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k-dense-ai/drug-discovery-agent-skills

v1.3.0MIT

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.

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 complex structure prediction, and interpret degradation readouts — DC50, Dmax, cooperativity, and the hook effect that makes a dose-response curve turn over. Also trigger on PROTAC, molecular glue, targeted protein degradation, E3 ligase, cereblon, VHL, ternary complex, DC50, Dmax, hook effect, cooperativity, or PROTAC-DB.

Version
1.0
License
MIT
Compatibility
Requires Python 3.10+. The bundled scripts implement degrader property rules, linker metrics, and ternary-complex input preparation using only the standard library. Predicting a ternary structure needs an external tool (PRosettaC, AlphaFold3, or DeepTernary) with its own licence and, in most cases, a GPU.
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Pre-approved tools experimental

Experimental field. Support varies between clients, so this list is what the author declared, not what your client will enforce.

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