SKILL

medchem

药物化学过滤器。应用类药性规则(Lipinski、Veber)、PAINS 过滤器、结构警示和复杂度指标,用于化合物优先级排序和化合物库筛选。

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Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.

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原文涵盖的主题

  • Medchem
  • Overview
  • When to Use This Skill
  • Installation
  • Core Capabilities
  • 1. Medicinal Chemistry Rules
  • 2. Structural Alert Filters
  • 3. Functional API for High-Level Operations

Medchem is a Python library for molecular filtering and prioritization in drug discovery workflows. Apply hundreds of well-established and novel molecular filters, structural alerts, and medicinal chemistry rules to efficiently triage and prioritize compound libraries at scale. Rules and filters are context-specific—use as guidelines combined with domain expertise. This skill should be used when: Applying drug-likeness rules (Lipinski, Veber, etc.) to compound libraries Filtering molecules by structural alerts or PAINS patterns Prioritizing compounds for lead optimization Assessing compound quality and medicinal chemistry properties Detecting reactive or problematic functional groups Calculating molecular complexity metrics Apply established drug-likeness rules to molecules using the medchem.rules module. Available Rules: Rule of Five (Lipinski) Rule of Oprea Rule of CNS Rule of leadlike (soft and strict) Rule of three Rule of Reos Rule of drug Rule of Veber Golden triangle PAINS filters Single Rule Application: Multiple Rules with RuleFilters: Result Format: Results are returned as dictionaries with pass/fail status and detailed information for each rule. Detect potentially proble

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来源仓库
davila7/claude-code-templates
源文件
cli-tool/components/skills/scientific/medchem/SKILL.md
固定版本
21be197f100a12b8d0dd9a211cc244c8179f84d3
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原作者:davila7/claude-code-templates · 原作者仓库元数据。文档核查:未核查。最近来源检查:2026-11-09T15:01:40.162Z。

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