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Build and operate reproducible genomics workloads on DNAnexus with the dx CLI, dxpy, apps/applets, native workflows, dxCompiler, and Nextflow. Use for DNAnexus data transfers, dxapp.json development, …
Fetch biological assays and target proteins a chemical has been tested against via PubChem.
Search, filter, and retrieve macromolecular structures from the RCSB Protein Data Bank (PDB), including metadata, bound ligands, and optional coordinate/validation downloads.
Dock small-molecule ligands into a protein receptor using AutoDock Vina (Python API) and save ranked poses + docking metadata for reproducible virtual screening.
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Validate docked or generated ligand poses for physical plausibility using PoseBusters, filtering out chemically invalid or clashing poses before downstream refinement.
Architecture, component selection, firmware design, and regulatory considerations for a medical wearable device (smartwatch-class) that records patient vital signs (SpO2, ECG, heart rate, temperature,…
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Compute RDKit physicochemical descriptors and rule-based drug-likeness heuristics (Ro5, Veber, QED) from SMILES.
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Query PubChem via PUG-REST to retrieve CIDs, computed properties, synonyms, and 2D/3D SDF structures.
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Compute Morgan/ECFP fingerprints, Tanimoto similarity, and optional Butina clusters/heatmaps for small-molecule comparison.
Run a protein-ligand MD simulation in OpenMM with energy minimization, restrained equilibration, and production NPT, producing trajectory and checkpoint files for downstream analysis.
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Query ChEMBL web services for targets, molecules, and curated bioactivity measurements (IC50, Ki, EC50, etc.).
Post-docking analysis of virtual screening results including score distributions, enrichment metrics (ROC AUC, enrichment factors), and ligand efficiency calculations.
Prepare small-molecule ligands for docking and analysis via optional state enumeration, 3D conformer generation, MMFF/UFF minimization, and export to SDF + AutoDock PDBQT.
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Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
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