Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:21:28.459598Z
Paper Citation Record · LEDGER
As of 9 August 2026, this Paper Citation Record lists 85 of 85 outbound references and 0 inbound Pith citation observations for arXiv:2507.19568.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:21:28.459598Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
85 of 85 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation f6f033af-c6c5-4821-add6-d0b993243a42 · outbound
Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Target-based drug discovery: is something wrong?,
Reference 1
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Artificial intelligence in drug discovery: what is realistic, what are illusions? part 1: Ways to make an impact, and why we are not there yet,
Reference 2
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Digital twins to personalize medicine,
Reference 3
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Digital twins in medicine,
Reference 4
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Reference 5
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery A roadmap for the development of human body digital twins,
Reference 6
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery A proteome-wide atlas of drug mechanism of action,
Reference 7
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Reference 8
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Large-scale chemoproteomics expedites ligand discovery and predicts lig- and behavior in cells,
Reference 9
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Reference 10
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling,
Reference 12
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Mole: a foundation model for molecular graphs using disentangled attention,
Reference 13
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Scientific machine learning through physics–informed neural net- works: Where we are and what’s next,
Reference 14
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Reference 15
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Novel computational approaches to polypharmacology as a means to define responses to individ- ual drugs,
Reference 16
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Evolutionary-scale prediction of atomic- level protein structure with a language model,
Reference 17
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Accurate struc- ture prediction of biomolecular interactions with alphafold 3,
Reference 18
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Model agnostic semi-supervised meta-learning elucidates understudied out-of-distribution molecular inter- actions,
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Reference 20
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Leveraging transfer learning for predicting protein-small molecule interactions,
Reference 21
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Contrastive learning in protein language space predicts interactions between drugs and protein targets,
Reference 22
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery A euclidean transformer for fast and stable machine learned force fields,
Reference 24
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Direct generation of protein conformational ensembles via machine learning,
Reference 25
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Deepreal: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand- induced gpcr activity,
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Reference 27
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Single-cell epigenomics: Recording the past and predicting the future,
Reference 28
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Reference 29
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Reference 30
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Reference 31
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Spatial multimodal analysis of transcriptomes and metabolomes in tissues,
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Single-cell lipidomics with high structural specificity by mass spectrometry,
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Reference 35
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery D’or: Deep orienter of protein-protein interaction networks,
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Reference 49
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Reference 51
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Comprehensive transcription factor perturbations recapitulate fibroblast transcriptional states,
Reference 58
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Reference 59
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Reference 60
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Reference 62
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Tackling the challenges of new approach methods for predicting drug ef- fects from model systems,
Reference 63
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Reference 64
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to covid-19 drug repurposing,
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Reference 66
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Reference 67
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Reference 68
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Unlocking gene regulation with sequence-to-function models,
Reference 69
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Towards the prediction of general biomolecular interactions with ai,
Reference 70
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Progress and opportunities of foundation models in bioinformatics,
Reference 71
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery A survey of uncertainty in deep neu- ral networks,
Reference 72
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023),
Reference 73
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Reference 74
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Explainable ai: A review of machine learning interpretability methods,
Reference 75
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Causal machine learning for healthcare and precision medicine,
Reference 76
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Causal inference in drug discovery and de- velopment,
Reference 77
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
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Reference 79
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Appli- cations of genome-scale metabolic models to the study of human diseases: A systematic review,
Reference 80
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Protocol for hybrid flux balance, statis- tical, and machine learning analysis of multi-omic data from the cyanobac- terium synechococcus sp. pcc 7002,
Reference 81
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Integration of machine learning and genome- scale metabolic modeling identifies multi-omics biomarkers for radiation resistance,
Reference 82
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Unsupervised representa- tion learning on high-dimensional clinical data improves genomic discovery and prediction,
Reference 83
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Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacoki- netic (ai-pbpk) modelling,
Reference 84
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Observation b7d1b229-a55a-4e9f-bbfd-45584372ee58 · outbound
Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery Whole person research and coordination center,
Reference 85
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No inbound Pith citation observations are available.