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SkillsMP ha recopilado 63 skills de InternScience/MolClaw. Abre una skill para revisar su origen y sus detalles.
Mostrando 40 de 63 skills recopiladas.
All tools utilized within MolClaw skills connect via the MCP protocol. This skill is the unified guide for connecting to the deployed MCP server before invoking tools.
Idioma del texto original: inglés
Formats extracted execution patterns into standard MolClaw skill documents. Accepts structured input from the Skill Crystallization Meta-Workflow (L2-12) and outputs a properly formatted L1 or L2 skill document conforming to MolClaw conventions. This skill…
Idioma del texto original: inglés
Predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the input molecules.
Idioma del texto original: inglés
Predict binding affinity between target protein sequence and small molecule SMILES using Boltz-2.
Idioma del texto original: inglés
Retrieve SMILES strings by compound name using PubChem with an NCI resolver fallback.
Idioma del texto original: inglés
Generate new molecules de novo.
Idioma del texto original: inglés
End-to-end docking-score ranking using EquiScore for candidate molecules against a target protein.
Idioma del texto original: inglés
Repair and clean PDB or mmCIF structures with PDBFixer, returning a repaired PDB path and topology counts.
Idioma del texto original: inglés
Run GoCa coarse-grained protein MD pipeline and collect key simulation artifacts from a unified run directory.
Idioma del texto original: inglés
Run HDOCKlite docking for protein complexes and return run directories with ranked models.
Idioma del texto original: inglés
**PRIMARY tool for all single-structure interaction analysis.** MCP-exposed protein–ligand / peptide / protein–protein interaction analysis and Schrödinger-style multi-dimensional visualization. Pure Python/NumPy engine covering 9 interaction types with 2D…
Idioma del texto original: inglés
Generate new molecules sampling from the input two warhead fragments.
Idioma del texto original: inglés
Calculate both Tanimoto similarities and the count of shared structural fragments between a target molecule and a list of candidate molecules via Morgan fingerprints.
Idioma del texto original: inglés
Generate new molecules sampling from the input molecule.
Idioma del texto original: inglés
Runs OpenAWSEM simulations and extracts representative trajectory frames for downstream ensemble analysis.
Idioma del texto original: inglés
Repair a protein PDB or mmCIF structure with PDBFixer and write a repaired PDB.
Idioma del texto original: inglés
Generate new peptide molecules sampling from the input peptide sequence.
Idioma del texto original: inglés
Execution-ready protein-ligand MM/GB(PB)SA workflow with explicit MCP handoffs and optional analysis.
Idioma del texto original: inglés
Run OpenMM protein MD and extract evenly spaced trajectory frames for downstream structural analysis.
Idioma del texto original: inglés
Execution-ready protein-protein MM/GB(PB)SA workflow with MCP-exposed tool names, strict file validation, and failure guards.
Idioma del texto original: inglés
Search the target protein sequence information from the input gene name or uniprot id.
Idioma del texto original: inglés
Retrieve and download a protein structure file (.pdb or .cif) using a gene name, UniProt ID, or PDB ID.
Idioma del texto original: inglés
Perform molecular docking using QuickVina2-GPU between target protein structure and small molecules.
Idioma del texto original: inglés
Generate new molecules sampling from the input scaffold.
Idioma del texto original: inglés
Run BioEmu sequence sampling and extract ensemble structures for downstream conformation analysis.
Idioma del texto original: inglés
Render a molecule from a SMILES string or a server-side molecular structure file with the MolClaw MCP tool `visualize_molecule`.
Idioma del texto original: inglés
Render a server-side PDB protein structure as a PNG with the MolClaw MCP tool `visualize_protein`.
Idioma del texto original: inglés
Predict protein structures with Chai-1 from sequence or FASTA input and return model scoring summaries.
Idioma del texto original: inglés
Chroma toolkit skill covering chroma_monomer for single-chain generation, chroma_complex for multi-chain assembly generation, and chroma_symmetry for symmetry-constrained protein design.
Idioma del texto original: inglés
[CURRENTLY UNAVAILABLE] DiffDock protein-ligand docking. This tool is not deployed on the current MCP server. Use molclaw-quickvina-docking or molclaw-karmadock-tool as alternatives.
Idioma del texto original: inglés
Calculate disease reversal scores for the provided molecules relative to a specific disease.
Idioma del texto original: inglés
High-level large-scale virtual screening workflow (10+ ligands) combining property filtering, QuickVina docking, EquiScore rescoring, and consensus ranking for target prioritization.
Idioma del texto original: inglés
Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format).
Idioma del texto original: inglés
Unified EquiScore skill for pocket extraction, pocket scoring, and end-to-end docking-to-score pipeline execution.
Idioma del texto original: inglés
Use ESMFold model to predict 3D structure of the input protein sequence.
Idioma del texto original: inglés
Design linear or cyclic peptide binders from receptor FASTA sequences using EvoBind2 with structured result outputs.
Idioma del texto original: inglés
Extract protein sequence of each chain from the protein structure file (pdb format).
Idioma del texto original: inglés
Implement data transmission between the local computer and the MCP Server using Base64 encoding
Idioma del texto original: inglés
FoldX protein stability and mutation analysis tool. Supports 8 modes: structure repair (RepairPDB), stability calculation (Stability), mutation ΔΔG (BuildModel), complex interface energy (AnalyseComplex), alanine scanning (AlaScan), position scanning…
Idioma del texto original: inglés
Use fpocket to detect binding pockets and output their detailed properties for the input protein. This offers a more concise approach to pocket identification.
Idioma del texto original: inglés