimotoCombo. If a normalized shape similarity is required, compute 1 - rdShapeHelpers.ShapeTanimotoDist(...) after alignment.
GetO3A finds an alignment between conformers; Align() applies it and returns RMSD. Keep o3a_score and normalized shape_tanimoto distinct in outputs.
Open3DAlign vs ROCS: Open3DAlign is open-source and competitive on small benchmarks; slower than ROCS at scale.
Conformer-Ensemble Shape Searching
For each library molecule, generate ensemble of conformers; pick best-shape conformer:
Goal: Run shape-similarity search over a conformer ensemble per library molecule so bound-conformer-like shapes are recovered.
Approach: For each library molecule, add hydrogens, embed n_conf conformers with ETKDGv3, MMFF-optimize, score each conformer against the query with Open3DAlign, and keep the best score per molecule.
Critical: Results depend on conformer coverage. Use an ensemble sized and validated for the library and query rather than assuming one conformer is representative.
ESP Similarity (Electrostatic)
ShaEP and ESPSim extend shape with electrostatic surface potential overlap. For ESP-relevant pharmacophores (binding pockets with strong electrostatics):
ESP scoring catches electrostatic-equivalent bioisosteres that pure shape misses (carboxylate vs tetrazole same charge).
Shape vs ECFP4 Complementarity
Shape result
ECFP4 result
Interpretation
High
High
Close analog candidate
High
Low
Scaffold-hop candidate
Low
High
Similar 2D chemotype in a different sampled shape
Low
Low
Unrelated by these representations
Calibrate “high” and “low” on a task-relevant reference set; do not treat the illustrative function defaults below as universal scientific cutoffs.
The shape >> ECFP4 quadrant is the scaffold-hopping gold:
Goal: Identify scaffold-hop candidates that are 3D-shape-similar but 2D-chemotype-dissimilar to the query.
Approach: Run the conformer-ensemble shape search, keep hits above a shape Tanimoto cutoff, then retain only those whose ECFP4 Tanimoto to the query is below an ECFP4 dissimilarity cutoff.
# These thresholds are repository starting defaults only; calibrate both on a# task-relevant active/decoy or retrieval benchmark before making decisions.defscaffold_hop_candidates(query_mol, library, shape_threshold=0.7,
ecfp_threshold=0.5):
shape_hits = shape_search_ensemble(query_mol, library)
candidates = []
for target, shape_score in shape_hits:
if shape_score >= shape_threshold:
ecfp_sim = ecfp_tanimoto(query_mol, target)
if ecfp_sim < ecfp_threshold:
candidates.append((target, shape_score, ecfp_sim))
return candidates
Per-Tool Failure Modes
USRCAT -- false positive on small molecules
Trigger: Library has many fragment-sized compounds.
Mechanism: USRCAT moments dominated by overall shape; small molecules look "similar" if shape resemble.
Symptom: Many fragment hits; not pharmacophore-relevant.
Fix: Calibrate size/property filters on the retrieval task and rescore selected hits with an alignment or feature-aware method.
Open3DAlign -- slow on large library
Trigger: Million-compound library, full alignment.
Mechanism: Open3DAlign is iterative; O(N) per molecule.
Symptom: Hours of compute.
Fix: Pre-filter with USRCAT and choose the rescore budget from measured throughput and retrieval saturation.
Shape only -- wrong stereochemistry match
Trigger: Mirror-image of correct binder.
Mechanism: Shape-only scoring may insufficiently penalize stereochemical alternatives even though a rigid rotational overlay is not generally invariant to mirror reflection.
Symptom: Enantiomer of inactive scores as hit.
Fix: Validate hits by 3D pose; check stereochemistry.
ROCS color -- bioisostere missed
Trigger: -COOH replaced by -SO3H or tetrazole.
Mechanism: Default color types may not equate these bioisosteres.
Symptom: Known bioisostere doesn't score high.
Fix: Validate the color-force-field treatment for the bioisostere and compare shape, color, and pharmacophore evidence separately.
Conformer not bioactive
Trigger: Library compound generated conformer is not the bound conformation.
Mechanism: ETKDGv3 generates plausible conformers; bound conformer may be higher energy.
Symptom: Known active doesn't shape-match query.
Fix: Use larger conformer ensemble; weight by Boltzmann; or use CREST + GFN2-xTB for high-quality sampling.
Field-based methods slower
Trigger: ShaEP or ESPSim on production library.
Mechanism: Field-based methods compute Gaussian fields per molecule.
Symptom: Field calculation or alignment dominates runtime on the prepared library.
Fix: Use as second-stage rescore; not primary screen.
Reconciliation: Shape vs Pharmacophore
Aspect
Shape
Pharmacophore
Representation
Volume distribution
Discrete features in space
Captures
Overall bulk
Interaction-relevant features
Speed
Fast (USRCAT) to medium (Open3DAlign)
Fast
Specificity
Task- and query-dependent
Task- and feature-definition-dependent
False positive rate
Measure on a matched benchmark
Measure on a matched benchmark
Best for
Scaffold hopping initial
Scaffold hopping refinement
Shape and pharmacophore searches make different approximations. Compare them alone and in sequence on a matched active/decoy or retrieval benchmark before assigning recall/precision roles.
Common Errors
Symptom
Cause
Fix
Open3DAlign RMSD is near 0
Near-exact O3A alignment
Treat as a successful alignment; evaluate the O3A and shape scores separately
USRCAT vector all zeros
Mol has no 3D coords
Generate conformer first
Shape Tanimoto > 1
Raw O3A or TanimotoCombo mislabeled as shape Tanimoto
Shape Tanimoto is 0-1; O3A is unnormalized and ROCS TanimotoCombo is 0-2
ROCS very slow
Sequential processing
Use parallel batching
Shape match but no docking pose
Wrong binding pose
Use docking on top shape hits, not shape alone
Missing co-crystal template
Apo or AlphaFold-only structure
Use ligand-based pharmacophore + shape
ShaEP returns no hits
Strict tolerance
Loosen overlap thresholds
References
Hawkins et al., J. Med. Chem. 50:74-82 (2007), DOI 10.1021/jm0603365 -- ROCS virtual-screening comparison.
Schreyer AM, Blundell T. J. Cheminformatics 4:27 (2012) -- USRCAT (DOI 10.1186/1758-2946-4-27).
Vainio, Puranen & Johnson, J. Chem. Inf. Model. 49:492-502 (2009), DOI 10.1021/ci800315d -- ShaEP.
Tosco, Balle & Shiri, J. Comput. Aided Mol. Des. 25:777-783 (2011), DOI 10.1007/s10822-011-9462-9 -- Open3DALIGN.
