Foundation Models for AI-Enabled Biological Design | Paper Citation Record

Observation 5d9476c3-071d-48a8-a081-2f6d05078fc9 · outbound

This paper cites National Institute of Gen- eral Medical Sciences, U.S.

Foundation Models for AI-Enabled Biological Design National Institute of Gen- eral Medical Sciences, U.S

Reference 1

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Observation 9d8a89ee-cd51-48a5-a700-6c1c71ac8a79 · outbound

This paper cites Collins and Leslie Fink.

Foundation Models for AI-Enabled Biological Design Collins and Leslie Fink

Reference 2

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Observation c5abd4d9-d129-41be-81d2-b642ed99d952 · outbound

This paper cites an unresolved cited work.

Foundation Models for AI-Enabled Biological Design Unresolved cited work

Reference 3

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Observation 3615b171-4a04-4214-a2a4-c22e0ca2697a · outbound

This paper cites The structural genomics consortium: a knowledge platform for drug discovery: a summary.

Foundation Models for AI-Enabled Biological Design The structural genomics consortium: a knowledge platform for drug discovery: a summary

Reference 4

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Observation 460a507e-44c2-4693-8cd8-894f835f3c92 · outbound

This paper cites Protein data bank.Nature New Biol, 233(223):10–1038, 1971.

Foundation Models for AI-Enabled Biological Design Protein data bank.Nature New Biol, 233(223):10–1038, 1971

Reference 5

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Observation ffb037b6-7add-4304-a4d9-5021e43f2272 · outbound

This paper cites Highly accurate protein structure prediction with alphafold.nature, 596(7873):583–589, 2021.

Foundation Models for AI-Enabled Biological Design Highly accurate protein structure prediction with alphafold.nature, 596(7873):583–589, 2021

Reference 6

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Observation e9fb532d-07c1-44ba-8636-9971ee8885c1 · outbound

This paper cites The role of ai in drug discovery: chal- lenges, opportunities, and strategies.Pharmaceuticals, 16(6):891, 2023.

Foundation Models for AI-Enabled Biological Design The role of ai in drug discovery: chal- lenges, opportunities, and strategies.Pharmaceuticals, 16(6):891, 2023

Reference 7

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Observation 6e9fffda-b671-4634-a33b-d6f75cbff5db · outbound

This paper cites Synthetic biology 2020–2030: six commercially-available products that are changing our world.Nature Communications, 11(1):1–6, 2020.

Foundation Models for AI-Enabled Biological Design Synthetic biology 2020–2030: six commercially-available products that are changing our world.Nature Communications, 11(1):1–6, 2020

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:25.146433Z

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Observation 862f2a4b-964f-480c-b076-625edf4aa3ac · outbound

This paper cites Materials design by synthetic biol- ogy.Nature Reviews Materials, 6(4):332–350, 2021.

Foundation Models for AI-Enabled Biological Design Materials design by synthetic biol- ogy.Nature Reviews Materials, 6(4):332–350, 2021

Reference 9

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Observation 6abcb241-1ba0-4fa2-ad5f-db9fdcf89a90 · outbound

This paper cites On the opportunities and risks of foundation models.arXiv e-prints, pages arXiv–2108, 2021.

Foundation Models for AI-Enabled Biological Design On the opportunities and risks of foundation models.arXiv e-prints, pages arXiv–2108, 2021

Reference 10

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Observation 34923e04-b289-40b4-b346-167402d5fb82 · outbound

This paper cites Attention Is All You Need.

Foundation Models for AI-Enabled Biological Design Attention Is All You Need

Reference 11

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Observation 548726c4-fa43-487b-889c-0255d3cde923 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Foundation Models for AI-Enabled Biological Design Efficiently Modeling Long Sequences with Structured State Spaces

Reference 12

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Observation 163eb2d1-f550-4f67-82ad-6abdea992bf8 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Foundation Models for AI-Enabled Biological Design Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 13

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Observation 6ea4ff69-483f-471a-b901-e1d4325beae0 · outbound

This paper cites Diffusion-lm im- proves controllable text generation.Advances in Neu- ral Information Processing Systems, 35:4328–4343, 2022.

Foundation Models for AI-Enabled Biological Design Diffusion-lm im- proves controllable text generation.Advances in Neu- ral Information Processing Systems, 35:4328–4343, 2022

Reference 14

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Observation c660c3f1-a10e-4e8e-ae4c-10ff85920269 · outbound

This paper cites Argmax flows and multinomial diffusion: Learning categorical distribu- tions.Advances in Neural Information Processing Sys- tems, 34:12454–12465, 2021.

Foundation Models for AI-Enabled Biological Design Argmax flows and multinomial diffusion: Learning categorical distribu- tions.Advances in Neural Information Processing Sys- tems, 34:12454–12465, 2021

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:25.062438Z

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Observation e666f195-f243-4fc9-b4ab-f298bc6ae57f · outbound

This paper cites Autoregressive diffusion models.

Foundation Models for AI-Enabled Biological Design Autoregressive diffusion models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:25.037037Z

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Observation f96c92e1-6d62-49ab-b067-c8af50f79c5d · outbound

This paper cites Molgpt: molecular generation using a transformer-decoder model.Journal of Chemical In- formation and Modeling, 62(9):2064–2076, 2021.

Foundation Models for AI-Enabled Biological Design Molgpt: molecular generation using a transformer-decoder model.Journal of Chemical In- formation and Modeling, 62(9):2064–2076, 2021

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:25.010729Z

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Observation 4769509f-1b76-4f33-b214-87072064ac46 · outbound

This paper cites Multi- constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning.Nature Machine Intelligence, 3(10):914–922, 2021.

Foundation Models for AI-Enabled Biological Design Multi- constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning.Nature Machine Intelligence, 3(10):914–922, 2021

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.985380Z

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Observation 69d2028a-1728-4b9d-9286-e9c31d0276c2 · outbound

This paper cites Molecule generation using transformers and policy gradient reinforcement learning.Scientific Re- ports, 13(1):8799, 2023.

Foundation Models for AI-Enabled Biological Design Molecule generation using transformers and policy gradient reinforcement learning.Scientific Re- ports, 13(1):8799, 2023

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.961782Z

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Observation 7fcf42b9-26b8-4901-9451-2337c9923eb1 · outbound

This paper cites Regression trans- former enables concurrent sequence regression and generation for molecular language modelling.Nature Machine Intelligence, 5(4):432–444, 2023.

Foundation Models for AI-Enabled Biological Design Regression trans- former enables concurrent sequence regression and generation for molecular language modelling.Nature Machine Intelligence, 5(4):432–444, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.938117Z

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Observation d0c78860-a011-4263-888c-0bdd6da9fa45 · outbound

This paper cites Large language models generate func- tional protein sequences across diverse families.Na- ture Biotechnology, 41(8):1099–1106, 2023.

Foundation Models for AI-Enabled Biological Design Large language models generate func- tional protein sequences across diverse families.Na- ture Biotechnology, 41(8):1099–1106, 2023

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.903653Z

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Observation a269ed69-3c18-4358-ab17-d0f83f8fddfb · outbound

This paper cites Progen2: exploring the boundaries of protein language models.Cell systems, 14(11):968–978, 2023.

Foundation Models for AI-Enabled Biological Design Progen2: exploring the boundaries of protein language models.Cell systems, 14(11):968–978, 2023

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T20:54:23.605655Z

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Observation 92586758-c95f-40e2-8311-ce44f1a5a5a5 · outbound

This paper cites Protgpt2 is a deep unsupervised language model for protein design.Nature communications, 13(1):4348, 2022.

Foundation Models for AI-Enabled Biological Design Protgpt2 is a deep unsupervised language model for protein design.Nature communications, 13(1):4348, 2022

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.867214Z

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Observation 39f91937-4edf-412a-b663-efc1442b5177 · outbound

This paper cites Generalized biomolecular modeling and design with rosettafold all- atom.Science, 384(6693):eadl2528, 2024.

Foundation Models for AI-Enabled Biological Design Generalized biomolecular modeling and design with rosettafold all- atom.Science, 384(6693):eadl2528, 2024

Reference 24

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Observation 15d147e1-8ac9-4da0-92c1-001432fc1897 · outbound

This paper cites Protein design with guided discrete diffusion.

Foundation Models for AI-Enabled Biological Design Protein design with guided discrete diffusion

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.830027Z

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Observation 4ae0e813-c481-4d5b-9792-ebdf0752b525 · outbound

This paper cites Sequence modeling and design from molecular to genome scale with evo.Science, 2024.

Foundation Models for AI-Enabled Biological Design Sequence modeling and design from molecular to genome scale with evo.Science, 2024

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.806283Z

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Observation 7323abef-2ecc-41e8-a917-ae14185c6345 · outbound

This paper cites Chemical language modeling with structured state space sequence models.Nature Communications, 15(1):6176, 2024.

Foundation Models for AI-Enabled Biological Design Chemical language modeling with structured state space sequence models.Nature Communications, 15(1):6176, 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.783254Z

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Observation d6671082-c53a-4ddc-89ad-b62d1da05766 · outbound

This paper cites Protmamba: a homology-aware but alignment-free protein state space model.bioRxiv, pages 2024–05, 2024.

Foundation Models for AI-Enabled Biological Design Protmamba: a homology-aware but alignment-free protein state space model.bioRxiv, pages 2024–05, 2024

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.758851Z

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Observation 24f8ddcd-8a8d-44cb-b082-fe8e3eeb3ea5 · outbound

This paper cites reglm: Designing realistic regu- latory dna with autoregressive language models.

Foundation Models for AI-Enabled Biological Design reglm: Designing realistic regu- latory dna with autoregressive language models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.735932Z

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Observation e04401fe-ecb4-421d-8bfb-92b7a4ab6aee · outbound

This paper cites Discdiff: Latent diffusion model for dna sequence gen- eration.CoRR, 2024.

Foundation Models for AI-Enabled Biological Design Discdiff: Latent diffusion model for dna sequence gen- eration.CoRR, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.714311Z

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Observation 903e5f2f-8ae6-4b08-b36a-545800cd14cd · outbound

This paper cites Dna-diffusion: Leveraging generative models for controlling chro- matin accessibility and gene expression via synthetic regulatory elements.

Foundation Models for AI-Enabled Biological Design Dna-diffusion: Leveraging generative models for controlling chro- matin accessibility and gene expression via synthetic regulatory elements

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.694953Z

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Observation 6707e6bf-0eab-4e19-8a04-e41c969e744b · outbound

This paper cites Hitting stride by degrees: Fine grained molecular generation via diffusion model.

Foundation Models for AI-Enabled Biological Design Hitting stride by degrees: Fine grained molecular generation via diffusion model

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.675997Z

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Observation 28d64d84-800e-4ee0-905c-81a324f233b9 · outbound

This paper cites Protein generation with evolutionary diffusion: se- quence is all you need.

Foundation Models for AI-Enabled Biological Design Protein generation with evolutionary diffusion: se- quence is all you need

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.647610Z

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Observation 4ecea4e3-e246-413d-9234-0b714f67a7ea · outbound

This paper cites Towards joint sequence-structure gen- eration of nucleic acid and protein complexes with se (3)-discrete diffusion.

Foundation Models for AI-Enabled Biological Design Towards joint sequence-structure gen- eration of nucleic acid and protein complexes with se (3)-discrete diffusion

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.625598Z

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Observation f101e9a7-fd7a-4d8d-b707-caaa668c36ac · outbound

This paper cites Model-based reinforcement learning for biological se- quence design.

Foundation Models for AI-Enabled Biological Design Model-based reinforcement learning for biological se- quence design

Reference 35

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Observation fe5894fc-b26a-44f9-94b0-033c1a960cb6 · outbound

This paper cites nach0: Multimodal natural and chemical languages foundation model.

Foundation Models for AI-Enabled Biological Design nach0: Multimodal natural and chemical languages foundation model

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.581079Z

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Observation 01cf6b41-f454-4d84-9936-841ea4aee8e7 · outbound

This paper cites Advancing biomolecular understanding and design following human instructions.

Foundation Models for AI-Enabled Biological Design Advancing biomolecular understanding and design following human instructions

Reference 37

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Observation 29979bb4-8de5-4b0e-914c-97aed6db5f65 · outbound

This paper cites Chatnt: A mul- timodal conversational agent for dna, rna and protein tasks.bioRxiv, pages 2024–04, 2024.

Foundation Models for AI-Enabled Biological Design Chatnt: A mul- timodal conversational agent for dna, rna and protein tasks.bioRxiv, pages 2024–04, 2024

Reference 38

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Observation a790ca7b-bd8b-4acd-b663-433b8d70278f · outbound

This paper cites Roformer: Enhanced trans- former with rotary position embedding.Neurocomput- ing, 568:127063, 2024.

Foundation Models for AI-Enabled Biological Design Roformer: Enhanced trans- former with rotary position embedding.Neurocomput- ing, 568:127063, 2024

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.534320Z

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Observation d862830d-428e-46eb-86d6-b595ceec0761 · outbound

This paper cites Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution.Ad- vances in neural information processing systems, 36, 2024.

Foundation Models for AI-Enabled Biological Design Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution.Ad- vances in neural information processing systems, 36, 2024

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.509672Z

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Observation 0adcaca1-aad4-4eda-b995-38c57e86aee0 · outbound

This paper cites XLNet: Generalized Autoregressive Pretraining for Language Understanding.

Foundation Models for AI-Enabled Biological Design XLNet: Generalized Autoregressive Pretraining for Language Understanding

Reference 41

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Observation 9d64f5d2-9f47-4493-9e34-1dd6f3a90807 · outbound

This paper cites Generative molecular design in low data regimes.Nature Machine Intelligence, 2(3):171–180, 2020.

Foundation Models for AI-Enabled Biological Design Generative molecular design in low data regimes.Nature Machine Intelligence, 2(3):171–180, 2020

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.490522Z

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Observation 87a479d1-c4e4-4459-b5b4-edca43a29efa · outbound

This paper cites Hyena hierar- chy: Towards larger convolutional language models.

Foundation Models for AI-Enabled Biological Design Hyena hierar- chy: Towards larger convolutional language models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.472208Z

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Observation bfa811ec-7306-4481-b435-48d4e5225fe3 · outbound

This paper cites Protein-Mamba: Biological Mamba Models for Protein Function Prediction.

Foundation Models for AI-Enabled Biological Design Protein-Mamba: Biological Mamba Models for Protein Function Prediction

Reference 44

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Observation 89a9e906-6cbb-4fce-999a-9e97965b3708 · outbound

This paper cites Ptm-mamba: A ptm-aware protein lan- guage model with bidirectional gated mamba blocks.

Foundation Models for AI-Enabled Biological Design Ptm-mamba: A ptm-aware protein lan- guage model with bidirectional gated mamba blocks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.453415Z

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Observation 0d84855e-da66-4d26-b2f1-cacd7fdc7fed · outbound

This paper cites Efficient Training of Language Models to Fill in the Middle.

Foundation Models for AI-Enabled Biological Design Efficient Training of Language Models to Fill in the Middle

Reference 46

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Observation 2a1215de-d33a-4002-8763-81b83db986f6 · outbound

This paper cites Genomic Language Models: Opportunities and Challenges.

Foundation Models for AI-Enabled Biological Design Genomic Language Models: Opportunities and Challenges

Reference 47

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This paper cites Hungry hungry hippos: Towards language modeling with state space models.

Foundation Models for AI-Enabled Biological Design Hungry hungry hippos: Towards language modeling with state space models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.433821Z

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Observation 61788a3b-22ef-44e9-a7c3-1c89d1b43747 · outbound

This paper cites Zero-shot text-to-image generation.

Foundation Models for AI-Enabled Biological Design Zero-shot text-to-image generation

Reference 49

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This paper cites Simple statistical gradient- following algorithms for connectionist reinforcement learning.Machine learning, 8:229–256, 1992.

Foundation Models for AI-Enabled Biological Design Simple statistical gradient- following algorithms for connectionist reinforcement learning.Machine learning, 8:229–256, 1992

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.389043Z

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Observation 68a71a29-ac64-48f3-abb5-657f46fdafd1 · outbound

This paper cites Evaluating protein transfer learning with tape.Advances in neural information processing sys- tems, 32, 2019.

Foundation Models for AI-Enabled Biological Design Evaluating protein transfer learning with tape.Advances in neural information processing sys- tems, 32, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.368455Z

Source-reported events for the cited work

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Observation cc69523e-65a8-40ed-a183-8e0b236cb3ba · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.Jour- nal of machine learning research, 21(140):1–67, 2020.

Foundation Models for AI-Enabled Biological Design Exploring the limits of transfer learning with a unified text-to-text transformer.Jour- nal of machine learning research, 21(140):1–67, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.348613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c6129bba-11e5-4417-bb43-f193ea78b1a0 · outbound

This paper cites Language Models are Few-Shot Learners.

Foundation Models for AI-Enabled Biological Design Language Models are Few-Shot Learners

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T20:54:23.778239Z

Source-reported events for the cited work

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Observation 0da9b407-5ea8-4c95-98c2-9ba46d9ff34a · outbound

This paper cites A theory of biological relativity: no priv- ileged level of causation.Interface focus, 2(1):55–64, 2012.

Foundation Models for AI-Enabled Biological Design A theory of biological relativity: no priv- ileged level of causation.Interface focus, 2(1):55–64, 2012

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.327981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 987a7621-9a3d-4e9f-b78d-a89d72764c89 · outbound

This paper cites It’s time to admit that genes are not the blueprint for life.Nature, 626(7998):254–255, 2024.

Foundation Models for AI-Enabled Biological Design It’s time to admit that genes are not the blueprint for life.Nature, 626(7998):254–255, 2024

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.308773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0850d0a4-1b47-4ba6-9d0d-a13226ebf932 · outbound

This paper cites Springer Nature, 2023.

Foundation Models for AI-Enabled Biological Design Springer Nature, 2023

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.290328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 82c682e8-2fc9-478f-a2c4-075618cda2dd · outbound

This paper cites The omg dataset: An open metagenomic corpus for mixed- modality genomic language modeling.bioRxiv, pages 2024–08, 2024.

Foundation Models for AI-Enabled Biological Design The omg dataset: An open metagenomic corpus for mixed- modality genomic language modeling.bioRxiv, pages 2024–08, 2024

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.270095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7b97911b-7918-4bc6-90c8-1348d2bfda9e · outbound

This paper cites Effi- cient and accurate prediction of protein structure using rosettafold2.BioRxiv, pages 2023–05, 2023.

Foundation Models for AI-Enabled Biological Design Effi- cient and accurate prediction of protein structure using rosettafold2.BioRxiv, pages 2023–05, 2023

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.246516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c988fa9e-74ed-4022-8d4d-50fd9ffc0197 · outbound

This paper cites Gene ontology: tool for the unification of biology.Nature genetics, 25(1):25–29, 2000.

Foundation Models for AI-Enabled Biological Design Gene ontology: tool for the unification of biology.Nature genetics, 25(1):25–29, 2000

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.220860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d3a73b40-bd85-4f7d-bfd6-2bc85277341b · outbound

This paper cites The nucleotide transformer: Building and evaluating robust foundation models for human ge- nomics.BioRxiv, pages 2023–01, 2023.

Foundation Models for AI-Enabled Biological Design The nucleotide transformer: Building and evaluating robust foundation models for human ge- nomics.BioRxiv, pages 2023–01, 2023

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.200121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9a3669da-472d-4068-80d2-0b67b5df1025 · outbound

This paper cites Scaling Laws for Neural Language Models.

Foundation Models for AI-Enabled Biological Design Scaling Laws for Neural Language Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T20:54:23.825858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1a9567e-df38-44dc-b722-b4dd51c5332a · outbound

This paper cites Explaining neural scaling laws.Proceedings of the National Academy of Sci- ences, 121(27):e2311878121, 2024.

Foundation Models for AI-Enabled Biological Design Explaining neural scaling laws.Proceedings of the National Academy of Sci- ences, 121(27):e2311878121, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.178707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cf9a54e0-d279-4c4e-8ef7-820b916afd28 · outbound

This paper cites Biolog- ical structure and function emerge from scaling un- supervised learning to 250 million protein sequences.

Foundation Models for AI-Enabled Biological Design Biolog- ical structure and function emerge from scaling un- supervised learning to 250 million protein sequences

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.160202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e1e8cc74-c81d-4864-9c36-3b324239c340 · outbound

This paper cites Neural scaling of deep chemical models.Nature Machine Intelligence, 5(11):1297–1305, 2023.

Foundation Models for AI-Enabled Biological Design Neural scaling of deep chemical models.Nature Machine Intelligence, 5(11):1297–1305, 2023

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.142128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c6849290-8a9c-4671-96d0-ad3e88a3070f · outbound

This paper cites Molca: Molecular graph-language modeling with cross-modal projector and uni-modal adapter.

Foundation Models for AI-Enabled Biological Design Molca: Molecular graph-language modeling with cross-modal projector and uni-modal adapter

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.123465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7d6e7e79-6e06-49ef-a01f-292c298f4e31 · outbound

This paper cites Foundation models for scientific discovery and innovation: Opportunities across the department of energy, 2024.

Foundation Models for AI-Enabled Biological Design Foundation models for scientific discovery and innovation: Opportunities across the department of energy, 2024

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:54:24.104157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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