What makes Binding site prediction notable?

1.2K downloads. Trust evidence available

How should Binding site prediction be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Binding site prediction

Binding-site and pocket prediction workflows using P2Rank, AF2BIND, and fpocket through SciMiner.

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.2k

Updated

Oct 11, 2026

Version

1.0.4

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.2K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.4release · observed Jun 22, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170qp1q4twz35wa85ppa8894h83w461:binding-site-prediction

Install using `clawhub skill install s170qp1q4twz35wa85ppa8894h83w461:binding-site-prediction` in an isolated environment before connecting it to live workloads.
No published capability contract is available yet, so validate auth and request/response behavior manually.
Review the upstream CLAWHUB listing at https://clawhub.ai/sciminer/binding-site-prediction before using production credentials.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-sciminer-binding-site-prediction/snapshot"

Documentation

CLAWHUB

52,098 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: binding-site-prediction
description: Binding-site and pocket prediction workflows using P2Rank, AF2BIND, and fpocket through SciMiner.
credential_files:
- ~/.config/sciminer/credentials.json
---

# Binding-Site Prediction Skill

This skill supports protein ligand-binding site discovery workflows, including:

- machine-learning pocket prediction from uploaded protein structures
- geometry-based pocket detection and pocket descriptor mining
- per-residue ligand-binding probability scoring
- cross-validation of predicted pockets across complementary methods

## When to use this skill

- Predict likely ligand-binding pockets from a protein structure file
- Rank candidate pockets before docking, virtual screening, or structure-based design
- Compare geometry-based and ML-based pocket predictions on the same receptor
- Obtain residue-level ligand-binding confidence from a known structure or PDB identifier
- Prioritize consensus binding sites supported by multiple methods

## Method selection rule

- If the user provides a protein structure file and wants fast geometric pocket detection plus descriptors, use `fpocket Pocket Detection`.
- If the user provides a protein structure file and wants a machine-learning pocket ranking workflow, use `P2Rank Binding Site Prediction`.
- If the user wants residue-level binding probabilities, or only has a PDB code or UniProt-style structure identifier, use `AF2BIND Binding Probability`.
- When result confidence matters, run at least one pocket detector (`P2Rank` or `fpocket`) and then use `AF2BIND` to cross-check whether the highest-ranked pocket is supported by residue-level binding probabilities.

## Recommended workflow

### Fast pocket discovery

- Start with P2Rank when the goal is quick ML-based pocket ranking from an uploaded receptor structure.
- Start with fpocket when the goal is to enumerate pocket geometries and inspect pocket-size-sensitive candidates.

### Consensus refinement

- If both P2Rank and fpocket are available, compare the top-ranked pockets and prioritize overlapping sites.
- Use AF2BIND on the same structure to inspect whether high-probability binding residues cluster around the same region.

### Pre-docking handoff

- Use the consensus site from `P2Rank`, `fpocket`, and `AF2BIND` as the preferred handoff for docking box selection, virtual screening, or focused mutational analysis.
- If the three methods disagree, treat the site as uncertain and inspect multiple candidate pockets rather than overcommitting to a single location.

## Prerequisites

1. Obtain a free SciMiner API key from `https://sciminer.tech/utility`.
2. Store it outside this repository at `~/.config/sciminer/credentials.json` with JSON shaped as `{"api_key":"your_api_key_here"}`.
3. For SciMiner calls, read the API key from `~/.config/sciminer/credentials.json` and send it as the `X-Auth-Token` header.
4. Never print, persist, or store the API key in prompts, logs, or repository files. Agents should remember on

_meta.json

{
  "ownerId": "kn725br751g8y5tkj1h6d2krf58356et",
  "slug": "binding-site-prediction",
  "version": "1.0.4",
  "publishedAt": 1782141558586
}

skill-card.md

## Description:

Binding-site and pocket prediction workflows using P2Rank, AF2BIND, and fpocket through SciMiner.

This skill is ready for commercial/non-commercial use.

## Publisher:

[sciminer](https://clawhub.ai/user/sciminer)

### License/Terms of Use:

MIT-0

## Use Case:

Developers, computational biologists, and structure-based drug discovery teams use this skill to predict, rank, and cross-check candidate ligand-binding pockets from protein structures or supported identifiers before docking, virtual screening, or focused analysis.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Mutable remote SciMiner Markdown can affect credential-bearing API calls and protein file uploads.

Mitigation: Install only if SciMiner's hosted documentation and infrastructure are trusted; prefer reviewed, pinned API schemas and destination validation before attaching credentials.

Risk: Incorrect or divergent pocket predictions could lead users to overcommit to a single binding-site hypothesis.

Mitigation: Cross-check complementary methods and inspect uncertain or disagreeing candidate pockets before using results for docking, screening, or design decisions.

## Reference(s):

- [P2Rank SciMiner API documentation](https://sciminer.tech/tool_api_files/p2rank_api_doc.md)
- [AF2BIND SciMiner API documentation](https://sciminer.tech/tool_api_files/af2bind_api_doc.md)
- [fpocket SciMiner API documentation](https://sciminer.tech/tool_api_files/fpocket_api_doc.md)
- [Binding site prediction on ClawHub](https://clawhub.ai/sciminer/skills/binding-site-prediction)

## Skill Output:

**Output Type(s):** [guidance, code, shell commands, configuration, markdown]

**Output Format:** [Markdown with API invocation code, parameter guidance, status summaries, and SciMiner share URLs]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Uses SciMiner task results and share URLs; avoids printing or persisting the configured API key.]

## Skill Version(s):

1.0.4 (source: server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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