Skill UI

This comprehensive guide details strategies for migrating enterprise data platforms to Databricks. It provides PySpark examples for discovery, schema mapping (converting types from Hadoop, Snowflake, etc.), and executing robust data transfer methods like SYNC, DEEP CLONE, and CTAS. Use this when moving data from legacy sources like Hadoop, Snowflake, or Redshift to a modern Delta Lake environment.

Datamol wraps RDKit with Pythonic APIs tailored for drug discovery, offering SMILES/SELFIES/InChI parsing, standardization, descriptor and fingerprint calculation, clustering, 3D conformer workflows, and parallel/cloud-aware batch I/O while returning native rdkit.Chem.Mol objects.

Query the NIH Metabolomics Workbench via REST API to fetch metabolites, RefMet nomenclature, mass-spec searches, and detailed study datasets for biomarker discovery and metabolomics research.

This sophisticated workflow guides users from a broad research concept or initial topic to a fully validated, actionable, and pilot-tested research idea. It orchestrates a multi-stage pipeline, including literature surveys, novelty checks, detailed idea generation, critical review, and the creation of a refined experimental plan, ensuring the output is a robust proposal ready for implementation.

This skill provides a systematic framework for managing Indicators of Compromise (IOCs) throughout their entire life cycle—from initial discovery and validation to enrichment, deployment, monitoring, and eventual retirement. It emphasizes implementing confidence decay functions, tracking hit rates, and managing false positives to maintain a high-quality, actionable threat intelligence database, thereby minimizing analyst fatigue and maximizing detection efficacy.

Hypogenic is a powerful framework designed to accelerate scientific discovery by automating the process of hypothesis generation and testing on complex tabular datasets. It supports three advanced modes: HypoGeniC for purely data-driven pattern discovery; HypoRefine for synergistically integrating literature insights with empirical data; and Union methods for comprehensive hypothesis coverage. Ideal for domains like deception detection, content analysis, and advanced predictive modeling where systematic exploration of multiple theories is required.

This powerful Python library provides comprehensive medicinal chemistry filters for compound triage in drug discovery. Users can apply established drug-likeness rules (like Lipinski's Rule of Five, Veber, and CNS rules), detect structural alerts (PAINS, NIBR), calculate complexity metrics, and filter molecular libraries using a custom query language. It is essential for prioritizing hit compounds and optimizing lead structures.

PyTDC (Therapeutics Data Commons) is an open-science platform providing standardized, AI-ready datasets and benchmarks for drug discovery and development. It supports three core types of tasks: single-instance prediction (e.g., ADME, toxicity), multi-instance prediction (e.g., DDI, DTI), and novel molecule generation. It is essential for benchmarking and developing therapeutic machine learning models.