---
name: medchem-c20ed8f0e020
description: Medicinal chemistry screening filters for compound prioritization; use when you need to apply drug-likeness rules, PAINS/structural alerts, and complexity metrics to triage or optimize libraries.
license: MIT
author: AIPOCH
---
> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

## When to Use

- Screening large compound libraries to quickly triage for drug-like candidates (e.g., Lipinski/Veber + alerts).
- Flagging problematic chemotypes (e.g., PAINS, reactive/toxicophores, curated structural alerts) before follow-up assays.
- Prioritizing lead-optimization candidates with stricter criteria (lead-like rules, demerit systems, complexity caps).
- Enforcing property constraints (MW/logP/TPSA/rotatable bonds) for target-specific design windows (e.g., CNS).
- Identifying molecules containing specific functional groups/scaffolds (e.g., Michael acceptors, hinge binders) for SAR or risk assessment.

## Key Features

- **Drug-likeness and medchem rule sets**: Lipinski (Ro5), Veber, Oprea, CNS, lead-like (soft/strict), Rule of Three, REOS, Golden Triangle, etc.
- **PAINS and structural alert filtering**: curated alert catalogs and pattern-based screening.
- **Curated industrial filter sets**: e.g., NIBR filters; **Lilly demerit** scoring with pass/fail thresholds.
- **Functional-group detection** via SMARTS-based group matchers (hinge binders, phosphate binders, Michael acceptors, reactive groups, custom patterns).
- **Named catalogs** of curated structures (functional groups, protecting groups, reagents, fragments) for matching and annotation.
- **Molecular complexity metrics** (e.g., Bertz/Whitlock/Barone-style) and threshold-based complexity filters.
- **Constraint-based filtering** for property windows (MW/logP/TPSA/RB, etc.).
- **Query language** to combine heterogeneous criteria (rules + alerts + numeric thresholds) into a single expression.

## Dependencies

- `medchem` (latest)
- `datamol` (latest)
- `pandas` (latest, for tabular workflows)

## Example Usage

```python
# End-to-end, runnable example:
# 1) load SMILES
# 2) apply Ro5 + Veber
# 3) apply common structural alerts
# 4) compute complexity and filter
# 5) export a CSV with decisions

import pandas as pd
import datamol as dm
import medchem as mc

smiles_list = [
    "CC(=O)OC1=CC=CC=C1C(=O)O",  # aspirin
    "CN1C=NC2=C1C(=O)N(C(=O)N2C)C",  # caffeine
    "c1ccccc1",  # benzene
]

df = pd.DataFrame({"smiles": smiles_list})
mols = [dm.to_mol(smi) for smi in df["smiles"]]

# 1) Drug-likeness rules
rule_filter = mc.rules.RuleFilters(rule_list=["rule_of_five", "rule_of_veber"])
rule_res = rule_filter(mols=mols, n_jobs=-1, progress=False)
df["passes_rules"] = rule_res["pass"]

# 2) Structural alerts
alerts = mc.structural.CommonAlertsFilters()
alert_res = alerts(mols=mols, n_jobs=-1, progress=False)
df["has_alerts"] = alert_res["has_alerts"]

# 3) Complexity (example threshold)
complex_filter = mc.complexity.ComplexityFilter(max_complexity=500)
complex_res = complex_filter(mols=mols, n_jobs=-1, progress=False)
df["passes_complexity"] = complex_res["pass"]

# 4) Final decision
df["keep"] = df["passes_rules"] & (~df["has_alerts"]) & df["passes_complexity"]

# 5) Save results
df.to_csv("medchem_screening_results.csv", index=False)
print(df)
```

## Implementation Details

- **Rule evaluation (`medchem.rules`)**
  - Rules are implemented as callable checks over SMILES or RDKit-like molecule objects (commonly via `datamol`).
  - `RuleFilters(rule_list=[...])` applies multiple rules and returns a structured result (typically including an overall `pass` plus per-rule details).
  - Typical use: start broad (Ro5/Veber), then tighten (CNS/lead-like) as project constraints become clearer.

- **Structural alerts (`medchem.structural`)**
  - Alert systems are primarily **SMARTS/pattern-based** matchers curated from literature/industrial practice.
  - `CommonAlertsFilters`, `NIBRFilters`, and `LillyDemeritsFilters` provide different philosophies:
    - **Common alerts**: general-purpose red flags.
    - **NIBR**: curated industrial filter set.
    - **Lilly demerits**: assigns penalties per matched rule; a common convention is **reject if total demerits > 100**.

- **Complexity (`medchem.complexity`)**
  - Complexity scores approximate synthetic difficulty / structural intricacy using established heuristics (e.g., Bertz/Whitlock/Barone-style metrics).
  - `ComplexityFilter(max_complexity=...)` converts a numeric score into a pass/fail gate for library triage.

- **Constraints (`medchem.constraints`)**
  - Property windows (MW/logP/TPSA/rotatable bonds, etc.) are applied as **hard filters**.
  - Use constraints to encode target-specific design hypotheses (e.g., CNS-like space) rather than universal “good/bad” judgments.

- **Groups and catalogs (`medchem.groups`, `medchem.catalogs`)**
  - Group detection is SMARTS-driven and returns boolean matches and/or match details (substructure hits).
  - Named catalogs provide curated sets for consistent annotation and matching across projects.

- **Parallelization**
  - Most batch APIs accept `n_jobs`; set `n_jobs=-1` to use all available CPU cores for large libraries.

---
Source attribution: aipoch
Original project: https://github.com/aipoch/medical-research-skills
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: aipoch
Original project: https://github.com/aipoch/medical-research-skills
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences, Product Management
Roles: Clinical Research, Data Scientist, Medical Writer, Product Manager, Researcher
Specialties: API, Backend
Tools: Datamol, Pandas
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: e0cc1eca466e2f31ce95b67c76debc6f9e14574e0ab454b46923d6edcb6834a5