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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

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

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

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

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

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

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

cellpose-cell-segmentation

Deep learning cell and nucleus segmentation from fluorescence and brightfield microscopy images. Uses pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm that segments cells without requiring retraining on new image types. Outputs label masks for downstream morphology measurement and tracking. Use scikit-image watershed for rule-based segmentation; use Cellpose when deep learning generalization across staining conditions is needed.

jaechang-hits

research

scientific-computing

93

biopython-molecular-biology

Computational molecular biology toolkit for sequence manipulation, file I/O (FASTA/GenBank/PDB), NCBI database access (Entrez), BLAST automation, pairwise/multiple sequence alignment, protein structure analysis (Bio.PDB), and phylogenetic tree construction. Use for batch sequence processing, custom bioinformatics pipelines, format conversion, and programmatic PubMed/GenBank queries. For quick gene lookups use gget; for multi-service REST APIs use bioservices.

jaechang-hits

research

scientific-computing

93

attend

Route upstream epistemic deficits and evaluate execution-time risks during AI operations. Scans for unresolved upstream protocol needs, materializes intent into tasks, classifies each for risk signals, delegates low-risk tasks to executor, and surfaces elevated-risk findings for user judgment. Type: (ExecutionBlind, User, EVALUATE, ExecutionContext) → SituatedExecution. Alias: Prosoche(προσοχή).

jongwony

research

scientific-computing

93

pathml

PathML is an open-source toolkit for computational pathology. Use it to process whole-slide images (WSIs): load slides, extract tiles, apply stain normalization and nuclear segmentation preprocessing, extract features, and train machine learning models. Supports H&E and multiplex imaging. Ideal for building end-to-end digital pathology pipelines from raw WSI files to quantitative outputs.

jaechang-hits

research

scientific-computing

93

neurokit2

NeuroKit2 is a Python toolkit for neurophysiological signal processing. Process ECG (heart rate, HRV, R-peak detection), EEG (complexity, power spectral density), EMG (muscle activation onset), EDA/GSR (skin conductance, SCR decomposition), PPG (photoplethysmography), and RSP (respiration) signals. Simulate synthetic signals for testing. Alternatives: BioSPPy (older, less maintained), MNE (EEG/MEG specialist), heartpy (ECG only), scipy.signal (raw DSP without biosignal abstraction).

jaechang-hits

research

scientific-computing

93

mouse-phenome-database

Retrieve quantitative phenotype measurements across inbred mouse strains from the Mouse Phenome Database (MPD) for metabolic, behavioral, and physiological traits. Query strain means and raw individual measurements for body weight, glucose, blood pressure, behavioral assays, and 40+ additional procedures. Use for QTL analysis support, cross-strain phenotype comparison, and identifying mouse models for metabolic or behavioral traits. For mouse gene-disease-phenotype associations use monarch-database; for mouse genome annotations use ensembl-database.

jaechang-hits

research

domain-utilities

93

interpro-database

Query the InterPro REST API for protein domain architecture, family classification, and member database integration. Search InterPro entries by name or accession, retrieve all domains in a protein (domain architecture), list proteins in a family, get taxonomic distribution, and link to PDB structures. Integrates Pfam, PANTHER, PIRSF, PRINTS, PROSITE, SMART, CDD, and NCBIfam into a unified hierarchy. For protein sequence and Swiss-Prot annotations use uniprot-protein-database; for experimental 3D structures use pdb-database.

jaechang-hits

tools

domain-utilities

93

amazon-international-listings

Multi-marketplace listing management — translation, pricing localization, BIL (Build International Listings)

nexscope-ai

tools

documents

92

chinese-style-guide

This skill should be used when the user asks to "写文档", "撰写文章", "写邮件", "编辑中文", "检查格式", "修正排版", "中英文混排", "格式化文档", "review 文档格式", "proofread", or when the user is preparing to write or edit Chinese documents, emails, messages, or any formal Chinese text content. This skill provides formatting and typographical corrections based on the Chinese Style Guide, focusing on layout and formatting without altering the author's intended expression.

RightCapitalHQ

content-media

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