Small-Molecule Design Tools

This is an agent skill for routing small-molecule design tasks to suitable open tools. It is meant to improve agent performance on complex workflows where the agent must choose methods, load focused references, and keep licensing or data constraints in view while it works.

Use the skill in two passes:

1. Identify the physical or chemical task.
2. Open the matching reference file before recommending tools or writing code.

For commercial or product-facing work, also open references/licensing-and-data.md. Code, weights, data, and base-model terms often differ for the same upstream project.

Reference Map

Start with references/tool-matrix.md when the user wants a broad comparison across categories.

Makeability and synthesis:

• references/synthesizable-generation.md:

makeable molecule generation, analogs, make-on-demand libraries, and route co-generation.

• references/molecular-generation.md:

general molecule generators where validity matters more than synthesis route guarantees.

• references/retrosynthesis-planning.md:

multi-step route planners such as AiZynthFinder, ASKCOS, Syntheseus, and SynPlanner.

• references/singlestep-retrosynthesis.md:

one-step precursor prediction with ReactionT5v2, RXNGraphormer, GDiffRetro, RetroDiT, ConRetroBert, TempRe, and related models.

• references/agentic-retrosynthesis.md:

LLM and agent-assisted synthesis planners.

• references/forward-and-reaction-modeling.md:

forward reaction and mechanism prediction.

• references/template-and-rule-infrastructure.md:

reaction templates, SMARTS rules, RDChiral, rxnutils, and SynTemp.

• references/synthesizability-scoring.md:

makeability scoring and route consistency checks.

Target-based design:

• references/protein-structure-prediction.md:

receptor structure prediction and preparation.

• references/docking-and-cofolding.md:

classical docking, learned docking, protein-ligand co-folding, and pose scoring.

• references/binding-affinity-and-fep.md:

ML affinity, OpenFE, OpenMM, endpoint methods, and free-energy workflows.

• references/property-and-qsar-prediction.md:

QSAR, activity, property, and active-learning screens.

• references/admet-prediction.md:

ADMET, hERG, BBB, CYP, and deployable local predictors.

• references/target-and-selectivity-prediction.md:

off-target, selectivity, shape, and pharmacophore tools.

• references/structure-based-generation.md:

pocket-conditioned generation and pocket detection.

Cross-cutting:

• references/agentic-drug-design.md:

agent frameworks that orchestrate generation, scoring, ADMET, and affinity tools.

• references/chemical-language-models.md:

chemistry language models and multimodal chemistry models.

• references/licensing-and-data.md:

license layers, Enamine terms, PDB/PDBBind notes, ChEMBL/TDC data terms, and commercial-use checklist.

• references/worked-example-kras-glue.md:

public worked example on daraxonrasib, KRAS, and CypA using PDB 9BG6.

• references/watchlist.md:

promising tools with missing code, missing weights, missing license clarity, or immature releases.

Routing Rules

Use the user's actual task to choose the first reference:

First Picks

Use these as starting points, then read the relevant reference file for details:

Operating Notes

• Read source licenses directly. README badges and paper text can disagree with

the repository license, model-card license, or dataset terms.

• Check code, weights, data, and base-model terms separately. LLM fine-tunes add

the original model terms to the project terms.

• Treat docking and co-folding confidence scores as pose-confidence signals.

Binding-affinity estimates require an affinity model or a free-energy method.

• For molecular glues, degraders, covalent binders, and macrocycles, choose tools

that match the mechanism. A single-pocket docking workflow is often the wrong starting point for those systems.

• When an experimental ternary structure exists, consider perturbation and

scoring on that scaffold before asking a co-folding model to rediscover it.

• Pair pocket-conditioned generators with a makeability step. Most structure-

based generators do not guarantee a synthesis route.

• Re-check live upstream repositories for recent tools, especially pre-1.0

models and 2026 additions.

Pipeline Pattern

A full agent loop usually crosses both layers:

1. Get or predict the receptor structure.
2. Generate, dock, co-fold, or score candidate molecules for the target.
3. Filter for affinity, QSAR, ADMET, and selectivity.
4. Project survivors into synthesizable space.
5. Plan or validate routes.
6. Re-check licenses and data terms before claiming deployability.

Load the detailed reference file for install commands, model weights, benchmark context, GPU needs, and current caveats.