{"entities":[{"id":"deepmind","type":"organisation"},{"id":"alphafold-account","name":"AlphaFold — CASP14 research account (2020)","type":"document"},{"id":"arxiv","type":"organisation"},{"id":"aib-doc-esm3-release-2024","name":"ESM3: Simulating 500 million years of evolution with a language model","type":"document"},{"id":"aib-doc-exscientia-prospectus-2021","name":"Exscientia plc IPO prospectus (424B4)","type":"document"},{"id":"aib-doc-insilico-rentosertib-ph3-initiation-2026","name":"Insilico Medicine: Initiation of Rentosertib Phase III Clinical Trial","type":"document"},{"id":"aib-doc-iso-launch-2021","name":"Introducing Isomorphic Labs","type":"document"},{"id":"aib-doc-rfdiffusion-2023","name":"De novo design of protein structure and function with RFdiffusion","type":"document"},{"id":"aib-evolutionaryscale","type":"organisation"},{"id":"aib-exscientia","type":"organisation"},{"id":"aib-isomorphic-labs","type":"organisation"},{"id":"aib-doc-ahlmann-eltze-2025","name":"Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines","type":"document"},{"id":"aib-doc-arnold-2023","name":"AlphaFold touted as next big thing for drug discovery — but is it?","type":"document"},{"id":"aib-doc-berg-bpm31510-2013","name":"Berg Launches First-Ever Stratified Medicine Clinical Trial To Investigate Cancer Metabolism Of Solid Tumors","type":"document"},{"id":"aib-doc-blundell-1987","name":"Knowledge-based prediction of protein structures and the design of novel molecules","type":"document"},{"id":"aib-doc-bray-1990","name":"Intracellular signalling as a parallel distributed process","type":"document"},{"id":"aib-doc-bunne-2024","name":"How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities","type":"document"},{"id":"aib-doc-hallucination-preprint-2020","name":"De novo protein design by deep network hallucination","type":"document"},{"id":"aib-doc-hood-galas-2003","name":"The digital code of DNA","type":"document"},{"id":"aib-doc-huang-2016","name":"The coming of age of de novo protein design","type":"document"},{"id":"aib-doc-jayatunga-2024","name":"How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons","type":"document"},{"id":"aib-doc-lyu-2024","name":"AlphaFold2 structures guide prospective ligand discovery","type":"document"},{"id":"aib-doc-ma-2018","name":"Using deep learning to model the hierarchical structure and function of a cell","type":"document"},{"id":"aib-doc-mullard-2021","name":"What does AlphaFold mean for drug discovery?","type":"document"},{"id":"aib-doc-roohani-2025","name":"Virtual Cell Challenge: Toward a Turing test for the virtual cell","type":"document"},{"id":"aib-doc-sternberg-1992","name":"Modelling the structure and function of enzymes by machine learning","type":"document"},{"id":"aib-doc-sumitomo-dsp1181-2020","name":"Sumitomo Dainippon Pharma and Exscientia Joint Development New Drug Candidate Created Using Artificial Intelligence (AI) Begins Clinical Study","type":"document"},{"id":"aib-doc-terwilliger-2023","name":"AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination","type":"document"},{"id":"aib-doc-tomita-2001","name":"Whole-cell simulation: a grand challenge of the 21st century","type":"document"},{"id":"aib-doc-wang-2018","name":"Computational Protein Design with Deep Learning Neural Networks","type":"document"},{"id":"aib-doc-wong-2022","name":"Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery","type":"document"},{"id":"aib-doc-xu-2025","name":"A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial","type":"document"},{"id":"aib-prop-ai-drug-candidate-in-trials","name":"A drug candidate discovered or designed with AI has entered human clinical trials","type":"concept","proposition":{"code":"P3","criterion":"The text states that a named drug candidate, described as discovered, designed or created using AI, has begun testing in humans in a clinical trial.","terms":[]}},{"id":"aib-prop-ai-drugs-higher-trial-success","name":"AI-discovered drug candidates succeed in clinical trials more often than historical averages","type":"concept","proposition":{"code":"P6","criterion":"The text states that drug candidates discovered or designed with AI have passed at least one phase of clinical trials at a higher rate than historical industry averages.","terms":[]}},{"id":"aib-prop-ai-virtual-cell","name":"AI models can simulate or predict the behaviour of cells (a \"virtual cell\")","type":"concept","proposition":{"code":"P2","criterion":"The text states that a machine-learning or other AI model simulates, or will be able to simulate or predict, the behaviour of a living cell, such as its growth or its responses to perturbations.","terms":["virtual cell","AI Virtual Cell"]}},{"id":"aib-prop-biology-as-information-for-ai","name":"Biology is an information system that AI can read, describe or program","type":"concept","proposition":{"code":"P5","criterion":"The text states that biology is, or can be treated as, an information system, language or code, and that AI is, or may become, the means of reading, describing or programming it.","terms":["digital biology","programmable biology"],"withheld":[{"role":"earliest-located","reason":"Withheld until the criterion is decided (maintainer decision of 2026-10-30). The cross-family check located an earlier candidate, Searls (AAAI-88 proceedings, 1988), which treats genetic information as a knowledge representation for symbolic AI. Whether that meets the criterion’s AI clause is unsettled.","recordedAt":"2026-10-31"}]}},{"id":"aib-prop-ml-designs-new-proteins","name":"Machine-learning methods make proteins with new functions designable","type":"concept","proposition":{"code":"P4","criterion":"The text states that machine-learning models can be used, or will contribute, to designing proteins with functions, or sequences, not found among natural proteins.","terms":[],"withheld":[{"role":"earliest-located","reason":"Withheld until the criterion is decided (maintainer decision of 2026-10-30). The cross-family check located earlier candidates, Schneider and Wrede (1994) on sequence design assisted by neural networks and Greener, Moffat and Jones (arXiv, 26 June 2018) on sequences for a novel protein topology. Whether either meets the criterion’s “not found among natural proteins” is unsettled.","recordedAt":"2026-10-31"}]}},{"id":"aib-prop-predicted-structures-aid-drug-design","name":"Predicted protein structures aid drug design","type":"concept","proposition":{"code":"P1","criterion":"The text states that protein structures obtained by computational prediction, rather than by experiment, are or will be of use in designing or discovering drugs.","terms":["structure-based drug design"]}}],"claims":[{"id":"aib-arg-p1-blundell-1987","subject":"aib-doc-blundell-1987","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-predicted-structures-aid-drug-design"},"topic":"ideas","statement":"The Nature review \"Knowledge-based prediction of protein structures and the design of novel molecules\" (Blundell, Sibanda, Sternberg and Thornton, 26 March 1987) states that knowledge-based prediction techniques are of value in predicting receptor structures to aid the design of drugs.","time":{"kind":"event","date":"1987-03-26","basis":"publication","note":"Publication and issue date printed on the Nature article page."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p1-blundell-1987-1","evidence-aib-arg-p1-blundell-1987-2"],"attributes":{"thought":{"roles":["earliest-located"],"register":"nonfiction","qualifier":"Stated of knowledge-based (comparative) modelling, as a field of use; no drug is named."}}},{"id":"aib-arg-p1-sternberg-1992","subject":"aib-doc-sternberg-1992","predicate":"bears_on_proposition","object":{"kind":"entity","entityId":"aib-prop-predicted-structures-aid-drug-design"},"topic":"ideas","statement":"The Faraday Discussions paper \"Modelling the structure and function of enzymes by machine learning\" (Sternberg, Lewis, King and Muggleton, 1992) reports applying the machine-learning program GOLEM both to protein secondary-structure prediction and, separately, to modelling a structure-activity relationship in drug design.","time":{"kind":"event","date":"1992","basis":"publication","note":"Year of the Faraday Discussions issue (vol. 93) as given by Europe PMC; no finer date was retrieved."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p1-sternberg-1992-1"],"attributes":{"thought":{"roles":["context"],"register":"nonfiction","missing":"Does not state that predicted protein structures aid drug design: the structure-prediction and drug-design applications are separate, and the drug-design model rests on ligand activities, not on a predicted protein structure."}}},{"id":"aib-arg-p2-tomita-2001","subject":"aib-doc-tomita-2001","predicate":"bears_on_proposition","object":{"kind":"entity","entityId":"aib-prop-ai-virtual-cell"},"topic":"ideas","statement":"Tomita's article \"Whole-cell simulation: a grand challenge of the 21st century\" (Trends in Biotechnology, June 2001) states that the complex behaviour of the cell cannot be determined or predicted unless a computer model of the cell is constructed, and reports that the E-CELL project has completed \"a couple of\" virtual cell models.","time":{"kind":"event","date":"2001-06","basis":"publication","note":"Issue month as given by Europe PMC."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p2-tomita-2001-1"],"attributes":{"thought":{"roles":["context","earliest-located-term"],"register":"nonfiction","term":"virtual cell","missing":"No AI or machine-learning element: the virtual cell is a computer simulation built from biological data."}}},{"id":"aib-arg-p5-hood-galas-2003","subject":"aib-doc-hood-galas-2003","predicate":"bears_on_proposition","object":{"kind":"entity","entityId":"aib-prop-biology-as-information-for-ai"},"topic":"ideas","statement":"Hood and Galas (Nature, 23 January 2003) state that the discovery of the structure of DNA engendered a new view of biology as an information science.","time":{"kind":"event","date":"2003-01-23","basis":"publication","note":"Publication date printed on the article page."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p5-hood-galas-2003-1"],"attributes":{"thought":{"roles":["context"],"register":"nonfiction","missing":"No AI element: the information view is tied to DNA and genomics, not to AI as the means of reading or describing biology."}}},{"id":"aib-arg-p4-huang-2016","subject":"aib-doc-huang-2016","predicate":"bears_on_proposition","object":{"kind":"entity","entityId":"aib-prop-ml-designs-new-proteins"},"topic":"ideas","statement":"Huang, Boyken and Baker (Nature, published online 14 September 2016) state that computational methodology now allows a wide range of protein structures to be designed from scratch with atomic-level accuracy, and that it should now be possible to design new functional proteins from the ground up.","time":{"kind":"event","date":"2016-09-14","basis":"publication","note":"Online publication date printed on the article page."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p4-huang-2016-1","evidence-aib-arg-p4-huang-2016-2"],"attributes":{"thought":{"roles":["context"],"register":"nonfiction","missing":"No machine-learning element: the design methods are physics-based."}}},{"id":"aib-arg-p2-ma-2018","subject":"aib-doc-ma-2018","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-ai-virtual-cell"},"topic":"ideas","statement":"Ma et al. (Nature Methods, published online 5 March 2018) state that DCell, a \"visible\" neural network embedded in the hierarchical structure of the subsystems of a eukaryotic cell and trained on several million genotypes, simulates cellular growth nearly as accurately as laboratory observations.","time":{"kind":"event","date":"2018-03-05","basis":"publication","note":"Online publication date printed on the article page."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p2-ma-2018-1","evidence-aib-arg-p2-ma-2018-2"],"attributes":{"thought":{"roles":["articulation"],"register":"nonfiction","qualifier":"Stated of growth prediction from genotype in one eukaryotic cell model."}}},{"id":"aib-arg-p4-wang-2018","subject":"aib-doc-wang-2018","predicate":"bears_on_proposition","object":{"kind":"entity","entityId":"aib-prop-ml-designs-new-proteins"},"topic":"ideas","statement":"Wang, Cao, Zhang and Qi (Scientific Reports, 20 April 2018) report applying a deep-learning neural network to computational protein design and state that their results may benefit further development of computational protein design methods.","time":{"kind":"event","date":"2018-04-20","basis":"publication","note":"Publication date printed on the article page."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p4-wang-2018-1","evidence-aib-arg-p4-wang-2018-2"],"attributes":{"thought":{"roles":["context"],"register":"nonfiction","missing":"Applies machine learning to protein design but does not state that it makes proteins with new functions or unnatural sequences designable."}}},{"id":"aib-arg-p3-sumitomo-2020","subject":"aib-doc-sumitomo-dsp1181-2020","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-ai-drug-candidate-in-trials"},"topic":"ideas","statement":"A news release of Sumitomo Dainippon Pharma and Exscientia (30 January 2020) announces that a phase I clinical study of DSP-1181, which it says was created using artificial intelligence, has been initiated in Japan.","time":{"kind":"event","date":"2020-01-30","basis":"announcement","note":"Date printed on the release."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p3-sumitomo-2020-1","evidence-aib-arg-p3-sumitomo-2020-2"],"attributes":{"thought":{"roles":["articulation"],"register":"nonfiction","qualifier":"A company announcement of a phase I study start."}}},{"id":"aib-arg-p4-anishchenko-2020","subject":"aib-doc-hallucination-preprint-2020","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-ml-designs-new-proteins"},"topic":"ideas","statement":"The bioRxiv preprint \"De novo protein design by deep network hallucination\" (Anishchenko, Chidyausiku, Ovchinnikov, Pellock and Baker, v1 of 23 July 2020) states that deep networks trained to predict native protein structures can be inverted to design new proteins and should contribute to the de novo design of proteins with new functions.","time":{"kind":"event","date":"2020-07-23","basis":"publication","note":"Date of bioRxiv v1, from the bioRxiv details API. The journal version (Nature, published online 1 December 2021) carries the same concluding statement in edited wording."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p4-anishchenko-2020-1","evidence-aib-arg-p4-anishchenko-2020-2"],"attributes":{"thought":{"roles":["articulation"],"register":"nonfiction","qualifier":"An expectation for new functions, from a demonstration of new folded proteins."}}},{"id":"aib-arg-p1-deepmind-2020","subject":"alphafold-account","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-predicted-structures-aid-drug-design"},"topic":"ideas","statement":"DeepMind's account of AlphaFold at CASP14 (dated 30 November 2020) states that insights from protein structure predictions could enable more precise work on drug development, complementing existing experimental methods.","time":{"kind":"event","date":"2020-11-30","basis":"publication","note":"Date printed on the page. The quotations are those of the copy served on 2026-10-30, which carries a July 2022 update notice at its head; the cross-family check of 2026-10-30 found the passage and the header date of 30 November 2020 in the Internet Archive capture of 2 December 2020, recorded as a source of its own."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p1-deepmind-2020-1","evidence-aib-arg-p1-deepmind-2020-2","evidence-aib-arg-p1-deepmind-2020-3"],"attributes":{"thought":{"roles":["articulation"],"register":"nonfiction","qualifier":"A possibility, framed as complementing experimental methods."}}},{"id":"aib-arg-p1-mullard-2021","subject":"aib-doc-mullard-2021","predicate":"bears_on_proposition","object":{"kind":"entity","entityId":"aib-prop-predicted-structures-aid-drug-design"},"topic":"ideas","statement":"Mullard's Nature Reviews Drug Discovery news article \"What does AlphaFold mean for drug discovery?\" (14 September 2021) says in its standfirst that the implications of AlphaFold and RoseTTAFold for drug discovery are more incremental than their advance in structure prediction.","time":{"kind":"event","date":"2021-09-14","basis":"publication","note":"Date printed on the article page."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p1-mullard-2021-1"],"attributes":{"thought":{"roles":["context"],"register":"nonfiction","missing":"Assesses the degree of the implications for drug discovery (\"more incremental\"); it does not state that predicted structures are of no use, nor that they are of use."}}},{"id":"aib-arg-p3-exscientia-2021","subject":"aib-doc-exscientia-prospectus-2021","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-ai-drug-candidate-in-trials"},"topic":"ideas","statement":"Exscientia’s IPO prospectus (30 September 2021) states that it began a Phase 1 trial of EXS21546 in December 2020 and that it “originated the first three AI-designed precision drug candidates to enter human clinical trials”.","time":{"kind":"event","date":"2021-09-30","basis":"publication","note":"Dated by the prospectus; the trial start month (December 2020) is the company's statement within it."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p3-exscientia-2021-1","evidence-aib-arg-p3-exscientia-2021-2"],"attributes":{"thought":{"roles":["articulation"],"register":"nonfiction","qualifier":"A prospectus statement by the issuer; the priority wording is the company’s and is quoted, not adopted."}}},{"id":"aib-arg-p5-isomorphic-2021","subject":"aib-doc-iso-launch-2021","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-biology-as-information-for-ai"},"topic":"ideas","statement":"The announcement “Introducing Isomorphic Labs” (November 2021) states that biology can be thought of as an information processing system and that, just as mathematics turned out to be the right description language for physics, biology may turn out to be the perfect type of regime for the application of AI.","time":{"kind":"event","date":"2021-11","basis":"publication","note":"Month of the launch article as its original-period capture displays it (cross-family check of 2026-10-30); the page as served in 2026 displays 1 November 2021, a day no contemporaneous expression confirms."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p5-isomorphic-2021-1","evidence-aib-arg-p5-isomorphic-2021-2"],"attributes":{"thought":{"roles":["articulation"],"register":"nonfiction","qualifier":"Stated as a personal view (\"I think\") and a possibility (\"may\").","attested":{"date":"2026-10-30","note":"The quoted wording is attested in the page as served on 30 October 2026. The capture of November 2021 was read by the cross-family check for its date and opening, not for this passage, and differs from the later page in at least one phrase."}}}},{"id":"aib-arg-p1-wong-2022","subject":"aib-doc-wong-2022","predicate":"bears_on_proposition","object":{"kind":"entity","entityId":"aib-prop-predicted-structures-aid-drug-design"},"topic":"ideas","statement":"Wong et al. (Molecular Systems Biology, 6 September 2022) report that docking compounds against AlphaFold2-predicted structures of essential Escherichia coli proteins gave weak performance in their benchmark, and state that advances in modelling protein-ligand interactions are needed to better harness AlphaFold2 for drug discovery.","time":{"kind":"event","date":"2022-09-06","basis":"publication","note":"Online publication date from the JATS history of the Europe PMC full text."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p1-wong-2022-1","evidence-aib-arg-p1-wong-2022-2","evidence-aib-arg-p1-wong-2022-3"],"attributes":{"thought":{"roles":["context"],"register":"nonfiction","missing":"A caution about present docking methods; it neither states nor disputes that predicted structures aid drug discovery, and it frames AlphaFold2 as something to be \"better harnessed\"."}}},{"id":"aib-arg-p4-watson-2023","subject":"aib-doc-rfdiffusion-2023","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-ml-designs-new-proteins"},"topic":"ideas","statement":"Watson et al. (Nature, published online 11 July 2023) state that their deep-learning method RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.","time":{"kind":"event","date":"2023-07-11","basis":"publication","note":"Online publication date printed on the article page."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p4-watson-2023-1","evidence-aib-arg-p4-watson-2023-2"],"attributes":{"thought":{"roles":["articulation"],"register":"nonfiction","qualifier":"Stated of the authors' own method."}}},{"id":"aib-arg-p1-arnold-2023","subject":"aib-doc-arnold-2023","predicate":"bears_on_proposition","object":{"kind":"entity","entityId":"aib-prop-predicted-structures-aid-drug-design"},"topic":"ideas","statement":"Arnold's Nature news article (22 September 2023) says in its standfirst that questions remain about whether AlphaFold can really shake up the pharmaceutical industry.","time":{"kind":"event","date":"2023-09-22","basis":"publication","note":"Date printed on the article page."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p1-arnold-2023-1","evidence-aib-arg-p1-arnold-2023-2"],"attributes":{"thought":{"roles":["context"],"register":"nonfiction","missing":"Reports an open question about degree (whether AlphaFold can \"really shake up\" the industry); the readable text neither states nor disputes the criterion."}}},{"id":"aib-arg-p1-terwilliger-2023","subject":"aib-doc-terwilliger-2023","predicate":"bears_on_proposition","object":{"kind":"entity","entityId":"aib-prop-predicted-structures-aid-drug-design"},"topic":"ideas","statement":"Terwilliger et al. (Nature Methods, published online 30 November 2023) suggest treating AlphaFold predictions as exceptionally useful hypotheses and carrying out experimental structure determination to verify structural details, particularly interactions not included in the prediction.","time":{"kind":"event","date":"2023-11-30","basis":"publication","note":"Online publication date printed on the article page."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p1-terwilliger-2023-1","evidence-aib-arg-p1-terwilliger-2023-2"],"attributes":{"thought":{"roles":["context"],"register":"nonfiction","missing":"Addresses whether predictions can substitute for experimental structures, not whether they aid drug design."}}},{"id":"aib-arg-p3-jayatunga-2024","subject":"aib-doc-jayatunga-2024","predicate":"bears_on_proposition","object":{"kind":"entity","entityId":"aib-prop-ai-drug-candidate-in-trials"},"topic":"ideas","statement":"Jayatunga et al. (Drug Discovery Today, published online 30 April 2024) state that a growing number of drugs and vaccines have been discovered using AI and analyse the clinical pipelines of what they call AI-native biotech companies.","time":{"kind":"event","date":"2024-04-30","basis":"publication","note":"Electronic publication date as given by Europe PMC."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p3-jayatunga-2024-1"],"attributes":{"thought":{"roles":["context"],"register":"nonfiction","missing":"Names no specific candidate in the abstract read; the criterion requires a named candidate stated to have begun human testing."}}},{"id":"aib-arg-p6-jayatunga-2024","subject":"aib-doc-jayatunga-2024","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-ai-drugs-higher-trial-success"},"topic":"ideas","statement":"Jayatunga et al. (Drug Discovery Today, published online 30 April 2024) state that, in their analysis, AI-discovered molecules have an 80–90% success rate in phase I, substantially higher than historic industry averages, and a phase II success rate of about 40%, comparable to those averages, on a limited sample.","time":{"kind":"event","date":"2024-04-30","basis":"publication","note":"Electronic publication date as given by Europe PMC."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p6-jayatunga-2024-1","evidence-aib-arg-p6-jayatunga-2024-2"],"attributes":{"thought":{"roles":["earliest-located"],"register":"nonfiction","qualifier":"Phase I only; phase II stated as comparable."}}},{"id":"aib-arg-p1-lyu-2024","subject":"aib-doc-lyu-2024","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-predicted-structures-aid-drug-design"},"topic":"ideas","statement":"Lyu et al. (Science, published online 16 May 2024) report that prospective docking against unrefined AlphaFold2 models of two receptors gave hit rates similar to docking against experimental structures, and state that such models remain relevant for ligand discovery, extending the domain of structure-based drug design.","time":{"kind":"event","date":"2024-05-16","basis":"publication","note":"Online publication date on the publisher’s page, in its capture of 16 May 2024 read by the cross-family machine check of 2026-10-30; the print issue is dated 21 June 2024, which the Europe PMC record quoted here labels as the date of electronic publication. The disagreement is kept."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p1-lyu-2024-1","evidence-aib-arg-p1-lyu-2024-2","evidence-aib-arg-p1-lyu-2024-3"],"attributes":{"thought":{"roles":["articulation","earliest-located-term"],"register":"nonfiction","term":"structure-based drug design","qualifier":"Stated for two receptors, from prospective docking results reported by the authors."}}},{"id":"aib-arg-p4-esm3-2024","subject":"aib-doc-esm3-release-2024","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-ml-designs-new-proteins"},"topic":"ideas","statement":"EvolutionaryScale's ESM3 announcement (dated 25 June 2024) states that its language model generated a new fluorescent protein, esmGFP, with a sequence only 58% similar to the closest known fluorescent protein.","time":{"kind":"event","date":"2024-06-25","basis":"announcement","note":"Date printed on the post; the wording is attested in the copy served on 2026-10-30, which carries a January 2025 update notice."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p4-esm3-2024-1","evidence-aib-arg-p4-esm3-2024-2"],"attributes":{"thought":{"roles":["articulation"],"register":"nonfiction","qualifier":"A company's statement of a sequence distant from known natural proteins with a known (fluorescent) function."}}},{"id":"aib-arg-p5-esm3-2024","subject":"aib-doc-esm3-release-2024","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-biology-as-information-for-ai"},"topic":"ideas","statement":"EvolutionaryScale's ESM3 announcement (dated 25 June 2024) states that biology is written in a language not yet understood, and that learning to read and write in the code of life would make biology programmable.","time":{"kind":"event","date":"2024-06-25","basis":"announcement","note":"Date printed on the post; the wording is attested in the copy served on 2026-10-30."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p5-esm3-2024-1","evidence-aib-arg-p5-esm3-2024-2"],"attributes":{"thought":{"roles":["articulation"],"register":"nonfiction","qualifier":"Conditional, in a company's announcement of its own model."}}},{"id":"aib-arg-p2-bunne-2024","subject":"aib-doc-bunne-2024","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-ai-virtual-cell"},"topic":"ideas","statement":"Bunne et al. (arXiv v1, 18 September 2024) propose a vision of \"AI-powered Virtual Cells\" whose representations are learned from biological data and that facilitate in silico experiments to predict and understand cell behaviour.","time":{"kind":"event","date":"2024-09-18","basis":"publication","note":"Date of the arXiv v1 submission, the expression read."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p2-bunne-2024-1","evidence-aib-arg-p2-bunne-2024-2"],"attributes":{"thought":{"roles":["articulation"],"register":"nonfiction","qualifier":"A proposal and research agenda, in the future tense."}}},{"id":"aib-arg-p3-xu-2025","subject":"aib-doc-xu-2025","predicate":"articulates_proposition","object":{"kind":"entity","entityId":"aib-prop-ai-drug-candidate-in-trials"},"topic":"ideas","statement":"Xu et al. (Nature Medicine, published online 3 June 2025) report a phase 2a randomized, placebo-controlled trial of rentosertib, which they describe as an AI-generated small-molecule inhibitor of a target discovered using generative AI.","time":{"kind":"event","date":"2025-06-03","basis":"publication","note":"Online publication date printed on the article page."},"resolution":"supported","editorialState":"seed","evidenceIds":["evidence-aib-arg-p3-xu-2025-1","evidence-aib-arg-p3-xu-2025-2"],"attributes":{"thought":{"roles":["articulation"],"register":"nonfiction","qualifier":"A sponsor-authored trial report; the phase is as stated (2a)."}}},{"id":"aib-arg-p6-xu-2025","subject":"aib-doc-xu-2025","predicate":"bears_on_proposition","object":{"kind":"entity","entityId":"aib-prop-ai-drugs-higher-trial-success"},"topic":"ideas","statement":"Xu et al. 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