Use when you have annotated metabolite structures (with SMILES strings) from a reference library (e.
Use when you have received a JSON response from the CSI:FingerID web service endpoint after submitting a fragmentation tree or tandem mass spectrum query, and you need to extract…
Use when when you have labeled mass-spectrometry spectral data (precursor m/z and fragment m/z–intensity pairs) paired with known molecular structures (as InChIKeys or SMILES),…
Use when when evaluating how well mass spectral similarity scores correlate with actual chemical structure for annotated spectral pairs (e.g., spectra with InChIKey metadata).
Use when you have a calibrated FT-ICR transient (ESI_NEG or similar ionization mode) and need to annotate each detected m/z peak with its most likely elemental composition.
Use when processing tandem MS/MS libraries in mgf format (such as GNPS) that lack a Molecular Formula (MF) field but contain valid SMILES strings.
Use when when you need to constrain a large metabolite database to a specific instrumental range (e.g., m/z 100–1000) before generating virtual chemical mixtures for LC-MS/MS…
Use when you have MS/MS fragmentation spectra (from Orbitrap or Q-TOF instruments) in MGF format with known precursor m/z, adduct type, and collision energy, and you need to…
Use when you need to represent, validate, and manipulate molecular compositions in MS analysis—specifically when annotating precursor or product ions with elemental formulas,…
Use when you have a recalibrated FT-ICR mass spectrum (Bruker .d format or equivalent) with detected, noise-thresholded peaks and need to assign chemical formulas to each peak.
Use when when you have a feature list from HRMS with tentatively assigned molecular formulas (from in silico tools or databases) and need to assess formula plausibility before…
Use when when you have encoded spectral features (from a CNN featurizer applied to 1D 1H and/or 13C NMR spectra) and a set of candidate molecular fragments predicted for a…
Use when after RDKit has generated multiple conformations for a molecule in an SDF or XYZ format, and you need to reduce the conformational ensemble to a tractable size (by…
Use when you have raw SMILES strings from a chemical database (e.g., CCSBase, METLIN, or custom sources) and need to feed them into a graph neural network model.
Use when when you have a collection of molecular structures (as InChI strings, SMILES, or RDKit Mol objects) and need to feed them into a pretrained or transfer-learning neural…
Use when you have molecular identifiers (SMILES strings or molecular structure files) that need to be converted into node-edge graph tensors for input to message passing neural…
Use when during MSP, MGF, JSON, or CSV file parsing when standardizing mass spectra from heterogeneous open mass spectral libraries (OMSLs).
Use when you have a GNPS mass spectral molecular network (classical or feature-based) and MS2LDA-derived Mass2Motif data, and you need to annotate network nodes with both chemical…
Use when you have a GNPS mass spectral molecular network (in .graphml or Cytoscape format) and wish to annotate its nodes and edges with chemical class assignments from the GNPS…
Use when after generating candidate transformed structures from biotransformation rules and when you have MS/MS spectral feature data that you wish to organize into putative…
Use when you have untargeted metabolomics data (e.g., LC-MS/MS spectra) and need to organize compounds by structural relatedness to enable structure discovery for unknown…
Use when you have a GNPS-generated classical or feature-based molecular network (in graphml or JSON format) and corresponding MS2LDA or chemical class assignment data, and you…
Use when after GNPS_GC molecular networking job completion, when you have retrieved raw network output files and need to extract, validate, and structure the network topology for…
Use when you have a GNPS-generated molecular network (classical or feature-based
Use when you have LC-MS/MS DDA data from one or more samples and need to organize fragmentation spectra by similarity relationships to support compound annotation, enable…
Explore wine and food pairing through shared volatile aroma compounds — the Ahn et al. flavor network research, Foodpairing methodology, 14-compound reference table, and five…
Use when targeting Molecular Psychiatry (Mol Psychiatry) or deciding whether a biological psychiatry or translational neuroscience manuscript fits this venue.
Generates academic reviews for molecules in diseases using PubMed research. Invoke when user needs biomedical literature review with Vancouver citation format.
Standardizes molecular structures using ChEMBL chembl_structure_pipeline and RDKit rdMolStandardize covering sanitization, salt/solvent stripping, neutralization, tautome — from…
Use when you have a trained GNN model predicting CCS values from molecular graphs and need to understand which structural features (node and edge attributes) are most influential…
Use when you have a collection of molecular structures (with SMILES strings, InChI, or similar identifiers) and corresponding experimentally determined or reference CCS values,…
Use when when you have a set of chemical structures (SMILES strings or SDF files) that need to be processed for training a graph neural network model on molecular property…
Use when after an end-to-end neural model (CNN + transformer) has generated predicted molecular structures (formula and connectivity) from 1D NMR spectra.
Use when you are receiving molecular structures from external sources (COCONUT database, ZINC database, user-provided chemical data) in varying formats (SMILES strings, InChI…
Use when you have trained a multitask NMR-to-structure model and need to quantify its predictive accuracy on held-out test molecules.
Use when you have preprocessed 1D ¹H and/or ¹³C NMR spectra (as numerical arrays or feature tensors) from an unknown organic compound with ≤19 heavy atoms, and you need to predict…
Use when your goal is to assess whether a pretrained NMR2Struct model trained on molecules ≤19 heavy atoms can generalize to larger, more complex molecules, or whether accuracy…
Use when when you have executed the MultiModalSpectralTransformer architecture on a set of multi-modal spectroscopic inputs (NMR, HSQC, COSY, IR) and obtained predicted molecular…
Use when when you have paired mass spectra and molecular structure data and need to train a model that can bidirectionally map between experimental spectra and chemical s — from…
Use when when you have molecular structures encoded as SMILES strings and need to incorporate them into a multi-modal language model (such as BART) that also processes mass…
3Dmol.js WebGL molecular visualization emitted as self-contained HTML. Render structures (PDB/SDF/XYZ/MOL2/cube) with stick, sphere, cartoon, line, and surface styles; animate…
Use when after RAMClustR clustering of XCMS-detected features and prior to final compound annotation, when you need to verify the robustness of molecular weight inference or when…
Use when when you have metabolite structures (as SMILES strings) and need to predict their observable m/z ions under non-standard ionization conditions imposed by a derivatizing…
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Molecular featurization hub (100+ featurizers) for ML. SMILES to fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained embeddings (ChemBERTa, GIN,…
Outcome-driven scientific publishing for AI agents. Publish research papers, hypotheses, and experiments with validated artifacts, structured claims, milestone tracking — from…
Join the MoltLab research community — propose claims, run computations, vote on ideas, debate research, write papers, and review your colleagues' work.
Publish and discover AI-native scientific papers. Register agents, submit research for peer review, and search the repository.
Join the MoltUniversity research community — propose claims, run computations, vote on ideas, debate research, write papers, and review your colleagues' work.
Estrategia e implementacao de monetizacao para produtos digitais - Stripe, subscriptions, pricing experiments, freemium, upgrade flows, churn prevention, revenue optimization e…
Brainstorm 3-5 monetization strategies with audience fit, risks, and validation experiments. Use when exploring revenue models, evaluating pricing strategies, or deciding — from…
Brainstorm 3-5 monetization strategies with audience fit, risks, and validation experiments. Use when exploring revenue models, evaluating pricing strategies, or deciding — from…
Monitor running experiments, check progress, collect results. Use when user says \"check results\", \"is it done\", \"monitor\", or wants experiment output.
Monitor running experiments, check progress, collect results. Use when user says \"check results\", \"is it done\", \"monitor\", or wants experiment output.
Implement model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems.
Monitor Proxmox infrastructure health and performance. Track node statistics, analyze resource utilization, and identify optimization opportunities across your cluster.