--- title: "drug-discovery-agent-skills" description: "Agent Skills for small-molecule and protein therapeutics: target validation and human genetics, bioactivity and chemical space, generative design and retrosynthesis, docking, free energy and dynamics, ADMET and PK transl" canonical: https://agentpluginsdirectory.com/plugins/drug-discovery-agent-skills last-updated: 2026-10-02 --- # drug-discovery-agent-skills Agent Skills for small-molecule and protein therapeutics: target validation and human genetics, bioactivity and chemical space, generative design and retrosynthesis, docking, free energy and dynamics, ADMET and PK translation, protein, antibody, degrader and oligonucleotide design, and the clinical and regulatory record. - Slug: drug-discovery-agent-skills - Publisher: K-Dense Inc. - Repository: https://github.com/K-Dense-AI/drug-discovery-agent-skills - Manifest: plugin.json - Version: 1.3.0 - License: MIT - Category (editorial): research - Skills: 37 (adaptyv, admet-prediction, antibody-engineering, autodock-vina, binding-site-analysis, boltz, chembl, chemical-space, clinicaltrials, datamol, deepchem, degraders, depmap, diffdock, esm, free-energy-perturbation, generative-design, glycoengineering, immunogenicity, medchem, molecular-dynamics, molfeat, ncats-arax, oligonucleotides, open-targets, openfda, patent-landscape, pkpd-translation, primekg, protein-binder-design, pytdc, rdkit, retrosynthesis, rowan, tamarind, target-safety, uniprot-rcsb) - MCP servers: 0 - Stars: 34 - Repository created: 2026-08-16 - Repository last pushed: 2026-09-28 - Publisher type: Organization - Listing: https://agentpluginsdirectory.com/plugins/drug-discovery-agent-skills - Schema: https://agent-plugins.org/schemas/1.0.0/plugin.schema.json ## What drug-discovery-agent-skills does, in the publisher's words Agent Skills for small-molecule and protein therapeutics: target validation and human genetics, bioactivity and purchasable chemical space, generative design and retrosynthesis, docking, free energy and dynamics, ADMET and dose projection, protein, antibody, degrader and oligonucleotide design, and the clinical and regulatory record. Thirty-seven skills that teach your coding agent the tools computational chemists and biologists actually use, how to install them, which API to call, what the parameters mean, and where each one breaks. The bundle runs end to end: resolve a disease to a target, check whether healthy humans have already lost it, pull the chemistry and structures that exist, find or design molecules, work out whether they can be made and what dose they would need, and check what the clinic already tried. From the project README, punctuation lightly normalized. Full text: https://raw.githubusercontent.com/K-Dense-AI/drug-discovery-agent-skills/HEAD/README.md ## Skills - adaptyv: How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit p… - admet-prediction: Turn a set of structures into absorption, distribution, metabolism, excretion, and toxicity estimates with ADMET-AI, and read them as a developability verdict rather than a table of numbers. Use this skill to run batch prediction over a library, interpret each endpoint against its DrugBank-approved… - antibody-engineering: Number antibody variable domains, annotate CDRs, and assess developability from sequence. Use this skill to apply IMGT, Kabat, Chothia, Martin, or AHo numbering with ANARCI, delimit CDRs and framework regions, scan for chemical liabilities (N-glycosylation sequons, deamidation NG, isomerisation DG,… - autodock-vina: Structure-based docking with AutoDock Vina, Vinardo, and AutoDock4 through the Meeko toolchain. Use this skill to define a docking box, prepare receptors and ligands as PDBQT, run single or batch docking, rescore, and interpret affinities, poses, and ligand efficiency. Covers box definition from a… - binding-site-analysis: Decide whether a protein has a pocket worth targeting, and where it is, before committing to a docking or design campaign. Use this skill to run fpocket cavity detection, rank cavities by druggability and volume, compare apo and holo conformations to spot induced fit, identify allosteric and crypti… - boltz: Cofold protein-ligand, protein-protein, and nucleic-acid complexes with Boltz-2, and predict binding affinity with its trained affinity head. Use this skill to build Boltz input YAML, run structure prediction with MSAs, pocket constraints, templates, and modified residues, screen compound libraries… - chembl: Query the ChEMBL database web services for measured bioactivity data, compound records and calculated properties, targets, assays, mechanisms of action, drug indications and warnings. Use this skill to build curated SAR or QSAR datasets for a target, look compounds up by SMILES, InChIKey, name, or… - chemical-space: Navigate make-on-demand catalogues, ZINC-22 through CartBlanche and Enamine REAL Space, to find compounds that can actually be ordered. Use this skill to look substances up by ZINC identifier or structure, understand tranche partitioning by heavy-atom count and logP, and choose between screening… - clinicaltrials: Search the ClinicalTrials.gov registry through its version 2 REST API for interventional and observational studies, their phases, enrolment, endpoints, sponsors, and posted results. Use this skill to survey who is developing what against an indication, date a competitor's programme, read primary an… - datamol: Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work: SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diver… - deepchem: Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. Fo… - degraders: Work on bifunctional degraders and molecular glues, where potency comes from a ternary complex rather than occupancy. Use this skill to apply the property rules that govern this beyond-rule-of-five space, reason about linker length, attachment vector and E3 ligase choice, prepare inputs for ternary… - depmap - diffdock - esm - free-energy-perturbation - generative-design - glycoengineering - immunogenicity - medchem - molecular-dynamics - molfeat - ncats-arax - oligonucleotides - open-targets - openfda - patent-landscape - pkpd-translation - primekg - protein-binder-design - pytdc - rdkit - retrosynthesis - rowan - tamarind - target-safety - uniprot-rcsb Descriptions come from the frontmatter of each SKILL.md, punctuation lightly normalized.