Profacgen's Pharmacophore Modeling service helps drug discovery teams uncover the essential molecular features responsible for biological activity and translate them into predictive, reusable screening models. A pharmacophore is the ensemble of steric and electronic features that is responsible for a molecule's biological activity, and pharmacophore modeling has become a cornerstone of ligand-based virtual screening (LBVS). By capturing the abstract "active feature pattern" shared by diverse binders, we enable the identification of novel active compounds without requiring a 3D target structure.
Profacgen builds pharmacophore models by superimposing a set of structurally diverse compounds that bind the same target, then extracting the common chemical features essential for bioactivity. These models are subsequently used to virtually screen large compound libraries and to guide the rational design of new chemical entities. Our feature descriptions span hydrogen bond donors and acceptors, hydrophobic regions, positive and negative ionizable groups, aromatic rings, and excluded volumes, built with industry-standard platforms such as LigandScout, MOE Pharao, Phase, and Discovery Studio.
Pharmacophore modeling is one of the most efficient and robust ligand-based approaches for hit discovery and lead optimization. It is particularly valuable when structural biology has not yet delivered a usable receptor model, and it complements structure-based methods throughout the discovery pipeline.
| Service Component | Description |
| Training Set Compilation & Analysis | Collection of structurally diverse active compounds, curation of activity data, and assessment of structural diversity to ensure a representative and unbiased training set. |
| Molecular Alignment & Conformation Generation | Conformer ensemble generation, flexible alignment of training compounds, and selection of the bioactive conformation for feature mapping. |
| Pharmacophore Feature Extraction | Identification of common features—H-bond donor/acceptor, hydrophobic, ionizable, and aromatic features—together with excluded volume definition to represent steric constraints. |
| Model Validation & Refinement | Discrimination testing against a test set, decoy enrichment analysis, calculation of the Güner-Henry score, and selective refinement for optimal predictive power. |
| Virtual Screening & Hit List Delivery | Database screening, hit ranking, diversity analysis, and prioritization of candidate compounds for follow-up experimental validation. |
Background
A client sought novel leads for a Class A GPCR where only limited mutagenesis data existed and no usable receptor structure was available. Traditional structure-based screening was not feasible, and the available active compounds spanned several unrelated chemotypes.
Our Solution
We compiled 12 structurally diverse agonists with confirmed activity and generated conformer ensembles for each. After flexible alignment and bioactive conformation selection, we built a 4-feature pharmacophore consisting of 2 hydrogen bond acceptors, 1 hydrophobic region, and 1 positive ionizable group. The model was validated by decoy enrichment (Güner-Henry score > 0.85) and then used to screen a 2 million-compound library.
Final Results
The screen retrieved 800 candidates, of which 32 were selected for testing. Nine compounds were confirmed active, yielding a 28% hit rate. Importantly, three of the confirmed actives represented novel scaffolds absent from the training set, demonstrating successful scaffold hopping.
Background
A oncology program required discrimination between Type I and Type II kinase inhibitors to minimize off-target toxicity. Available co-crystal structures captured only the DFG-in (Type I) state, leaving the DFG-out (Type II) pocket poorly defined.
Our Solution
We developed a selectivity pharmacophore that incorporated excluded volumes around the DFG motif to encode the conformational difference between binding modes. By penalizing compounds intruding into the DFG-out space, the model enriched for Type II binders such as Abl and KIT while deprioritizing Type I targets.
Final Results
The selectivity model successfully enriched Type II binders and guided analog design toward compounds with reduced off-target risk, supporting progression of a more selective development candidate.
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