Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models | Paper Citation Record

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This paper cites Drug discovery and development: introduction to the general public and patient groups.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Drug discovery and development: introduction to the general public and patient groups

Reference 1

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This paper cites Innovation crisis in the pharmaceutical industry? a survey.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Innovation crisis in the pharmaceutical industry? a survey

Reference 2

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This paper cites Computational approaches streamlining drug discovery.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Computational approaches streamlining drug discovery

Reference 3

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This paper cites High-Throughput Screening: New Technology for the 21st Century.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models High-Throughput Screening: New Technology for the 21st Century

Reference 4

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This paper cites Supervised Prediction of Drug–Target Interactions Using Bipartite Local Models.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Supervised Prediction of Drug–Target Interactions Using Bipartite Local Models

Reference 5

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This paper cites The Concept of Probability in Safety Assessments of Technological Systems.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models The Concept of Probability in Safety Assessments of Technological Systems

Reference 6

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This paper cites Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

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This paper cites Sources of uncertainty in machine learning – a statisticians’ view, 2023.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Sources of uncertainty in machine learning – a statisticians’ view, 2023

Reference 8

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This paper cites Bayesian learning for neural networks, volume 118.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Bayesian learning for neural networks, volume 118

Reference 9

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This paper cites Weight uncertainty in neural network.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Weight uncertainty in neural network

Reference 10

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This paper cites What are bayesian neural network posteriors really like? In International conference on machine learning, pages 4629–4640.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models What are bayesian neural network posteriors really like? In International conference on machine learning, pages 4629–4640

Reference 11

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This paper cites Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction

Reference 12

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This paper cites Simple and scalable predictive uncer- tainty estimation using deep ensembles.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Simple and scalable predictive uncer- tainty estimation using deep ensembles

Reference 13

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Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 14

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This paper cites A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

Reference 15

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This paper cites MAPIE: an open-source library for distribution-free uncertainty quantification.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models MAPIE: an open-source library for distribution-free uncertainty quantification

Reference 16

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This paper cites Evidential deep learning to quantify classification uncertainty.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Evidential deep learning to quantify classification uncertainty

Reference 17

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This paper cites An uncertainty- guided deep learning method facilitates rapid screening of cyp3a4 inhibitors.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models An uncertainty- guided deep learning method facilitates rapid screening of cyp3a4 inhibitors

Reference 18

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This paper cites Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1a (kdm1a/lsd1) inhibitors.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1a (kdm1a/lsd1) inhibitors

Reference 19

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This paper cites Improving evidential deep learning via multi-task learning.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Improving evidential deep learning via multi-task learning

Reference 20

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This paper cites Evidential deep learning for guided molecular property prediction and discovery.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Evidential deep learning for guided molecular property prediction and discovery

Reference 21

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This paper cites Simple and principled uncertainty estimation with deterministic deep learning via distance awareness.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Simple and principled uncertainty estimation with deterministic deep learning via distance awareness

Reference 22

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This paper cites Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods

Reference 23

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This paper cites Venn-abers predictors, 2014.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Venn-abers predictors, 2014

Reference 24

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This paper cites Transforming classifier scores into accurate multiclass probability estimates.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Transforming classifier scores into accurate multiclass probability estimates

Reference 25

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This paper cites Mervin, Simon Johansson, Elizaveta Semenova, Kathryn A.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Mervin, Simon Johansson, Elizaveta Semenova, Kathryn A

Reference 26

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This paper cites Uncertainty quantification: Can we trust artificial intelligence in drug discovery? Iscience, 25(8), 2022.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Uncertainty quantification: Can we trust artificial intelligence in drug discovery? Iscience, 25(8), 2022

Reference 27

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Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models A large-scale study of probabilistic calibration in neural network regression

Reference 28

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This paper cites Quantifica- tion of uncertainty with adversarial models.Advances in Neural Information Processing Systems, 36:19446–19484, 2023.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Quantifica- tion of uncertainty with adversarial models.Advances in Neural Information Processing Systems, 36:19446–19484, 2023

Reference 29

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Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Probabilistic random forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty

Reference 30

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This paper cites An ensemble-based approach to estimate confidence of predicted protein–ligand binding affinity values.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models An ensemble-based approach to estimate confidence of predicted protein–ligand binding affinity values

Reference 31

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This paper cites Reducing overconfident errors in molecular property classification using posterior network.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Reducing overconfident errors in molecular property classification using posterior network

Reference 32

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This paper cites Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models

Reference 33

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This paper cites Uncertainty quantification using neural networks for molecular property prediction.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Uncertainty quantification using neural networks for molecular property prediction

Reference 34

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This paper cites Mervin, Avid M.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Mervin, Avid M

Reference 35

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Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Large-scale evaluation of k-fold cross-validation ensembles for uncertainty estimation

Reference 36

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Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Time-split cross-validation as a method for estimating the goodness of prospective prediction

Reference 37

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Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Simpd: an algorithm for generating simulated time splits for validating machine learning approaches

Reference 38

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This paper cites Learning classifiers when the training data is not iid.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Learning classifiers when the training data is not iid

Reference 39

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This paper cites Beyond iid: Non-iid thinking, informatics, and learning.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Beyond iid: Non-iid thinking, informatics, and learning

Reference 40

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This paper cites Sculley, Sebastian Nowozin, Joshua Dillon, Bal- aji Lakshminarayanan, and Jasper Snoek.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Sculley, Sebastian Nowozin, Joshua Dillon, Bal- aji Lakshminarayanan, and Jasper Snoek

Reference 41

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This paper cites Wilds: A benchmark of in-the-wild distribution shifts.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Wilds: A benchmark of in-the-wild distribution shifts

Reference 42

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This paper cites Weinberger.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Weinberger

Reference 43

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This paper cites Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest

Reference 44

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This paper cites Predicting good probabilities with supervised learning.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Predicting good probabilities with supervised learning

Reference 45

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This paper cites The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.Nucleic acids research, 52(D1):D1180–D1192, 2024.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.Nucleic acids research, 52(D1):D1180–D1192, 2024

Reference 46

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Observation 644321fb-29b6-4ba2-be34-9d68d8d8e292 · outbound

This paper cites Multispecies machine learning predictions of in vitro intrinsic clearance with uncertainty quantification analyses.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Multispecies machine learning predictions of in vitro intrinsic clearance with uncertainty quantification analyses

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:51.008071Z

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Observation eda7c1b8-0480-4602-b9f6-4839acba6f76 · outbound

This paper cites Computational predictions of nonclinical pharmacokinetics at the drug design stage.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Computational predictions of nonclinical pharmacokinetics at the drug design stage

Reference 48

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Observation deefeaad-7d97-4aab-881d-2c8c84d5b856 · outbound

This paper cites Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels

Reference 49

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 997b7073-7e9f-4b34-be13-1a1af86b30f4 · outbound

This paper cites Towards reliable uncertainty estimates for drug discovery: A large-scale temporal study of probability calibration.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Towards reliable uncertainty estimates for drug discovery: A large-scale temporal study of probability calibration

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.993105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ab7e92e6-c54a-49ec-a365-f3c1d5adcc22 · outbound

This paper cites Temporal evaluation of probability calibration with experimental errors.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Temporal evaluation of probability calibration with experimental errors

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.971376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5da8a1c1-b435-46d4-811d-b72a8122ddb0 · outbound

This paper cites Risks in new drug development: approval success rates for investigational drugs.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Risks in new drug development: approval success rates for investigational drugs

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.949209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a0267ba8-39bf-4844-9037-abb6531e291e · outbound

This paper cites Admet in silico modelling: towards prediction paradise? Nature reviews Drug discovery, 2(3):192–204, 2003.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Admet in silico modelling: towards prediction paradise? Nature reviews Drug discovery, 2(3):192–204, 2003

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.926852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 54c6d5cc-35e3-4ee3-9a52-4acc290361d0 · outbound

This paper cites Mechanisms of cyp450 inhibition: understanding drug-drug interactions due to mechanism- based inhibition in clinical practice.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Mechanisms of cyp450 inhibition: understanding drug-drug interactions due to mechanism- based inhibition in clinical practice

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.906477Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f02b1abf-ad56-4091-93a6-21a0f44d4f3f · outbound

This paper cites Cytochromes P450: metabolic and toxicological aspects.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Cytochromes P450: metabolic and toxicological aspects

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.898998Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f02e5663-940f-4e03-97db-8093c6097d35 · outbound

This paper cites Cytochrome p450 enzymes in drug metabolism and chemical toxicology: An introduction.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Cytochrome p450 enzymes in drug metabolism and chemical toxicology: An introduction

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.891478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d1ba5ae0-b4ec-4afb-89d3-e6b960a021e2 · outbound

This paper cites Role of caco-2 cell monolayers in prediction of intestinal drug absorption.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Role of caco-2 cell monolayers in prediction of intestinal drug absorption

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.883685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 341a1bee-de9d-462c-a4ea-400659808fa0 · outbound

This paper cites Bridging solubility between drug discovery and development.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Bridging solubility between drug discovery and development

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.876057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f5398115-ab4e-4b57-8cf5-501ca870e0a4 · outbound

This paper cites Molecular genetic insights into cardiovascular disease.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Molecular genetic insights into cardiovascular disease

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.868618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6e7a9742-d470-4242-a876-42cbd56cdcae · outbound

This paper cites Lipophilicity in drug discovery.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Lipophilicity in drug discovery

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.860170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 376a0740-89a2-4d25-bd62-b69266f4549b · outbound

This paper cites High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.850675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f404ec8-55f6-4433-a1bb-81d6c4a6008a · outbound

This paper cites In vitro high throughput screening of compounds for favorable metabolic properties in drug discovery.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models In vitro high throughput screening of compounds for favorable metabolic properties in drug discovery

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.842605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 976cfd2d-1278-455d-9c58-c1fcc8bf394a · outbound

This paper cites Optimization of a higher throughput microsomal stability screening assay for profiling drug discovery candidates.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Optimization of a higher throughput microsomal stability screening assay for profiling drug discovery candidates

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.834998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 248fee82-6644-4725-a37f-64bad2998b21 · outbound

This paper cites Rdkit: Open-source cheminformatics, 2006.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Rdkit: Open-source cheminformatics, 2006

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.827224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f0c3e133-02c7-48b6-8ac3-ebe84354e793 · outbound

This paper cites SMILES, a Chemical Language and Information System.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models SMILES, a Chemical Language and Information System

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.811213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 39d75715-fcc7-4e8f-b0d8-a552d2d275e3 · outbound

This paper cites Registries in Machine Learning-Based Drug Discovery: A Shortcut to Code Reuse.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Registries in Machine Learning-Based Drug Discovery: A Shortcut to Code Reuse

Reference 66

Resolution: verified exact; doi, observed 2026-08-09T00:03:50.570847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ddd2de04-43c3-4777-89ca-af37193855eb · outbound

This paper cites Pedregosa, G.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Pedregosa, G

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-09T00:03:50.422617Z

Source-reported events for the cited work

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Observation 4c018882-cb9b-4850-896a-ad36d0bd3be4 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.783689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation da8a5503-be49-482b-9681-c2da3ecbdb7d · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Adam: A Method for Stochastic Optimization

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.756520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 22f8f96a-38d9-4096-abd4-efd7bc5111ca · outbound

This paper cites On information and sufficiency.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models On information and sufficiency

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-09T00:03:50.480529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e5fe3dc-c853-40b5-82c0-b25110726ac0 · outbound

This paper cites Excape wp1-probabilistic prediction, 2016.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Excape wp1-probabilistic prediction, 2016

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.730756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation afdb6d08-dd69-4adb-ab6d-e6a36c6f8913 · outbound

This paper cites A kernel two-sample test.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models A kernel two-sample test

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-09T00:03:50.502846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76a23383-1ff6-4ed2-85fe-23e62ad5c07f · outbound

This paper cites Grouping of coefficients for the calculation of inter-molecular similarity and dissimilarity using 2d fragment bit-strings.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Grouping of coefficients for the calculation of inter-molecular similarity and dissimilarity using 2d fragment bit-strings

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.701824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5bcc3644-edf5-49a8-8a13-69819a35fbd0 · outbound

This paper cites The meaning and use of the area under a receiver operating characteristic (roc) curve.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models The meaning and use of the area under a receiver operating characteristic (roc) curve

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.693256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b80223f6-5d22-4221-a030-5158c094926a · outbound

This paper cites Measuring Calibration in Deep Learning.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Measuring Calibration in Deep Learning

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-09T00:03:50.510855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c402abe-b91a-486f-be07-5e26391aadde · outbound

This paper cites Strictly proper scoring rules, prediction, and estimation.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Strictly proper scoring rules, prediction, and estimation

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-09T00:03:50.514078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 200a38b2-cb14-4c8e-a726-11320dfd83f3 · outbound

This paper cites Reliability, sufficiency, and the decomposition of proper scores.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Reliability, sufficiency, and the decomposition of proper scores

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.685028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3a0a8bc1-76b0-427f-a77f-96c2230cb127 · outbound

This paper cites A unified view of label shift estimation.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models A unified view of label shift estimation

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-09T00:03:50.519157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e8a9976-f351-44fa-a077-700022f91de8 · outbound

This paper cites Evaluating scalable bayesian deep learning methods for robust computer vision.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Evaluating scalable bayesian deep learning methods for robust computer vision

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.671323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fa207645-aa99-475e-96d2-2a58c79b6c6e · outbound

This paper cites Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.662955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 29421ce4-3d1d-4a04-8965-4fbe862862fb · outbound

This paper cites Uncertainty quantification and deep ensembles.

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models Uncertainty quantification and deep ensembles

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:03:50.653806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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