Drug-discovery analyses: connectivity-map / signature reversal, synergy & dose-response, genetic interactions

Context

pertpy already ships the drug/MoA/compound metadata scaffolding (pt.md.Drug, pt.md.Moa, pt.md.Compound) and drug2cell Enrichment, but not the analyses that consume it.
These are core pharma workflows sitting right next to metadata we already provide.

What's missing

• Connectivity-map / signature reversal (LINCS/L1000-style): given a disease/query signature, rank perturbations by how strongly they reverse it. Complements drug2cell enrichment.
• Synergy models for combinations: Bliss / Loewe combination-index models. Cinemaot covers single-cell synergy via optimal transport, but not the classic combination-index framework.
• Dose-response curve fitting: Hill / EC50 / IC50 as first-class output, building on the dose-response quantification already in the perturbation-space base class.
• Genetic-interaction / epistasis scoring (Norman 2019-style GI scores) for double perturbations.

Proposal / API

Add these under pertpy.tools, reusing the perturbation-space representations and Distance metrics where possible.
Signature reversal can operate on perturbation-space embeddings; GI scoring on paired-vs-single perturbation representations.