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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Define a docking search box (center coordinates + box dimensions in Angstroms) from a co-crystal ligand, binding-site residues, or a saved JSON specification. | learningmatter-mit/ | 176 | — | ~2.9k | Automated safety check: Pass | MIT | today |
| 2 | Build a solvated, charge-neutralized protein-ligand complex for OpenMM molecular dynamics simulation. | learningmatter-mit/ | 176 | — | ~2k | Automated safety check: Pass | MIT | today |
| 3 | Identify and rank ligandable pockets on a protein structure or model using geometry (fpocket) or an ML predictor (P2Rank). | learningmatter-mit/ | 176 | — | ~4k | Automated safety check: Pass | MIT | today |
| 4 | Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation. | learningmatter-mit/ | 176 | — | ~2.5k | Automated safety check: Pass | MIT | today |
| 5 | Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting. | learningmatter-mit/ | 176 | — | ~1.3k | Automated safety check: Pass | MIT | today |
| 6 | Query multiple MOF databases (QMOF via MPContribs; ARC-MOF DB7/Majumdar et al. | learningmatter-mit/ | 176 | — | ~1.9k | Automated safety check: Pass | MIT | today |
| 7 | Query the Quantum MOF (QMOF) database via Materials Project's MPContribs platform for DFT-computed properties (bandgap) and optimized crystal structures of Metal-Organic Frameworks. | learningmatter-mit/ | 176 | — | ~670 | Automated safety check: Pass | MIT | today |
| 8 | Search and download experimental InfraRed (IR), Mass spectra, and UV-Vis spectra data (JCAMP-DX format) for molecules. | learningmatter-mit/ | 176 | — | ~529 | Automated safety check: Pass | MIT | today |
| 9 | Write and run custom ORCA input files for advanced electronic structure methods or settings not available through the SCINE wrapper, including multi-reference methods, excited states, relativistic… | learningmatter-mit/ | 176 | — | ~2.3k | Automated safety check: Pass | MIT | today |
| 10 | Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper. | learningmatter-mit/ | 176 | — | ~2k | Automated safety check: Pass | MIT | today |
| 11 | Run a DFT or Coupled Cluster single-point energy calculation (with optional gradients/Hessian) on a molecular structure with ORCA through SCINE wrapper. | learningmatter-mit/ | 176 | — | ~1.8k | Automated safety check: Pass | MIT | today |
| 12 | Dock small-molecule guests into a porous host material using the VOID library (Voronoi Clustering), generating multiple 3D conformers with RDKit and ranking generated complexes. | learningmatter-mit/ | 176 | — | ~918 | Automated safety check: Pass | MIT | today |
| 13 | Extract explicit safety warnings, GHS classifications, LD50 toxicity profiles, and acute oral toxicity triage from PubChem PUG VIEW. | learningmatter-mit/ | 176 | — | ~702 | Automated safety check: Pass | MIT | today |
| 14 | Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. | learningmatter-mit/ | 176 | — | ~972 | Automated safety check: Pass | MIT | today |
| 15 | Predict LC-MS/MS (MS2, tandem mass spectra) from SMILES via ICEBERG, a two-stage deep neural network. | learningmatter-mit/ | 176 | — | ~1.6k | Automated safety check: Pass | MIT | today |
| 16 | Calculate activation barrier using Nudged Elastic Band (NEB) method with MLIPs. | learningmatter-mit/ | 176 | — | ~1.3k | Automated safety check: Pass | MIT | today |
| 17 | Scripts for Wasserstein deconvolution of 1H NMR mixture spectra against reference spectra, reaction product prediction, time-series kinetics, and spectral plotting. | learningmatter-mit/ | 176 | — | ~2.3k | Automated safety check: Pass | MIT | today |
| 18 | Predict 1H NMR spectra from SMILES strings via NMRdb.org SPINUS neural network prediction and nmrsim quantum mechanical spin simulation. | learningmatter-mit/ | 176 | — | ~1.8k | Automated safety check: Pass | MIT | today |
| 19 | Generate transition state structures for chemical reactions using React-OT. | learningmatter-mit/ | 176 | — | ~914 | Automated safety check: Pass | MIT | today |
| 20 | Find structurally similar chemical compounds using PubChem's 2D fast similarity engine via the PUG-REST API. | learningmatter-mit/ | 176 | — | ~614 | Automated safety check: Pass | MIT | today |
| 21 | Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics. | learningmatter-mit/ | 176 | — | ~1.9k | Automated safety check: Pass | MIT | today |
| 22 | Calculates gas adsorption isotherms via BVT/GCMC Monte Carlo simulations in a porous framework using MLIP. | learningmatter-mit/ | 176 | — | ~926 | Automated safety check: Pass | MIT | today |
| 23 | Prepares supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard MLIP relaxation tools. | learningmatter-mit/ | 176 | — | ~1.2k | Automated safety check: Pass | MIT | today |
| 24 | Calculates Henry coefficient and heat of adsorption for a gas in a porous framework using Widom insertion with any supported MLIP. | learningmatter-mit/ | 176 | — | ~853 | Automated safety check: Pass | MIT | today |
| 25 | Match an experimental spectrum (1H NMR, 13C NMR, IR) against predicted or database reference spectra for candidate ranking and structure confirmation. | learningmatter-mit/ | 176 | — | ~2.5k | Automated safety check: Pass | MIT | today |
| 26 | Compute gas-phase thermodynamic quantities (H, S, G) and reaction thermochemistry (ΔH, ΔS, ΔG) using MLIPs with the ideal-gas/rigid-rotor/harmonic-oscillator approximation. | learningmatter-mit/ | 176 | — | ~1.4k | Automated safety check: Pass | MIT | today |
| 27 | Optimize non-periodic molecular TS guesses and verify first-order saddle point from vibrational modes. | learningmatter-mit/ | 176 | — | ~876 | Automated safety check: Pass | MIT | today |
| 28 | Calculate vibrational frequencies, normal modes, zero-point energy, and IR spectra of molecules and clusters using MLIPs. | learningmatter-mit/ | 176 | — | ~1.3k | Automated safety check: Pass | MIT | today |
| 29 | Fetch biological assays and target proteins a chemical has been tested against via PubChem. | learningmatter-mit/ | 176 | — | ~693 | Automated safety check: Pass | MIT | today |
| 30 | Query ChEMBL web services for targets, molecules, and curated bioactivity measurements (IC50, Ki, EC50, etc.). | learningmatter-mit/ | 176 | — | ~1.4k | Automated safety check: Pass | MIT | today |
| 31 | 31.Drug DB Pdb Search, filter, and retrieve macromolecular structures from the RCSB Protein Data Bank (PDB), including metadata, bound ligands, and optional coordinate/validation downloads. | learningmatter-mit/ | 176 | — | ~1.1k | Automated safety check: Pass | MIT | today |
| 32 | Query PubChem via PUG-REST to retrieve CIDs, computed properties, synonyms, and 2D/3D SDF structures. | learningmatter-mit/ | 176 | — | ~1.3k | Automated safety check: Pass | MIT | today |
| 33 | Post-docking analysis of virtual screening results including score distributions, enrichment metrics (ROC AUC, enrichment factors), and ligand efficiency calculations. | learningmatter-mit/ | 176 | — | ~2.2k | Automated safety check: Pass | MIT | today |
| 34 | Dock small-molecule ligands into a protein receptor using AutoDock Vina (Python API) and save ranked poses + docking metadata for reproducible virtual screening. | learningmatter-mit/ | 176 | — | ~2.3k | Automated safety check: Pass | MIT | today |
| 35 | Prepare small-molecule ligands for docking and analysis via optional state enumeration, 3D conformer generation, MMFF/UFF minimization, and export to SDF + AutoDock PDBQT. | learningmatter-mit/ | 176 | — | ~772 | Automated safety check: Pass | MIT | today |
| 36 | Compute single-trajectory MM-GBSA and / or MM-PBSA binding free energy estimates from a protein-ligand MD trajectory. | learningmatter-mit/ | 176 | — | ~4.7k | Automated safety check: Pass | MIT | today |
| 37 | Validate docked or generated ligand poses for physical plausibility using PoseBusters, filtering out chemically invalid or clashing poses before downstream refinement. | learningmatter-mit/ | 176 | — | ~1.3k | Automated safety check: Pass | MIT | today |
| 38 | Run a protein-ligand MD simulation in OpenMM with energy minimization, restrained equilibration, and production NPT, producing trajectory and checkpoint files for downstream analysis. | learningmatter-mit/ | 176 | — | ~1.4k | Automated safety check: Pass | MIT | today |
| 39 | Prepare macromolecular receptor structures (PDB/mmCIF or RCSB PDB ID) for docking or simulation by fixing common structure issues and adding hydrogens. | learningmatter-mit/ | 176 | — | ~1.1k | Automated safety check: Pass | MIT | today |
| 40 | Compute symmetry-corrected heavy-atom RMSD between docked poses and a reference crystal ligand to validate docking protocols. | learningmatter-mit/ | 176 | — | ~2.3k | Automated safety check: Pass | MIT | today |
| 41 | Predict synthetic accessibility and retrosynthetic pathways for novel molecules using the IBM RXN API. | learningmatter-mit/ | 176 | — | ~647 | Automated safety check: Pass | MIT | today |
| 42 | Analyze a protein-ligand MD trajectory to compute ligand RMSD, pocket RMSF, hydrogen bonds, contact occupancy, and protein-ligand interaction fingerprints over time. | learningmatter-mit/ | 176 | — | ~1.2k | Automated safety check: Pass | MIT | today |
| 43 | Search and retrieve research papers from ArXiv API for scientific research. | learningmatter-mit/ | 176 | — | ~634 | Automated safety check: Pass | MIT | today |
| 44 | Set up, check or troubleshoot how AtomisticSkills runs on this machine -- creating its Python environments, connecting its MCP servers, choosing uv or a container runtime, and configuring API keys. | learningmatter-mit/ | 176 | — | ~1.2k | Automated safety check: Notes | MIT | today |
| 45 | Search and retrieve preprint metadata from bioRxiv and medRxiv APIs for biological and medical research. | learningmatter-mit/ | 176 | — | ~854 | Automated safety check: Pass | MIT | today |
| 46 | Retrieve extensive literature (PubMed) and patent associated with a specific chemical compound via PubChem. | learningmatter-mit/ | 176 | — | ~515 | Automated safety check: Pass | MIT | today |
| 47 | Retrieves averaged elemental prices and provides direct vendor purchase links for elements and precursor compounds. | learningmatter-mit/ | 176 | — | ~566 | Automated safety check: Pass | MIT | today |
| 48 | Search for patents by keyword, material name, or assignee using free data sources (Google Patents). | learningmatter-mit/ | 176 | — | ~540 | Automated safety check: Pass | MIT | today |