Research & Science · By learningmatter-mit

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/AtomisticSkills

Build a solvated, charge-neutralized protein-ligand complex for OpenMM molecular dynamics simulation.

learningmatter-mit/AtomisticSkills

Identify and rank ligandable pockets on a protein structure or model using geometry (fpocket) or an ML predictor (P2Rank).

learningmatter-mit/AtomisticSkills

Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.

learningmatter-mit/AtomisticSkills

Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.

learningmatter-mit/AtomisticSkills

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/AtomisticSkills

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/AtomisticSkills

Predict LC-MS/MS (MS2, tandem mass spectra) from SMILES via ICEBERG, a two-stage deep neural network.

learningmatter-mit/AtomisticSkills

Predict 1H NMR spectra from SMILES strings via NMRdb.org SPINUS neural network prediction and nmrsim quantum mechanical spin simulation.

learningmatter-mit/AtomisticSkills

Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.

learningmatter-mit/AtomisticSkills

Query ChEMBL web services for targets, molecules, and curated bioactivity measurements (IC50, Ki, EC50, etc.).

learningmatter-mit/AtomisticSkills

Search, filter, and retrieve macromolecular structures from the RCSB Protein Data Bank (PDB), including metadata, bound ligands, and optional coordinate/validation downloads.

learningmatter-mit/AtomisticSkills

Post-docking analysis of virtual screening results including score distributions, enrichment metrics (ROC AUC, enrichment factors), and ligand efficiency calculations.

learningmatter-mit/AtomisticSkills

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/AtomisticSkills

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/AtomisticSkills

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/AtomisticSkills

Prepare macromolecular receptor structures (PDB/mmCIF or RCSB PDB ID) for docking or simulation by fixing common structure issues and adding hydrogens.

learningmatter-mit/AtomisticSkills

Predict synthetic accessibility and retrosynthetic pathways for novel molecules using the IBM RXN API.

learningmatter-mit/AtomisticSkills

Search and retrieve research papers from ArXiv API for scientific research.

learningmatter-mit/AtomisticSkills

Search and retrieve preprint metadata from bioRxiv and medRxiv APIs for biological and medical research.

learningmatter-mit/AtomisticSkills

Retrieve extensive literature (PubMed) and patent associated with a specific chemical compound via PubChem.

learningmatter-mit/AtomisticSkills

Query Materials Project database for crystal structures, computed properties, elastic/magnetic data, and structurally similar materials using the MP API.

learningmatter-mit/AtomisticSkills

Query the Crystallography Open Database (COD) and other OPTIMADE-compliant databases for experimental crystal structures.

learningmatter-mit/AtomisticSkills

Calculate charged defect formation energies and transition level diagrams using pymatgen-analysis-defects and atomate2 VASP workflows.

learningmatter-mit/AtomisticSkills

Calculate ionic diffusion coefficients and activation energy from MD trajectories using pymatgen.

learningmatter-mit/AtomisticSkills

Discover new crystal structures by data-mined ionic substitution — propose candidates from existing structures (forward) or find potential structures for a target composition (reverse).

learningmatter-mit/AtomisticSkills

Build and run LAMMPS molecular dynamics with isolated MLIP-specific binaries (MACE, MatGL/CHGNet, FairChem) to avoid Python and Torch stack conflicts.

learningmatter-mit/AtomisticSkills

Calculate and visualize the probability density of diffusing ions from a Molecular Dynamics (MD) trajectory.

learningmatter-mit/AtomisticSkills

Generate random crystal structures for a given composition (AIRSS-style) and relax with MLIPs to find low-energy candidates.

learningmatter-mit/AtomisticSkills

Calculate the X-ray Diffraction (XRD) spectrum of a material using pymatgen.

learningmatter-mit/AtomisticSkills

Generate crystal structures with exact composition control using DiffCSP++ (space group + Wyckoff positions), or unconditionally from trained distributions.

learningmatter-mit/AtomisticSkills

Compute RDKit physicochemical descriptors and rule-based drug-likeness heuristics (Ro5, Veber, QED) from SMILES.

learningmatter-mit/AtomisticSkills

Compute Morgan/ECFP fingerprints, Tanimoto similarity, and optional Butina clusters/heatmaps for small-molecule comparison.

learningmatter-mit/AtomisticSkills

Perform iterative, deep, and comprehensive literature research on a specific materials/chemistry topic.

learningmatter-mit/AtomisticSkills

Review a manuscript or code repository for FAIR data compliance (Findable, Accessible, Interoperable, Reusable), producing a structured report with pass/fail per principle and actionable remediation…

learningmatter-mit/AtomisticSkills

Act as a reviewer to critically review research plans, manuscripts, or task summaries, pointing out missing baselines, statistical flaws, and weak assumptions.

learningmatter-mit/AtomisticSkills

Real-time monitoring tools for stability, equilibration, and diffusion during ASE molecular dynamics simulations.

learningmatter-mit/AtomisticSkills

Generate novel crystal structures and molecules using ADiT (All-atom Diffusion Transformer), a unified latent diffusion model.

learningmatter-mit/AtomisticSkills