# Medchem

> Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski rule of five, Veber, Oprea, CNS, lead-like, rule of three), structural alert catalogs (PAINS a/b/c, NIBR screening-deck severity, Brenk, BMS, Glaxo, Dundee, ChEMBL common alerts), ZINC-15 percentile complexity metrics (Bertz, SAscore, QED, Whitlock, Barone), chemical-group detection, Lilly demerits, and the medch…

- **Type:** Skill
- **Install:** `agentstack add skill-k-dense-ai-drug-discovery-agent-skills-medchem`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [K-Dense-AI](https://agentstack.voostack.com/s/k-dense-ai)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [K-Dense-AI](https://github.com/K-Dense-AI)
- **Source:** https://github.com/K-Dense-AI/drug-discovery-agent-skills/tree/main/skills/medchem
- **Website:** www.k-dense.ai

## Install

```sh
agentstack add skill-k-dense-ai-drug-discovery-agent-skills-medchem
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Medchem

## Overview

Medchem is a Python library from [datamol-io](https://github.com/datamol-io/medchem) for molecular filtering and prioritization in drug discovery. Apply literature-derived drug-likeness rules, named alert catalogs, complexity thresholds, chemical-group detection, and a custom query language to triage compound libraries at scale. Filters are context-specific guidelines — combine with domain expertise and target knowledge.

**Checked against:** medchem **2.0.5** (PyPI stable, released 2024-11-18; still the current release
as of August 2026) — 22 rules and 32 named catalogs verified live. Requires **Python ≥3.9**. Depends on **datamol** and **RDKit** (installed automatically). `RuleFilters` and structural filter classes return **pandas DataFrames**. Lilly demerits require optional native binaries (`mamba install lilly-medchem-rules`).

## When to Use This Skill

This skill should be used when:
- Applying drug-likeness rules (Lipinski, Veber, CNS, lead-like) to compound libraries
- Filtering molecules by structural alerts, PAINS, or NIBR screening-deck rules
- Prioritizing compounds for hit-to-lead or lead optimization
- Calculating complexity metrics against ZINC-derived thresholds
- Detecting functional groups or named substructure catalogs
- Building multi-criteria filters with the medchem query language

## Installation

```bash
uv pip install medchem datamol
```

Optional — Eli Lilly demerit filter (requires conda-forge native binaries):

```bash
mamba install -c conda-forge lilly-medchem-rules
```

## Core Capabilities

### 1. Medicinal Chemistry Rules

Apply established drug-likeness rules via `medchem.rules`.

**List available rules:**

```python
import medchem as mc

mc.rules.RuleFilters.list_available_rules_names()
# ['rule_of_five', 'rule_of_five_beyond', 'rule_of_four', 'rule_of_three', ...]
```

**Single rule on one molecule:**

```python
import datamol as dm
import medchem as mc

smiles = "CC(=O)OC1=CC=CC=C1C(=O)O"  # aspirin
mc.rules.basic_rules.rule_of_five(smiles)   # True
mc.rules.basic_rules.rule_of_cns(smiles)    # True
mc.rules.basic_rules.rule_of_veber(smiles)  # True
```

**Multiple rules with `RuleFilters` (returns a DataFrame):**

```python
import datamol as dm
import medchem as mc

mols = [dm.to_mol(s) for s in smiles_list]

rfilter = mc.rules.RuleFilters(
    rule_list=["rule_of_five", "rule_of_oprea", "rule_of_cns", "rule_of_leadlike_soft"]
)
df = rfilter(mols=mols, n_jobs=-1, progress=True, keep_props=False)

# Columns: mol, pass_all, pass_any, rule_of_five, rule_of_oprea, ...
passing = df[df["pass_all"]]
```

Use `keep_props=True` to include computed descriptors (`mw`, `clogp`, `tpsa`, etc.) in the result.

### 2. Structural Alert Filters

Detect problematic patterns with `medchem.structural`. Both classes return **DataFrames** with `pass_filter`, `status`, and `reasons` columns.

**Common alerts (ChEMBL-derived rule sets):**

```python
import medchem as mc

alert_filter = mc.structural.CommonAlertsFilters()
df = alert_filter(mols=mol_list, n_jobs=-1, progress=True)
# df columns: mol, pass_filter, status, reasons

clean = df[df["pass_filter"]]
```

**NIBR filters (Novartis screening-deck curation):**

```python
nibr_filter = mc.structural.NIBRFilters()
df = nibr_filter(mols=mol_list, n_jobs=-1, progress=True)
# df columns: mol, pass_filter, status, severity, reasons, n_covalent_motif, special_mol
```

Compounds with `severity >= 10` are excluded by default (see NIBR paper).

### 3. Named Catalog Filters (PAINS, Brenk, etc.)

Use `medchem.catalogs.NamedCatalogs` for RDKit `FilterCatalog` instances, or the functional API:

```python
import medchem as mc

# List available named catalogs
mc.catalogs.list_named_catalogs()
# ['tox', 'pains', 'pains_a', 'brenk', 'nibr', 'zinc', ...]

# Functional API — True means molecule passes (no alert match)
passes = mc.functional.alert_filter(mols=mol_list, alerts=["pains"], n_jobs=-1)

# Or via catalog objects
passes = mc.functional.catalog_filter(
    mols=mol_list,
    catalogs=[mc.catalogs.NamedCatalogs.pains()],
    n_jobs=-1,
)
```

### 4. Functional API

`medchem.functional` provides one-call wrappers that return boolean masks (True = passes):

```python
import medchem as mc

mc.functional.rules_filter(mols=mol_list, rules=["rule_of_five", "rule_of_cns"], n_jobs=-1)
mc.functional.nibr_filter(mols=mol_list, max_severity=10, n_jobs=-1)
mc.functional.alert_filter(mols=mol_list, alerts=["pains", "brenk"], n_jobs=-1)
mc.functional.complexity_filter(mols=mol_list, complexity_metric="bertz", limit="99", n_jobs=-1)
```

Other helpers: `catalog_filter`, `chemical_group_filter`, `lilly_demerit_filter` (requires optional binaries), `macrocycle_filter`, `bredt_filter`, `protecting_groups_filter`, and more.

### 5. Chemical Groups

Detect functional groups and curated pattern collections via `medchem.groups`:

```python
import medchem as mc

# Browse available group collections
mc.groups.list_default_chemical_groups()
# ['privileged_scaffolds', 'common_warhead_covalent_inhibitors', 'rings_in_drugs', ...]

group = mc.groups.ChemicalGroup(groups=["privileged_scaffolds"])
group.has_match(mol)                          # bool
group.get_matches(mol)                        # dict of group → atom indices
group.filter(mols)                            # molecules matching the group

# Returns molecules that do NOT match the group
mc.functional.chemical_group_filter(mols=mol_list, chemical_group=group, n_jobs=-1)
```

Custom groups can be loaded from a file via `groups_db` (CSV with `smiles`/`smarts`, `name`, `group` columns).

### 6. Molecular Complexity

Compare complexity metrics to precomputed ZINC-15 percentile thresholds:

```python
import medchem as mc

# Single molecule
cf = mc.complexity.ComplexityFilter(limit="99", complexity_metric="bertz")
cf(mol)  # True if below 99th-percentile threshold

# Batch via functional API
mc.functional.complexity_filter(
    mols=mol_list,
    complexity_metric="bertz",  # also: sas, qed, whitlock, barone, smcm, twc
    limit="99",
    n_jobs=-1,
)

# Direct metric functions
mc.complexity.WhitlockCT(mol)
mc.complexity.BaroneCT(mol)
```

### 7. Scaffold Constraints

`medchem.constraints.Constraints` matches a core scaffold and applies per-atom constraint functions — not simple MW/LogP ranges. For property bounds, use `RuleFilters`, descriptors via `mc.rules.list_descriptors()`, or the query language.

```python
import datamol as dm
import medchem as mc

core = dm.to_mol("c1ccccc1")
constraints = mc.constraints.Constraints(
    core=core,
    constraint_fns={"query": lambda mol, atom_idx, query: ...},
)
constraints(mol)
```

### 8. Medchem Query Language

Build multi-criteria filters with `medchem.query.QueryFilter`:

```python
import medchem as mc

# Rule + alert combination
qf = mc.query.QueryFilter('MATCHRULE("rule_of_five") AND NOT HASALERT("pains")')
mask = qf(mols=mol_list, n_jobs=-1)  # list[bool]

# CNS-like with property bounds
qf = mc.query.QueryFilter('MATCHRULE("rule_of_cns") AND HASPROP("tpsa", 1000 molecules.
4. **Check return types** — `RuleFilters` and structural classes return DataFrames; functional helpers return boolean arrays.
5. **Lilly demerits are optional** — install `lilly-medchem-rules` separately; default max demerits is 160 in the functional API.
6. **Document decisions** — retain `status`, `reasons`, and `severity` columns for audit trails.

## Resources

### references/api_guide.md
Module-by-module API reference with signatures, return types, and patterns.

### references/rules_catalog.md
Catalog of available rules, alert sets, complexity metrics, and filter selection guidelines.

### scripts/filter_molecules.py
Batch filtering script for CSV/TSV/SDF/SMILES inputs with configurable rules, alerts, and complexity thresholds.

```bash
python skills/medchem/scripts/filter_molecules.py input.csv \
  --rules rule_of_five,rule_of_cns --pains --nibr --output filtered.csv
```

`--output` is required. Alongside it the script writes `_summary.txt` unless
`--no-summary` is passed.

## Composing with the rest of the bundle

- `rdkit` / `datamol` → before: parse and **standardize first**. An alert catalog matched against an
  unstandardized salt or tautomer answers a question about the wrong species.
- `chembl` → before: the measured library worth triaging.
- `chemical-space` → around: this is the cheap filter stage in an ultra-large screening cascade.
  Run it before docking, not after.
- `admet-prediction` → after: alerts cost nothing and catch much of what ADMET models flag more
  expensively and less reliably.
- `generative-design` / `retrosynthesis` → after: generated molecules need triage before route
  search. A PAINS hit is not worth a synthesis plan.
- `autodock-vina` / `diffdock` → before: docking 20,000 frequent hitters wastes the compute and
  pollutes the hit list.

**Do not over-filter early.** Alerts are context-specific guidelines, not verdicts — marketed drugs
violate Ro5 routinely, and covalent warheads are a liability only if you did not want a covalent
inhibitor. Keep `status`, `reasons`, and `severity` so a rejection can be argued with.

## Documentation

- Official docs: https://medchem-docs.datamol.io/
- GitHub: https://github.com/datamol-io/medchem
- PyPI: https://pypi.org/project/medchem/ (2.0.5)

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [K-Dense-AI](https://github.com/K-Dense-AI)
- **Source:** [K-Dense-AI/drug-discovery-agent-skills](https://github.com/K-Dense-AI/drug-discovery-agent-skills)
- **License:** MIT
- **Homepage:** www.k-dense.ai

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-k-dense-ai-drug-discovery-agent-skills-medchem
- Seller: https://agentstack.voostack.com/s/k-dense-ai
- Browse the marketplace: https://agentstack.voostack.com/browse

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