bio-pharmacophore-modeling
Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al 2022/2023 J Chem Inf Model 63:147-158), Pharmer / Pharmit (search), and PharmacoForge (diffusion-based generation, Flynn et al 2025 Front Bioinform), covering ligand-based pharmacophore (from active set alignment) and receptor-based pharmacophore (from binding pocket geometry). Explicit handling of feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search quer
npx skills add BioTender-max/awesome-bio-agent-skills --skill pharmacophore-modeling --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: RDKit 2024.09+, pharmer / pharmit (web service), PharmIT 1.1+, plip 2.4+ (interaction analysis). Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show rdkit` then `help(rdkit.Chem.Pharm3D)` 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. # Pharmacophore Modeling Build 3D pharmacophore queries that capture the essential interaction features of a ligand-target binding event. A pharmacophore is the *spatial arrangement of pharmacophore features* (donor, acceptor, hydrophobe, aromatic, charged) sufficient for activity, abstracted from any specific chemotype. Used for scaffold-hopping (find compounds with different scaffold but matching pharmacophore), virtual screening (faster than docking), and cross-target SAR transfer. Modern best practice: derive pharmacophore from co-crystal structure if available (receptor-based; apo2ph4 workflow of Heider et al 2022/2023 *J Chem Inf Model* 63:147-158) or align actives if no crystal (ligand-based). Diffusion-based g
- Version Compatibility
- Pharmacophore Feature Types
- Method Taxonomy
- Decision Tree by Scenario
- Ligand-Based Pharmacophore (RDKit Pharm3D)
- Receptor-Based Pharmacophore (apo2ph4 workflow)
- Pharmacophore Search (Pharmit / Pharmer)
- Pharmacophore Quality Validation
- Diffusion-Based Pharmacophore Design (PharmacoForge)
- Pharmacophore vs Shape vs 2D Fingerprint
- Per-Tool Failure Modes
- Ligand-based -- diverse actives confound
- Receptor-based -- apo structure
- Pharmacophore -- single conformer bias
Conceptual apo2ph4-style workflow; flags shown are illustrative. apo2ph4 -pdb receptor.pdb -site_residues 'A:100,A:101,A:104,A:108' \ Pharmer command line (offline alternative) pharmer search -q pharmacophore.ph4 -dbdir zinc_db -out hits.sdf
What does the bio-pharmacophore-modeling skill do?
Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al 2022/2023 J Chem Inf Model 63:147-158), Pharmer / Pharmit (search), and PharmacoForge (diffusion-based generation, Flynn et al 2025 Front Bioinform), covering ligand-based pharmacophore (from active set alignment) and receptor-based pharmacophore (from binding pocket geometry). Explicit handling of feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search quer
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill pharmacophore-modeling --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.