Evolutionary Profiles for Protein Fitness Prediction | Paper Citation Record

Observation b424d2d2-c948-499c-9160-6e4d0d6177b9 · outbound

This paper cites Correlated mutations and residue contacts in proteins.Proteins: Structure, Function, and Bioin- formatics, 18(4):309–317.

Evolutionary Profiles for Protein Fitness Prediction Correlated mutations and residue contacts in proteins.Proteins: Structure, Function, and Bioin- formatics, 18(4):309–317

Reference 1

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Observation d6bd861c-144a-4a62-9fcd-4293794c06da · outbound

This paper cites Exploring protein fitness landscapes by directed evolution.Nature reviews Molecular cell biology, 10(12):866–876.

Evolutionary Profiles for Protein Fitness Prediction Exploring protein fitness landscapes by directed evolution.Nature reviews Molecular cell biology, 10(12):866–876

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.178013Z

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Observation 1781fb0e-d25f-4ac1-8a02-a02604c77564 · outbound

This paper cites Machine learning for functional protein design.Nature biotechnology, 42(2):216–228.

Evolutionary Profiles for Protein Fitness Prediction Machine learning for functional protein design.Nature biotechnology, 42(2):216–228

Reference 3

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Observation 11f3f0d5-ec41-4d57-8ce7-baebb772b370 · outbound

This paper cites Low-n protein engineering with data-efficient deep learning.Nature methods, 18(4):389–396.

Evolutionary Profiles for Protein Fitness Prediction Low-n protein engineering with data-efficient deep learning.Nature methods, 18(4):389–396

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.184658Z

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Observation 1211bcc1-4293-4df4-9392-5f1654347df7 · outbound

This paper cites Mutation effects predicted from sequence co-variation.Nature biotechnology, 35(2):128–135.

Evolutionary Profiles for Protein Fitness Prediction Mutation effects predicted from sequence co-variation.Nature biotechnology, 35(2):128–135

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.194649Z

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Observation 09ea7c06-a8c4-4805-a528-aa960b357a13 · outbound

This paper cites Language models enable zero-shot prediction of the effects of mutations on protein function.Advances in neural information processing systems, 34:29287–29303.

Evolutionary Profiles for Protein Fitness Prediction Language models enable zero-shot prediction of the effects of mutations on protein function.Advances in neural information processing systems, 34:29287–29303

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.187889Z

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Observation 9fe71b03-d243-4798-949f-85cb17906abb · outbound

This paper cites Multi-scale representation learning for protein fitness prediction.

Evolutionary Profiles for Protein Fitness Prediction Multi-scale representation learning for protein fitness prediction

Reference 7

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Observation acff2c83-3604-4f19-93d2-824ecbce2eb4 · outbound

This paper cites Lawrence Zitnick, Jerry Ma, and Rob Fergus.

Evolutionary Profiles for Protein Fitness Prediction Lawrence Zitnick, Jerry Ma, and Rob Fergus

Reference 8

Resolution: verified exact; doi, observed 2026-05-18T09:01:08.786299Z

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Observation 34f18947-982d-4cba-8676-798129c8540e · outbound

This paper cites Evolutionary-scale prediction of atomic level protein structure with a language model.

Evolutionary Profiles for Protein Fitness Prediction Evolutionary-scale prediction of atomic level protein structure with a language model

Reference 9

Resolution: metadata mismatch; doi, observed 2026-05-18T09:01:08.793279Z

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Observation 5b982290-edd9-4693-8e91-3acaab4ab98a · outbound

This paper cites Learning inverse folding from millions of predicted structures.

Evolutionary Profiles for Protein Fitness Prediction Learning inverse folding from millions of predicted structures

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.215277Z

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Observation e5a5ccca-3495-4be7-a66e-06801b39b047 · outbound

This paper cites Proteingym: 11 Large-scale benchmarks for protein fitness prediction and design.Advances in Neural Information Processing Systems, 36:64331–64379.

Evolutionary Profiles for Protein Fitness Prediction Proteingym: 11 Large-scale benchmarks for protein fitness prediction and design.Advances in Neural Information Processing Systems, 36:64331–64379

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.209097Z

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Observation 8d97c53e-1686-4446-9a1a-c1133fb5058f · outbound

This paper cites Retrieval augmented protein language models for protein structure prediction.

Evolutionary Profiles for Protein Fitness Prediction Retrieval augmented protein language models for protein structure prediction

Reference 12

Resolution: verified exact; doi, observed 2026-05-18T09:01:08.773053Z

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Observation 66c0cc73-fc32-44e3-bb65-62bb9367522e · outbound

This paper cites Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model.

Evolutionary Profiles for Protein Fitness Prediction Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:01:09.689258Z

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Observation eacfbdf7-c080-4c83-8a89-4bc2f986ecda · outbound

This paper cites Multiple sequence alignment.Current Opinion in Structural Biology, 16(3):368–373.

Evolutionary Profiles for Protein Fitness Prediction Multiple sequence alignment.Current Opinion in Structural Biology, 16(3):368–373

Reference 14

Resolution: verified exact; doi, observed 2026-05-18T09:01:08.816236Z

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Observation 9ec55ab6-82aa-45cd-a310-8704043b76aa · outbound

This paper cites Esm-if1: Structure-informed protein language model for inverse folding.

Evolutionary Profiles for Protein Fitness Prediction Esm-if1: Structure-informed protein language model for inverse folding

Reference 15

Resolution: verified exact; doi, observed 2026-05-18T09:01:08.805097Z

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Observation 20382257-1e2a-4519-8bee-9b0e10ecfeb3 · outbound

This paper cites Algorithms for inverse reinforcement learning.

Evolutionary Profiles for Protein Fitness Prediction Algorithms for inverse reinforcement learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.197703Z

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Observation baea7af9-a59a-48dc-98ee-612c02528796 · outbound

This paper cites Ziebart, Andrew Maas, J.

Evolutionary Profiles for Protein Fitness Prediction Ziebart, Andrew Maas, J

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.200923Z

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Observation 915bb0eb-8699-40ef-829f-9bc872f7c97e · outbound

This paper cites Fast and accurate protein structure search with foldseek.Nature biotechnology, 42(2):243–246.

Evolutionary Profiles for Protein Fitness Prediction Fast and accurate protein structure search with foldseek.Nature biotechnology, 42(2):243–246

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.161706Z

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Observation 0b9e1f73-52ea-416f-979f-a660a2bbf53c · outbound

This paper cites R., Bruun, T.

Evolutionary Profiles for Protein Fitness Prediction R., Bruun, T

Reference 19

Resolution: verified exact; doi, observed 2026-05-18T09:01:08.777818Z

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Observation 78de7896-c85d-4f99-8866-ab82ba4c1358 · outbound

This paper cites Advancing protein evolution with inverse folding models integrating structural and evolutionary constraints.Cell, 188(17):4674–4692.e19.

Evolutionary Profiles for Protein Fitness Prediction Advancing protein evolution with inverse folding models integrating structural and evolutionary constraints.Cell, 188(17):4674–4692.e19

Reference 20

Resolution: verified exact; doi, observed 2026-05-18T09:01:08.796816Z

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Observation ab0107bc-9288-4c28-9c2f-9996da217a94 · outbound

This paper cites Deep mutational scanning: a new style of protein science.Nature Methods.

Evolutionary Profiles for Protein Fitness Prediction Deep mutational scanning: a new style of protein science.Nature Methods

Reference 21

Resolution: verified exact; doi, observed 2026-05-18T09:01:08.808695Z

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Observation ddcffeca-e861-4453-bec6-a77f241b0f92 · outbound

This paper cites Semantical and geometrical protein encoding toward enhanced bioactivity and thermostability.Elife, 13:RP98033.

Evolutionary Profiles for Protein Fitness Prediction Semantical and geometrical protein encoding toward enhanced bioactivity and thermostability.Elife, 13:RP98033

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.165030Z

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Observation 14b26bd8-a65a-49c1-989c-a05447a8fe60 · outbound

This paper cites Saprot: Protein language modeling with structure-aware vocabulary.BioRxiv, pages 2023–10.

Evolutionary Profiles for Protein Fitness Prediction Saprot: Protein language modeling with structure-aware vocabulary.BioRxiv, pages 2023–10

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.168301Z

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Observation cd2ed7e3-20d4-43e4-bcba-432dd7db75bf · outbound

This paper cites ProSST: Protein language modeling with quantized structure and disentangled attention.

Evolutionary Profiles for Protein Fitness Prediction ProSST: Protein language modeling with quantized structure and disentangled attention

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.190880Z

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This paper cites an unresolved cited work.

Evolutionary Profiles for Protein Fitness Prediction Unresolved cited work

Reference 25

Resolution: verified exact; doi, observed 2026-05-18T09:01:08.789416Z

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Observation 3f3f612f-ce5d-48fb-8c55-3d73c57814ce · outbound

This paper cites Diffusion language models are versatile protein learners.

Evolutionary Profiles for Protein Fitness Prediction Diffusion language models are versatile protein learners

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.211936Z

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Observation 0bd3e8a1-442f-44b2-91b0-9d5f048e8db3 · outbound

This paper cites Language models enable zero-shot prediction of the effects of mutations on protein function.

Evolutionary Profiles for Protein Fitness Prediction Language models enable zero-shot prediction of the effects of mutations on protein function

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.218485Z

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Observation a3eaef52-1b05-404e-b9b9-efbb4791c057 · outbound

This paper cites Epistasis in protein evolution.Protein science, 25(7):1204–1218.

Evolutionary Profiles for Protein Fitness Prediction Epistasis in protein evolution.Protein science, 25(7):1204–1218

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.227303Z

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Observation 223f83e9-4683-4014-97c6-60343a8d22b9 · outbound

This paper cites Deep Think with Confidence.

Evolutionary Profiles for Protein Fitness Prediction Deep Think with Confidence

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:01:09.649282Z

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Observation c0bb8668-c87c-40b2-aadf-56c56daf823f · outbound

This paper cites Deep Researcher with Test-Time Diffusion.

Evolutionary Profiles for Protein Fitness Prediction Deep Researcher with Test-Time Diffusion

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:01:09.654643Z

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Observation e8a351ff-d8bf-4700-b04a-c38a8979cdc8 · outbound

This paper cites ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute.

Evolutionary Profiles for Protein Fitness Prediction ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:01:09.658411Z

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Observation a6e26b5f-d64a-4902-a3dc-db33497432dc · outbound

This paper cites The Majority is not always right: RL training for solution aggregation.

Evolutionary Profiles for Protein Fitness Prediction The Majority is not always right: RL training for solution aggregation

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:01:09.671919Z

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Observation f23c4df2-5c56-49cd-a9aa-b1958a937555 · outbound

This paper cites Learning from Protein Structure with Geometric Vector Perceptrons.

Evolutionary Profiles for Protein Fitness Prediction Learning from Protein Structure with Geometric Vector Perceptrons

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:01:09.680028Z

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Observation 1a4c5b36-3308-4867-91a8-c4029cb4e486 · outbound

This paper cites Steering protein family design through profile bayesian flow.

Evolutionary Profiles for Protein Fitness Prediction Steering protein family design through profile bayesian flow

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.147953Z

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Observation 26070dbb-ba38-4ff5-944e-5f1154be9070 · outbound

This paper cites an unresolved cited work.

Evolutionary Profiles for Protein Fitness Prediction Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:02:32.221331Z

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Observation e712a2e4-8307-4669-b48b-963a60d99545 · outbound

This paper cites Qu, W., Ma, Y ., Ye, F., Lu, C., Zhou, Y ., Zhang, K., Wang, L., Gui, M., and Gu, Q.

Evolutionary Profiles for Protein Fitness Prediction Qu, W., Ma, Y ., Ye, F., Lu, C., Zhou, Y ., Zhang, K., Wang, L., Gui, M., and Gu, Q

Reference 36

Resolution: metadata mismatch; doi, observed 2026-05-18T09:01:08.812561Z

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Observation a70a2600-2adf-4453-b1c4-d3cd4dd15045 · outbound

This paper cites AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model.

Evolutionary Profiles for Protein Fitness Prediction AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-06-09T03:07:01.766987Z

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Observation b67d4805-7f4b-44c2-adf1-850699311f61 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Evolutionary Profiles for Protein Fitness Prediction BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:01:09.667450Z

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Observation e53949e5-37ec-40e3-a90c-6b87ecce7415 · outbound

This paper cites Cath: increased structural coverage of functional space.Nucleic acids research, 49(D1): D266–D273.

Evolutionary Profiles for Protein Fitness Prediction Cath: increased structural coverage of functional space.Nucleic acids research, 49(D1): D266–D273

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.158313Z

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Observation 9e05a98b-3028-4d0a-a717-273b66bb5c07 · outbound

This paper cites Gemme: a simple and fast global epistatic model predicting mutational effects.Molecular biology and evolution, 36 (11):2604–2619.

Evolutionary Profiles for Protein Fitness Prediction Gemme: a simple and fast global epistatic model predicting mutational effects.Molecular biology and evolution, 36 (11):2604–2619

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.151607Z

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Observation 382b7052-cba3-47c9-9771-aec65a478ffc · outbound

This paper cites Progen2: exploring the boundaries of protein language models.Cell systems, 14(11):968–978.

Evolutionary Profiles for Protein Fitness Prediction Progen2: exploring the boundaries of protein language models.Cell systems, 14(11):968–978

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.224227Z

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Observation 25694cf7-95ba-4879-8291-48534370dff0 · outbound

This paper cites Convolutions are competitive with transformers for protein sequence pretraining.Cell Systems, 15(3):286–294.

Evolutionary Profiles for Protein Fitness Prediction Convolutions are competitive with transformers for protein sequence pretraining.Cell Systems, 15(3):286–294

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.122471Z

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Observation af6a13ff-1661-45ce-b1b3-57db30866555 · outbound

This paper cites Disease variant prediction with deep generative models of evolutionary data.Nature, 599(7883):91–95.

Evolutionary Profiles for Protein Fitness Prediction Disease variant prediction with deep generative models of evolutionary data.Nature, 599(7883):91–95

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.125904Z

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Observation f6048021-09a7-4e8d-95e9-80e68bad67fc · outbound

This paper cites Msa transformer.

Evolutionary Profiles for Protein Fitness Prediction Msa transformer

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.154939Z

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Observation 49c0f971-2f17-4799-830c-807a8cd4d5b6 · outbound

This paper cites Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval.

Evolutionary Profiles for Protein Fitness Prediction Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.171598Z

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Observation 3e534e18-7f72-40ca-babf-b7a138922cfc · outbound

This paper cites Trancepteve: Combining family-specific and family-agnostic models of protein sequences for improved fitness prediction.bioRxiv, pages 2022–12.

Evolutionary Profiles for Protein Fitness Prediction Trancepteve: Combining family-specific and family-agnostic models of protein sequences for improved fitness prediction.bioRxiv, pages 2022–12

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.144152Z

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Observation 2227a31d-866d-4c3e-90b0-8f7d4c9325bd · outbound

This paper cites Robust deep learning–based protein sequence design using proteinmpnn.Science, 378 (6615):49–56.

Evolutionary Profiles for Protein Fitness Prediction Robust deep learning–based protein sequence design using proteinmpnn.Science, 378 (6615):49–56

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.174689Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 66773503-748b-4232-8933-3329259f8521 · outbound

This paper cites Masked inverse folding with sequence transfer for protein representation learning.Protein Engineering, Design and Selection, 36:gzad015.

Evolutionary Profiles for Protein Fitness Prediction Masked inverse folding with sequence transfer for protein representation learning.Protein Engineering, Design and Selection, 36:gzad015

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.140154Z

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Observation bb1068c0-2103-4bf5-bc16-e40528116cc3 · outbound

This paper cites Deep generative models of genetic variation capture the effects of mutations.Nature methods, 15(10):816–822.

Evolutionary Profiles for Protein Fitness Prediction Deep generative models of genetic variation capture the effects of mutations.Nature methods, 15(10):816–822

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.136427Z

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Observation 1cf29f73-86aa-4448-a883-1196cb6971eb · outbound

This paper cites Evolutionary-scaleprediction of atomic-level protein structure with a language model.Science, 379(6637):1123–1130.

Evolutionary Profiles for Protein Fitness Prediction Evolutionary-scaleprediction of atomic-level protein structure with a language model.Science, 379(6637):1123–1130

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:02:32.132486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b202a901-a872-4140-81f3-f3b9e9cc3410 · outbound

This paper cites Muon is Scalable for LLM Training.

Evolutionary Profiles for Protein Fitness Prediction Muon is Scalable for LLM Training

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:01:09.675552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7ac52364-46eb-495e-a444-6c22c8898a72 · outbound

This paper cites Decoupled Weight Decay Regularization.

Evolutionary Profiles for Protein Fitness Prediction Decoupled Weight Decay Regularization

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:01:09.684478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e1543698-35fa-4bbf-8e5a-415b4ab34763 · outbound

This paper cites Alphafold protein structure database in 2024: providing structure coverage for over 214 million protein se- quences.Nucleic Acids Research, 52(D1):D368–D375.

Evolutionary Profiles for Protein Fitness Prediction Alphafold protein structure database in 2024: providing structure coverage for over 214 million protein se- quences.Nucleic Acids Research, 52(D1):D368–D375

Reference 53

Resolution: verified exact; doi, observed 2026-05-18T09:01:08.801385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 599b5fab-7ae3-460e-8539-7b34816a41dc · outbound

This paper cites an unresolved cited work.

Evolutionary Profiles for Protein Fitness Prediction Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-05-18T09:02:32.129249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Outbound references

Pith citing papers