bioinformatics
156
clinvar-database
Query NCBI ClinVar for variant clinical significance. Search by gene/position, interpret pathogenicity classifications, access via E-utilities API or FTP, annotate VCFs, for genomic medicine.
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bioinformatics
156
pdb-database
Python API for RCSB PDB 3D structures (search, fetch coordinates, metadata). Input MUST be a protein/gene name (e.g. 'KRAS', 'EGFR', 'BTK') or a 4-character PDB ID (e.g. '6OIM'). Returns zero results for drug/chemistry phrases such as 'covalent inhibitors' or 'warhead selectivity'. Strip all drug qualifiers — pass only the target protein name or PDB accession.
lamm-mit
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bioinformatics
156
bioservices
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
lamm-mit
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bioinformatics
156
arboreto
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
lamm-mit
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bioinformatics
156
alphafold-database
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
lamm-mit
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bioinformatics
156
peptide-msa
Perform a simple multiple-sequence alignment (MSA) for short peptides and return aligned sequences + consensus.
lamm-mit
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bioinformatics
156
monarch-database
Query the Monarch Initiative knowledge graph for disease-gene-phenotype associations across species. Integrates OMIM, ORPHANET, HPO, ClinVar, and model organism databases. Use for rare disease gene discovery, phenotype-to-gene mapping, cross-species disease modeling, and HPO term lookup.
lamm-mit
research
computational-chemistry
156
candidate-ranking
Rank peptide variants using stability heuristics and hotspot protection; emit top candidates.
lamm-mit
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computational-chemistry
156
adaptyv
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
lamm-mit
research
computational-chemistry
156
pyopenms
Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms.
lamm-mit
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computational-chemistry
156
structure-contact-analysis
Identify peptide–protein contact hotspots from a PDB structure (local file or fetched from RCSB) and emit binding hotspot positions.
lamm-mit
research
computational-chemistry
156
rowan
Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated computational chemistry pipelines. Provides cloud compute resources with no local setup required.
lamm-mit
research
computational-chemistry
156
qutip
Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.
lamm-mit
research
computational-chemistry
156
qiskit
IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.
lamm-mit
research
computational-chemistry
156
rdkit
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
lamm-mit
research
computational-chemistry
156
chembl
Small-molecule drug lookup by exact drug name or ChEMBL ID. Query MUST be a single drug name or ID — 1 to 3 words maximum. Valid examples: 'sotorasib', 'imatinib', 'ibrutinib', 'CHEMBL25', 'AMG 510'. If the topic is 'sotorasib KRAS G12C', the correct query is 'sotorasib'. If the topic is 'BTK inhibitors in CLL', search PubMed first to get a specific drug name, then query ChEMBL with that name. Strip protein names, mutation labels, and mechanism words — pass only the compound name.
lamm-mit
research