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Efficient evolution with ESM-1v

ESM-1v efficient evolution

Peer-reviewed Prospective flag

Summary card

Поле	Значение
Год / version	2024 / NR
Last verified	2026-08-13
Что генерирует	Ranked single-amino-acid substitutions
Форматы	Human antibodies; tested across seven antibody series
De novo или optimization	Optimizes existing antibody
DOI	10.1038/s41587-023-01763-2
Paper	https://doi.org/10.1038/s41587-023-01763-2
GitHub / repo	https://github.com/brianhie/efficient-evolution
Service / docs	NR
Лицензия	Repository license NR in this pass; ESM dependencies apply
Доступность / commercial-use	Scripts + model dependency

Задача и generated object

Used in these tasks: Предложение single mutations, Affinity maturation, ML-guided directed evolution

Part of these workflows: Zero-shot mutation proposal

Поле	Значение
Required input	Parent antibody sequence
Conditioning	Single substitutions; no antigen/structure required
Fixed	NR
Editable	NR
Generated	Ranked single-amino-acid substitutions
Single mutations	Yes
Combinatorial mutations	No in primary workflow
Multi-objective	No explicit multi-objective optimizer

Architecture and operating principle

Author-reported (matrix)

Поле	Значение
Base architecture	NR
Model type	6 Mutation generator / optimization workflow
Representation	NR
Generation unit	Ranked single-amino-acid substitutions
Conditioning	Single substitutions; no antigen/structure required
Output	Ranked single-amino-acid substitutions
Sampling	NR
Internal scorer	ESM-1v ensemble likelihood
Structure generation	No

Input representation

Поле	Значение
Node features	NR
Edge / pair features	NR

Encoder и decoder

Поле	Значение
Encoder	NR
Decoder	NR

Latent or generative state

Conditioning mechanism

Single substitutions; no antigen/structure required

Generation mechanism

Ensemble protein language-model likelihood ranking with efficient experimental selection

Sampling: NR

Training objectives

Losses: NR

Mathematical details: NR

Integrated evaluation

ESM-1v ensemble likelihood

Architectural limitations

Zero-shot evolutionary plausibility, not affinity prediction.
combination/epistasis not addressed.
improvements are antibody/assay specific.

Uses these architecture components: Protein language models, Zero-shot mutation scoring

Generator versus evaluation

Component	Role	Architecture class	Integrated or external	Required or optional	Influences generation or post hoc	Runtime included or excluded
ESM-1v mutation ranking/proposal workflow	primary_generator	Ensemble protein language-model likelihood ranking with efficient experimental selection	integrated	required	generation	included
ESM-1v ensemble likelihood	integrated_scorer	NR	integrated	optional	post_hoc	unknown
Binding/neutralization and developability checks	mandatory_external_dependency	NR	external	required	post_hoc	excluded
Experimental binding/function assays	experimental_validation	NR	external	required	post_hoc	excluded

Training data и biases

Поле	Значение
Training data	NR
Preprocessing	NR
Negative examples	NR
Fine-tuning	NR
Label requirements	NR
Training hardware	NR
Training time	NR

Inference hardware и runtime

Поле	Значение
Candidate count	NR
Batch size	NR
Generation steps	NR
Recycles	NR
Size	NR
Reported runtime	NR
Runtime boundary	NR
CPU	NR
GPU	NR
VRAM	NR
RAM	NR
Disk	NR
Databases	NR

Experimental evidence

Поле	Значение
Evidence class	4 prospective functional validation
Retrospective	Seven antibodies; roughly 10 single substitutions per parent selected for testing
Prospective binding	C143 variants included 32× Beta and 19× D614G neutralization improvements; workload is single-mutation only
Prospective function	C143 variants included 32× Beta and 19× D614G neutralization improvements; workload is single-mutation only
Developability	NR
Tested design count	Seven antibodies; roughly 10 single substitutions per parent selected for testing
Success criterion	C143 variants included 32× Beta and 19× D614G neutralization improvements; workload is single-mutation only
Independent validation	NR
Summary	C143 variants included 32× Beta and 19× D614G neutralization improvements; workload is single-mutation only

Limitations and failure modes

Zero-shot evolutionary plausibility, not affinity prediction.
combination/epistasis not addressed.
improvements are antibody/assay specific.

Deployment: Limited internal pilot

Reproducibility, license, commercial use

Поле	Значение
Repo	https://github.com/brianhie/efficient-evolution
License	Repository license NR in this pass; ESM dependencies apply
Commercial-use status	Scripts + model dependency

Alternative tools: Structural evolution (ESM-IF1 complex)

Complementary: EVOLVEpro

Not directly comparable with

EVOLVEpro, FrameDiff, Makowski et al. affinity–specificity co-optimization, RFantibody, RFdiffusion, RFdiffusion All-Atom (RFdiffusionAA), tFold System

Сравнение допустимо только внутри совместимых категорий. Runtime и quality AntiFold vs RFantibody нельзя читать как одну шкалу: разные starting points и generated objects.

Depends on these external modules

Primary paper и полный список источников

Efficient evolution of human antibodies from general protein language models

Source matrix: landscape xlsx, last verified 2026-08-13.

Детальный source-grounded разбор

1. Что именно делает инструмент

[MATRIX]Вход: Parent antibody sequence

[MATRIX]Generated object: Ranked single-amino-acid substitutions

[MATRIX]Фиксируется: NR. Изменяется: NR.

[MATRIX]De novo или parent-based: Optimizes existing antibody.

2. Архитектура в одном абзаце

[MATRIX]Ensemble protein language-model likelihood ranking with efficient experimental selection

4. Математическая модель

NRТочные уравнения, losses и sampling solver будут внесены после чтения paper/code. Не подставляйте родовой UCB/transformer/diffusion, если он не подтверждён для этого инструмента.

Поля NR: conditional probability, update equations, sampling algorithm

5. Физическое устройство состояния

[MATRIX]Seed generated state / output: Ranked single-amino-acid substitutions. Structure generation: No.

6. Физико-химическая интерпретация

[INTERPRETATION]Координаты, frames, PLM embeddings или residue tokens не являются расчётом ΔG, электростатики или растворителя, пока это не сделано внешней energy function. Likelihood / pLDDT / docking score ≠ экспериментальный binding.

7. Training

[MATRIX]Данные: NR. Fine-tuning: NR. Negative examples: NR.

8. Inference

[MATRIX]Sampling: NR. Generation steps: NR. Runtime boundary: NR.

10. Почему, по мнению авторов, метод эффективен

[MATRIX]Author-reported summary (матрица): C143 variants included 32× Beta and 19× D614G neutralization improvements; workload is single-mutation only

NRCausal support не классифицирован, пока не прочитаны ablation и experimental sections.

12. Ablation analysis

NRAblation в этом выпуске не разобрана.

13. What the model does not represent

Явный растворитель и ионная сила — обычно отсутствуют в генераторе.
Протонирование, гликозилирование, кофакторы — обычно отсутствуют, если не оговорено.
Конформационная энтропия и долгая динамика — не моделируются одним forward pass.
Экспрессия, агрегация, иммуногенность — не равны likelihood генератора.
Истинная binding free energy — не равна internal score.

14. Evidence and limitations

[MATRIX]Evidence class (матрица): 4 prospective functional validation. Prospective binding: C143 variants included 32× Beta and 19× D614G neutralization improvements; workload is single-mutation only. Function: C143 variants included 32× Beta and 19× D614G neutralization improvements; workload is single-mutation only. Developability: NR.

Zero-shot evolutionary plausibility, not affinity prediction.
combination/epistasis not addressed.
improvements are antibody/assay specific.

15. Reproducibility

NRРепозиторий: https://github.com/brianhie/efficient-evolution. Лицензия: Repository license NR in this pass; ESM dependencies apply. Commit: NR, пока страница не имеет отдельного analysis YAML.

Модульная схема

Модуль	Класс модели	Вход	Выход	Обучается или frozen	Generator или evaluator
ESM-1v mutation ranking/proposal workflow	Ensemble protein language-model likelihood ranking with efficient experimental selection	NR	NR	NR	primary_generator
ESM-1v ensemble likelihood	NR	NR	NR	NR	integrated_scorer
Binding/neutralization and developability checks	NR	NR	NR	NR	mandatory_external_dependency
Experimental binding/function assays	NR	NR	NR	NR	experimental_validation

Generator versus evaluator

Компонент	Роль	Integrated или external	Required или optional	Влияет на generation или только ранжирует после
ESM-1v mutation ranking/proposal workflow	primary_generator	integrated	required	generation
ESM-1v ensemble likelihood	integrated_scorer	integrated	optional	post_hoc
Binding/neutralization and developability checks	mandatory_external_dependency	external	required	post_hoc
Experimental binding/function assays	experimental_validation	external	required	post_hoc

Claim–evidence matrix

Утверждение	Предполагаемый механизм	Тип evidence	Датасет / эксперимент	Comparator	Метрика / результат	Источник	Оговорка
C143 variants included 32× Beta and 19× D614G neutralization improvements; workload is single-mutation only	Ensemble protein language-model likelihood ranking with efficient experimental selection	5. prospective functional validation	Seven antibodies; roughly 10 single substitutions per parent selected for testing	NR	C143 variants included 32× Beta and 19× D614G neutralization improvements; workload is single-mutation only	[MATRIX] landscape matrix 2026-08-13	Не читать как полный разбор primary paper.