Track candidate model integrations (open weights, pip-installable, active)

[iskandr]

Tracking models worth integrating — criteria: open weights, installable via pip/conda, actively maintained.

Class I

Model	Install	Notes
MHCflurry	pip install mhcflurry	Already integrated. Affinity + presentation.
BigMHC	git clone	Already integrated. Presentation + immunogenicity.
MHCRoBERTa	GitHub	RoBERTa transfer learning for class I affinity. Published 2022. Check if pip-installable.

Class II

Model	Install	Notes
NetMHCIIpan 4.3	binary	Already integrated. DTU tool, affinity + EL.

Processing

Model	Install	Notes
Pepsickle	pip install pepsickle	Already integrated. Proteasomal cleavage (epitope model).

Not integrating

Model	Reason
HLApollo (Genentech)	Linux-only compiled binary, no Python API (#179)
BERTMHC	Unmaintained since 2020 (#162)

Open to suggestions — if you know of a model that's pip-installable with open weights, comment here.

[iskandr]

Surveyed recent models (April 2026):

• MHCRoBERTa — dead repo, 2 commits total, requires custom fairseq fork. Not integrating.
• RPEMHC (Bioinformatics 2024) — no pip package found
• ESM-MHC (2024) — no pip package found
• CapsNet-MHC (2023) — no pip package found
• HLApollo — Linux-only binary, already closed in Add HLApollo integration #179

The pattern is clear: most academic MHC prediction tools are published as one-off repos without proper packaging. The pip-installable tools we already integrate (MHCflurry, pepsickle) plus the DTU command-line tools (NetMHCpan, NetMHCIIpan) cover the practical use cases well.

Will check again when new tools appear on PyPI.