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Scientific computing and academic tools.

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astronomy-physics

93

astropy-astronomy

Core Python library for astronomy and astrophysics. Units & quantities with dimensional analysis, celestial coordinate transformations (ICRS/Galactic/AltAz/FK5), FITS file handling, table operations (FITS/HDF5/VOTable/CSV), cosmological calculations (Planck18, distance/age/volume), precise time handling (UTC/TAI/TT/TDB, Julian dates, barycentric corrections), WCS pixel-world mapping, model fitting, image visualization. For general data tables use pandas/polars; for radio astronomy interferometry use CASA.

jaechang-hits

research

computational-chemistry

93

generate

Run code generation for Isar models, JSON serialization, and i18n

keychat-io

research

computational-chemistry

93

rdkit-cheminformatics

Cheminformatics toolkit for molecular analysis and virtual screening. Use for SMILES/SDF parsing, molecular descriptor calculation (MW, LogP, TPSA), fingerprint generation (Morgan/ECFP, MACCS, RDKit), Tanimoto similarity search, substructure filtering with SMARTS, drug-likeness assessment (Lipinski Ro5), chemical reaction enumeration, 2D/3D coordinate generation, and compound library profiling. For simpler high-level API, use datamol. Use RDKit when you need fine-grained control over sanitization, custom fingerprints, SMARTS queries, or reaction SMARTS.

jaechang-hits

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computational-chemistry

93

sar-analysis

Guide for performing Structure-Activity Relationship (SAR) analysis using RDKit. Covers core scaffold identification via Maximum Common Substructure (MCS), R-group decomposition, molecular alignment for visualization, activity heatmap generation, and interpretive SAR text output. For general cheminformatics operations, see rdkit-cheminformatics. For bioactivity data retrieval, see chembl-database-bioactivity.

jaechang-hits

research

computational-chemistry

93

rowan

Rowan is a cloud-based computational chemistry platform providing quantum chemistry calculations via a Python SDK. Use it to run geometry optimization, conformer generation, torsional scans, and energy minimization with DFT or semiempirical methods, and retrieve molecular properties (dipole moment, partial charges, frontier orbital energies) — without managing local quantum chemistry software or HPC clusters.

jaechang-hits

research

computational-chemistry

93

molfeat-molecular-featurization

Molecular featurization hub (100+ featurizers) for ML. Convert SMILES to numerical representations via fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained models (ChemBERTa, GIN, Graphormer), and pharmacophore features. Scikit-learn compatible transformers with parallelization, caching, and state persistence. For QSAR, virtual screening, similarity search, and deep learning on molecules.

jaechang-hits

research

computational-chemistry

93

medchem

Medicinal chemistry filters for drug discovery compound triage. Drug-likeness rules (Lipinski Ro5, Veber, Oprea, CNS, leadlike, REOS, Golden Triangle, Rule of Three), structural alerts (PAINS, NIBR, Lilly Demerits, Common Alerts), chemical group detection (hinge binders, Michael acceptors), molecular complexity metrics, property constraints, and a query language for composing filter logic. Built on RDKit and datamol. Use for hit-to-lead filtering, library design, and ADMET pre-screening. For molecular I/O and descriptors use rdkit-cheminformatics or datamol-cheminformatics instead.

jaechang-hits

research

computational-chemistry

93

pubchem-compound-search

Query PubChem database (110M+ compounds) via PubChemPy and PUG-REST API. Search compounds by name/CID/SMILES, retrieve molecular properties (MW, LogP, TPSA), perform similarity and substructure searches, access bioactivity data. For local cheminformatics computation use rdkit; for multi-database queries use bioservices.

jaechang-hits

research

computational-chemistry

93

drugbank-database-access

Parse and query DrugBank local XML database for drug information, interactions, targets, and chemical properties. Search drugs by ID/name/CAS, extract drug-drug interactions with severity, map targets/enzymes/transporters, compute molecular similarity from SMILES. Primary access via local XML (downloaded); REST API available but rate-limited (3,000/month dev tier). For live bioactivity queries use chembl-database-bioactivity; for compound property lookups use pubchem-compound-search.

jaechang-hits

research

computational-chemistry

93

pytdc-therapeutics-data-commons

Therapeutics Data Commons (TDC) — AI-ready drug discovery dataset platform. Access curated ADME, toxicity, DTI, DDI datasets with scaffold/cold splits, standardized evaluation metrics, molecular oracles for optimization, and ADMET benchmark groups. Use for therapeutic ML model training, benchmarking, and molecular property prediction. For chemical database queries use chembl-database-bioactivity; for molecular featurization use molfeat.

jaechang-hits

research

computational-chemistry

93

gtopdb-database

Query the IUPHAR/BPS Guide to Pharmacology (GtoPdb) REST API for receptor-ligand interaction data, target pharmacology, and quantitative affinity metrics. Retrieve pKi/pIC50/pEC50 values, ligand classifications (approved drugs, biologics, natural products), target families (GPCRs, ion channels, nuclear receptors, kinases), and selectivity profiles across the pharmacological target space.

jaechang-hits

research

computational-chemistry

93

torchdrug

TorchDrug is a PyTorch-based machine learning platform for drug discovery. Use it for graph-based molecular representation learning, molecular property prediction (ADMET, activity), retrosynthesis prediction, drug-target interaction (DTI) modeling, and pretraining on large molecular datasets. Provides GNN layers (GraphConv, GAT, MPNN), pretrained models, and benchmark datasets in a unified PyTorch-compatible API.

jaechang-hits

research

computational-chemistry

93

unichem-database

Cross-reference chemical compound identifiers across 50+ databases (ChEMBL, DrugBank, PubChem, ChEBI, PDB, KEGG) using the UniChem REST API. Resolve InChIKeys to database-specific IDs, find all sources for a compound, discover related compounds by structural connectivity, and batch-translate compound lists between naming systems. No authentication required.

jaechang-hits

research

computational-chemistry

93

deepchem

Deep learning framework for drug discovery and materials science. 60+ models (GCN, GAT, AttentiveFP, MPNN, DMPNN, ChemBERTa, GROVER), 50+ molecular featurizers, MoleculeNet benchmarks, hyperparameter optimization, transfer learning. Unified load-featurize-split-train-evaluate API. For fingerprint-only cheminformatics use rdkit-cheminformatics; for featurization hub without training use molfeat-molecular-featurization.

jaechang-hits

research

computational-chemistry

93

diffdock

DiffDock is a diffusion-based molecular docking tool that predicts protein-ligand binding poses without requiring a predefined binding site. Use it when the binding site is unknown, when traditional docking fails, or when exploring multiple binding modes. Pipeline: prepare protein (PDB) and ligand (SMILES/SDF) inputs → run DiffDock inference → analyze confidence-ranked poses → visualize in PyMOL or NGLview.

jaechang-hits

research

computational-chemistry

93

histolab-wsi-processing

Whole slide image processing for digital pathology. Tissue detection, tile extraction (random, grid, score-based), filter pipelines for H&E/IHC preprocessing. Use for dataset preparation, tile-based deep learning, and slide quality assessment. For advanced spatial proteomics or multiplexed imaging use pathml.

jaechang-hits

research

computational-chemistry

93

matchms-spectral-matching

Mass spectrometry spectral matching and metabolite identification with matchms. Use for importing spectra (mzML, MGF, MSP, JSON), filtering/normalizing peaks, computing spectral similarity (cosine, modified cosine, fingerprint), building reproducible processing pipelines, and identifying unknown metabolites from spectral libraries. For full LC-MS/MS proteomics pipelines, use pyopenms instead.

jaechang-hits

research

computational-chemistry

93

autodock-vina-docking

Molecular docking with AutoDock Vina via Python API. Receptor/ligand preparation (Meeko + RDKit), grid box setup, docking execution, pose extraction, binding energy analysis, and batch virtual screening. Use for protein-ligand docking and hit identification.

jaechang-hits

research

computational-chemistry

93

chembl-database-bioactivity

Query ChEMBL bioactive molecules and drug discovery data using the Python SDK. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki, EC50), find inhibitors for targets, perform SAR studies, and access drug mechanism/indication data for medicinal chemistry research.

jaechang-hits

research

computational-chemistry

93

zinc-database

Query ZINC15/ZINC22 virtual compound libraries for drug discovery. Search purchasable lead-like, fragment-like, and drug-like compounds by molecular weight, logP, reactivity class, or SMILES similarity. Download 3D compound sets for docking, retrieve SMILES for in-silico screening. ZINC20 contains 1.4B compounds; purchasable subset is 750M. For bioactivity data use chembl-database-bioactivity; for approved drugs use drugbank-database-access.

jaechang-hits

research

computational-chemistry

93

ddinter-database

Query drug-drug interaction (DDI) data from DDInter via REST API. Search interactions by drug name or ID, retrieve severity levels (major/moderate/minor), interaction mechanisms, and clinical recommendations for drug pairs. Covers 1.7M+ interactions across 2,400+ drugs. No authentication required. For FDA drug labeling use dailymed-database; for pharmacogenomics use clinpgx-database.

jaechang-hits

research

lab-tools

93

western-blot-quantification

Guide to quantitative Western blot analysis covering band detection, two-step normalization, fold change calculation, statistical aggregation across biological replicates, and publication-ready visualization. Consult when analyzing blot images with multiple conditions and repetitions, choosing normalization strategies, or preparing densitometry figures for publication.

jaechang-hits

research

scientific-computing

93

pymatgen

pymatgen (Python Materials Genomics) is a materials science Python library for structure analysis, thermodynamics, and electronic property calculation. Parse and create crystal structures (CIF, POSCAR, CIF), query the Materials Project database for DFT-computed properties, analyze phase diagrams and pourbaix diagrams, compute X-ray diffraction patterns, and generate DFT input files for VASP, Quantum ESPRESSO, and CP2K. Alternatives: ASE (Atomic Simulation Environment) for MD/geometry; AFLOW for high-throughput; OVITO for visualization.

jaechang-hits

research

scientific-computing

93

maxquant-proteomics

MaxQuant + Perseus proteomics pipeline: configure and run MaxQuant for label-free quantification (LFQ) and SILAC; parse proteinGroups.txt in Python; filter contaminants/reverse decoys; log2-transform and median-normalize LFQ intensities; impute MNAR missing values; t-test with FDR correction; volcano plot; GO/pathway enrichment. Use Proteome Discoverer for Thermo instrument-native processing; FragPipe/MSFragger for GPU-accelerated database search.

jaechang-hits

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