Challenging reaction prediction models to generalize to novel chemistry | Paper Citation Record

Observation 02ba8234-3135-4bfd-b597-8c16bca85d9c · outbound

This paper cites Reagent prediction with a molecular transformer improves reaction data quality.

Challenging reaction prediction models to generalize to novel chemistry Reagent prediction with a molecular transformer improves reaction data quality

Reference 1

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Observation 61226360-a404-4b59-93a3-58a271bdaec3 · outbound

This paper cites ASKCOS (Automated System for Knowledge-based Continuous Organic Synthesis), 2019.

Challenging reaction prediction models to generalize to novel chemistry ASKCOS (Automated System for Knowledge-based Continuous Organic Synthesis), 2019

Reference 2

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Observation 96259f8d-c397-4b60-8e9c-5e80efc49ff5 · outbound

This paper cites Learning to Split for Automatic Bias Detection.

Challenging reaction prediction models to generalize to novel chemistry Learning to Split for Automatic Bias Detection

Reference 3

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Observation eb24a935-b1b9-403a-a169-58960f1dbabd · outbound

This paper cites Non-autoregressive electron redistribution modeling for reaction prediction.

Challenging reaction prediction models to generalize to novel chemistry Non-autoregressive electron redistribution modeling for reaction prediction

Reference 4

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Observation 1f5caa5e-d623-4782-9f17-faad37313635 · outbound

This paper cites Discovery of novel chemical reactions by deep generative recurrent neural network.

Challenging reaction prediction models to generalize to novel chemistry Discovery of novel chemical reactions by deep generative recurrent neural network

Reference 5

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Observation ace1050b-913a-4d66-bf6d-1d1096857a9c · outbound

This paper cites A generative model for electron paths.

Challenging reaction prediction models to generalize to novel chemistry A generative model for electron paths

Reference 6

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Observation 15e70137-7e26-4aed-b7ea-dc37a516dc92 · outbound

This paper cites A model to search for synthesizable molecules.

Challenging reaction prediction models to generalize to novel chemistry A model to search for synthesizable molecules

Reference 7

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Observation 8c51ec70-938d-4666-a74f-a95d17929556 · outbound

This paper cites Barking up the right tree: an approach to search over molecule synthesis DAGs.

Challenging reaction prediction models to generalize to novel chemistry Barking up the right tree: an approach to search over molecule synthesis DAGs

Reference 8

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Observation 23c68d38-00e8-4c9b-a73f-9fa7a4e86027 · outbound

This paper cites Analysis of the reactions used for the preparation of drug candidate molecules.

Challenging reaction prediction models to generalize to novel chemistry Analysis of the reactions used for the preparation of drug candidate molecules

Reference 9

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Observation d14968cb-fcb2-487d-9b40-600055838e73 · outbound

This paper cites On Evaluating Adversarial Robustness.

Challenging reaction prediction models to generalize to novel chemistry On Evaluating Adversarial Robustness

Reference 10

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Observation 22bb2ff5-da75-49d4-9245-8e60e86762a5 · outbound

This paper cites Assessing the Extrapolation Capability of Template-Free Retrosynthesis Models.

Challenging reaction prediction models to generalize to novel chemistry Assessing the Extrapolation Capability of Template-Free Retrosynthesis Models

Reference 11

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Observation 887bbf3c-ce75-451d-80b0-0d0f3386f088 · outbound

This paper cites SCUBIDOO: A large yet screenable and easily searchable database of computationally created chemical compounds optimized toward high likelihood of synthetic tractability.

Challenging reaction prediction models to generalize to novel chemistry SCUBIDOO: A large yet screenable and easily searchable database of computationally created chemical compounds optimized toward high likelihood of synthetic tractability

Reference 12

Resolution: verified exact; doi, observed 2026-08-10T21:02:06.428531Z

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Observation 8e41dc20-5c5f-4f1e-b7bd-ec0facbe1dbd · outbound

This paper cites ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction.

Challenging reaction prediction models to generalize to novel chemistry ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction

Reference 13

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Observation 29d3366e-d184-4358-aef8-3fea71341bee · outbound

This paper cites A graph-convolutional neural network model for the prediction of chemical reactivity.

Challenging reaction prediction models to generalize to novel chemistry A graph-convolutional neural network model for the prediction of chemical reactivity

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:04.864784Z

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Observation 993f31d3-182e-4421-b285-5a1b1c8157ce · outbound

This paper cites Graph transformation policy network for chemical reaction prediction.

Challenging reaction prediction models to generalize to novel chemistry Graph transformation policy network for chemical reaction prediction

Reference 15

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Observation c9c88359-ade9-40da-bb9d-2f1a7d3d602d · outbound

This paper cites Deep learning for chemical reaction prediction.Molecular Systems Design & Engineering, 3(3):442–452, 2018.

Challenging reaction prediction models to generalize to novel chemistry Deep learning for chemical reaction prediction.Molecular Systems Design & Engineering, 3(3):442–452, 2018

Reference 16

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Observation 6e30d283-f95a-4571-8b71-4e7a9fe1011c · outbound

This paper cites The 25th anniversary of the Buchwald–Hartwig amination: Development, applications, and outlook.

Challenging reaction prediction models to generalize to novel chemistry The 25th anniversary of the Buchwald–Hartwig amination: Development, applications, and outlook

Reference 17

Resolution: verified exact; doi, observed 2026-08-10T21:02:06.378356Z

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Observation ed26aebf-2f96-4fc0-a58e-e96b15af5bbb · outbound

This paper cites Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design.

Challenging reaction prediction models to generalize to novel chemistry Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design

Reference 18

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Observation c26c10ef-c395-4e8a-a7ff-8e2fcdc51557 · outbound

This paper cites Holistic chemical evaluation reveals pitfalls in reaction prediction models.

Challenging reaction prediction models to generalize to novel chemistry Holistic chemical evaluation reveals pitfalls in reaction prediction models

Reference 19

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Observation d1e9b5f9-be83-4db2-a1ab-25cc7ac8063e · outbound

This paper cites Deep Learning.

Challenging reaction prediction models to generalize to novel chemistry Deep Learning

Reference 20

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Observation 6017e32f-741c-4190-b504-34213d7e8174 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Challenging reaction prediction models to generalize to novel chemistry Explaining and Harnessing Adversarial Examples

Reference 21

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Observation d77d30be-7417-45ce-8e07-df02f9ae829d · outbound

This paper cites Reaction prediction and synthesis design.

Challenging reaction prediction models to generalize to novel chemistry Reaction prediction and synthesis design

Reference 22

Resolution: verified exact; doi, observed 2026-08-10T21:02:06.327963Z

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Observation 5a29c59f-7ae7-4c43-b1e6-9784b10bfee7 · outbound

This paper cites Learning to navigate the synthetically accessible chemical space using reinforcement learning.

Challenging reaction prediction models to generalize to novel chemistry Learning to navigate the synthetically accessible chemical space using reinforcement learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:04.904111Z

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Observation 0d4c6499-46bb-4d09-a58c-7154dcf6080b · outbound

This paper cites Dataset bias in the natural sciences: A case study in chemical reaction prediction and synthesis design.

Challenging reaction prediction models to generalize to novel chemistry Dataset bias in the natural sciences: A case study in chemical reaction prediction and synthesis design

Reference 24

Resolution: verified exact; doi, observed 2026-08-10T21:02:06.309809Z

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Observation 60079e59-a742-44e9-a985-04e7afcaa7d5 · outbound

This paper cites In search of lost domain generalization.

Challenging reaction prediction models to generalize to novel chemistry In search of lost domain generalization

Reference 25

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Observation f7edcef7-d9bc-49ab-813d-42a38aaf09d7 · outbound

This paper cites Palladium-catalyzed aromatic aminations with in situ generated aminostannanes.

Challenging reaction prediction models to generalize to novel chemistry Palladium-catalyzed aromatic aminations with in situ generated aminostannanes

Reference 26

Resolution: verified exact; doi, observed 2026-08-10T21:02:06.293709Z

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Observation 38c67b3e-e982-496a-8e73-cdda5b3ca02f · outbound

This paper cites A baseline for detecting misclassified and out-of-distribution examples in neural networks.

Challenging reaction prediction models to generalize to novel chemistry A baseline for detecting misclassified and out-of-distribution examples in neural networks

Reference 27

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Observation 89eaffd2-7886-4e16-90c7-111a2b926126 · outbound

This paper cites Reaction planning: prediction of new organic reactions.

Challenging reaction prediction models to generalize to novel chemistry Reaction planning: prediction of new organic reactions

Reference 28

Resolution: verified exact; doi, observed 2026-08-10T21:02:06.274006Z

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Observation dd93b760-e5d0-4846-bb11-30b13c67de38 · outbound

This paper cites Molecular design in synthetically accessible chemical space via deep reinforcement learning.

Challenging reaction prediction models to generalize to novel chemistry Molecular design in synthetically accessible chemical space via deep reinforcement learning

Reference 29

Resolution: verified exact; doi, observed 2026-08-10T21:02:06.255137Z

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Observation 8a8e144f-c076-4607-ae3a-c0fcdc7e7564 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Challenging reaction prediction models to generalize to novel chemistry LoRA: Low-rank adaptation of large language models

Reference 30

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Observation f5310d17-727b-4c5c-99cd-6595e0a91fc5 · outbound

This paper cites Chemformer: A pre-trained transformer for computational chemistry.

Challenging reaction prediction models to generalize to novel chemistry Chemformer: A pre-trained transformer for computational chemistry

Reference 31

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Observation 54970549-94aa-41df-833f-91f67dfa3b1b · outbound

This paper cites Transformer performance for chemical reactions: Analysis of different predictive and evaluation scenarios.

Challenging reaction prediction models to generalize to novel chemistry Transformer performance for chemical reactions: Analysis of different predictive and evaluation scenarios

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Observation 37401f53-d5b2-4686-bc54-b5ce691d77b2 · outbound

This paper cites Predicting organic reaction outcomes with Weisfeiler-Lehman network.

Challenging reaction prediction models to generalize to novel chemistry Predicting organic reaction outcomes with Weisfeiler-Lehman network

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This paper cites Latent biases in machine learning models for predicting binding affinities using popular data sets.

Challenging reaction prediction models to generalize to novel chemistry Latent biases in machine learning models for predicting binding affinities using popular data sets

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Observation 7c98d277-d814-4855-96b1-7112a91e0be8 · outbound

This paper cites ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning.

Challenging reaction prediction models to generalize to novel chemistry ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning

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This paper cites Pursuing a prospective perspective.

Challenging reaction prediction models to generalize to novel chemistry Pursuing a prospective perspective

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Observation 37363237-08fb-4777-812f-8f4b54edb473 · outbound

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

Challenging reaction prediction models to generalize to novel chemistry WILDS: A benchmark of in-the-wild distribution shifts

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Observation ddb5acf5-ccd9-4c7e-b215-f67b0e8e6fbc · outbound

This paper cites ChemBO: Bayesian optimization of small organic molecules with synthesizable recommendations.

Challenging reaction prediction models to generalize to novel chemistry ChemBO: Bayesian optimization of small organic molecules with synthesizable recommendations

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Observation 9f31c674-b22b-43f8-a40c-2ffa5c8f2161 · outbound

This paper cites Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias.

Challenging reaction prediction models to generalize to novel chemistry Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias

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Observation 0e26b13d-02b6-44a7-a2f3-4545019b6514 · outbound

This paper cites NameRxn: More than just a reaction classifier.

Challenging reaction prediction models to generalize to novel chemistry NameRxn: More than just a reaction classifier

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This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Challenging reaction prediction models to generalize to novel chemistry Simple and scalable predictive uncertainty estimation using deep ensembles

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This paper cites SIMPD: An algorithm for generating simulated time splits for validating machine learning approaches.

Challenging reaction prediction models to generalize to novel chemistry SIMPD: An algorithm for generating simulated time splits for validating machine learning approaches

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Observation d240ff09-f99f-4640-81c3-ae51ca8d526e · outbound

This paper cites Handwritten digit recognition with a back-propagation network.

Challenging reaction prediction models to generalize to novel chemistry Handwritten digit recognition with a back-propagation network

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This paper cites BART: Denoising sequence-to-sequence pre-training for natural language genera- tion, translation, and comprehension.

Challenging reaction prediction models to generalize to novel chemistry BART: Denoising sequence-to-sequence pre-training for natural language genera- tion, translation, and comprehension

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This paper cites A system for massively parallel hyperparameter tuning.

Challenging reaction prediction models to generalize to novel chemistry A system for massively parallel hyperparameter tuning

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Observation 2d076a17-e4bf-4762-8fe6-1885ebfcc033 · outbound

This paper cites Decoupled weight decay regularization.

Challenging reaction prediction models to generalize to novel chemistry Decoupled weight decay regularization

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This paper cites Extraction of chemical structures and reactions from the literature.

Challenging reaction prediction models to generalize to novel chemistry Extraction of chemical structures and reactions from the literature

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This paper cites Ideation and evaluation of novel multicomponent reactions via mechanistic network analysis and automation.

Challenging reaction prediction models to generalize to novel chemistry Ideation and evaluation of novel multicomponent reactions via mechanistic network analysis and automation

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Resolution: verified exact; doi, observed 2026-08-10T21:02:06.089474Z

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Observation 2f815b11-669c-4c6d-bfd0-1ecbf4583e41 · outbound

This paper cites Pistachio - search and faceting of large reaction databases.

Challenging reaction prediction models to generalize to novel chemistry Pistachio - search and faceting of large reaction databases

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Observation 61393411-811e-4c49-9fb8-9b7f3b80eb1c · outbound

This paper cites Doubly stochastic graph-based non-autoregressive reaction prediction.

Challenging reaction prediction models to generalize to novel chemistry Doubly stochastic graph-based non-autoregressive reaction prediction

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Observation 93477630-e2eb-42c7-acc0-b13f76c80ea0 · outbound

This paper cites Ray: A distributed framework for emerging AI applications.

Challenging reaction prediction models to generalize to novel chemistry Ray: A distributed framework for emerging AI applications

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This paper cites Pistachio.

Challenging reaction prediction models to generalize to novel chemistry Pistachio

Reference 52

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Observation 747d242e-a139-45c7-a488-52e55fcaf3da · outbound

This paper cites NameRxn (expert system for named reaction identification and classification), 2022.

Challenging reaction prediction models to generalize to novel chemistry NameRxn (expert system for named reaction identification and classification), 2022

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.050153Z

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Observation 0d99fead-aea1-48f2-8177-77d44570ed1d · outbound

This paper cites Palladium-catalyzed formation of carbon-nitrogen bonds.

Challenging reaction prediction models to generalize to novel chemistry Palladium-catalyzed formation of carbon-nitrogen bonds

Reference 54

Resolution: verified exact; doi, observed 2026-08-10T21:02:06.037901Z

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Observation 66d108fa-bbff-4a5f-babb-5e820c1e5d5d · outbound

This paper cites Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates.Nature communications, 11(1):4874, 2020.

Challenging reaction prediction models to generalize to novel chemistry Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates.Nature communications, 11(1):4874, 2020

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Resolution: verified exact; doi, observed 2026-08-10T21:02:06.021726Z

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Observation 278de73d-59ca-4bcd-a590-67c47d66338a · outbound

This paper cites Elements of Causal Inference.

Challenging reaction prediction models to generalize to novel chemistry Elements of Causal Inference

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This paper cites MIT Press, 2008.

Challenging reaction prediction models to generalize to novel chemistry MIT Press, 2008

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Observation a1f74e00-0e47-4826-9a19-974223ae4da1 · outbound

This paper cites RDKit: Open-source cheminformatics, 2021.

Challenging reaction prediction models to generalize to novel chemistry RDKit: Open-source cheminformatics, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:02:06.982358Z

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Observation 601eee98-7e02-45c2-ba10-531900f864f8 · outbound

This paper cites The medicinal chemist’ s toolbox: An analysis of reactions used in the pursuit of drug candidates.

Challenging reaction prediction models to generalize to novel chemistry The medicinal chemist’ s toolbox: An analysis of reactions used in the pursuit of drug candidates

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.089224Z

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Observation a96b752b-0d5e-4ab4-b480-1ec0c2208cea · outbound

This paper cites RXNO: reaction ontologies, 2012.

Challenging reaction prediction models to generalize to novel chemistry RXNO: reaction ontologies, 2012

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:02:06.966051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 973d64f2-ad75-4795-aa51-78cfbb2697de · outbound

This paper cites Molecule edit graph attention network: Modeling chemical reactions as sequences of graph edits.

Challenging reaction prediction models to generalize to novel chemistry Molecule edit graph attention network: Modeling chemical reactions as sequences of graph edits

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.101137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 168195f1-2c0b-4a5e-83c0-200aae48b567 · outbound

This paper cites BREEDS: Benchmarks for subpopulation shift.

Challenging reaction prediction models to generalize to novel chemistry BREEDS: Benchmarks for subpopulation shift

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:02:06.951762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 852ff1cf-fca3-46d9-a09a-ea0302dde5c2 · outbound

This paper cites Big data from pharmaceutical patents: A computational analysis of medicinal chemists’ bread and butter.Journal of medicinal chemistry, 59(9):4385–4402, 2016.

Challenging reaction prediction models to generalize to novel chemistry Big data from pharmaceutical patents: A computational analysis of medicinal chemists’ bread and butter.Journal of medicinal chemistry, 59(9):4385–4402, 2016

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.110789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2eb6bbc-491b-475c-8724-18892994f3ed · outbound

This paper cites Found in translation.

Challenging reaction prediction models to generalize to novel chemistry Found in translation

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.115609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4c304da-5173-480c-8823-da89cc4e1b73 · outbound

This paper cites Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction.

Challenging reaction prediction models to generalize to novel chemistry Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.121113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 482e6675-d47c-41e9-9e3b-8f01c144048b · outbound

This paper cites Predicting retrosynthetic pathways using transformer- based models and a hyper-graph exploration strategy.

Challenging reaction prediction models to generalize to novel chemistry Predicting retrosynthetic pathways using transformer- based models and a hyper-graph exploration strategy

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Resolution: verified exact; doi, observed 2026-08-10T21:02:05.918524Z

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Observation fb03ee96-1be4-441d-8b13-82687450027f · outbound

This paper cites On causal and anticausal learning.

Challenging reaction prediction models to generalize to novel chemistry On causal and anticausal learning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:02:06.937119Z

Source-reported events for the cited work

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Observation 308e6273-9663-47a6-b68f-db5621b812ac · outbound

This paper cites Modelling chemical reasoning to predict and invent reactions.Chemistry, 23(25):6118–6128, 2017.

Challenging reaction prediction models to generalize to novel chemistry Modelling chemical reasoning to predict and invent reactions.Chemistry, 23(25):6118–6128, 2017

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Resolution: verified exact; doi, observed 2026-08-10T21:02:05.890881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 78ff74ad-24e0-4d76-aab2-2de2a081f2b3 · outbound

This paper cites Planning chemical syntheses with deep neural networks and symbolic AI.

Challenging reaction prediction models to generalize to novel chemistry Planning chemical syntheses with deep neural networks and symbolic AI

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.141536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97d92371-4319-4e07-a2d1-70c0d7725d29 · outbound

This paper cites Improving few- and zero-shot reaction template prediction using modern Hopfield networks.

Challenging reaction prediction models to generalize to novel chemistry Improving few- and zero-shot reaction template prediction using modern Hopfield networks

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.146374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17db48e4-c050-4a19-bf9a-2c2a9cf6a646 · outbound

This paper cites Time-split cross-validation as a method for estimating the goodness of prospective predic- tion.

Challenging reaction prediction models to generalize to novel chemistry Time-split cross-validation as a method for estimating the goodness of prospective predic- tion

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.150517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ad13e92-8d73-405d-bede-c2e1ebbd70d2 · outbound

This paper cites Improving predictive inference under covariate shift by weighting the log-likelihood function.

Challenging reaction prediction models to generalize to novel chemistry Improving predictive inference under covariate shift by weighting the log-likelihood function

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.155545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8088dead-9c40-4cea-8094-f8308dbab54a · outbound

This paper cites Beyond the imitation game: Quantifying and extrapolating the capabilities of language models.

Challenging reaction prediction models to generalize to novel chemistry Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:02:06.918490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 46793636-70f7-4e64-b1fb-c2fb475f93ce · outbound

This paper cites Lo-Hi: Practical ML drug discovery benchmark.

Challenging reaction prediction models to generalize to novel chemistry Lo-Hi: Practical ML drug discovery benchmark

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:02:06.895166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2ff68fc1-bdaa-4a02-8449-fe5976483b3b · outbound

This paper cites Reproducing the invention of a named reaction: zero-shot prediction of unseen chemical reactions.

Challenging reaction prediction models to generalize to novel chemistry Reproducing the invention of a named reaction: zero-shot prediction of unseen chemical reactions

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Resolution: verified exact; doi, observed 2026-08-10T21:02:05.800148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 831b2039-2ddb-489d-a581-2a6e2cbaf22f · outbound

This paper cites Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation.

Challenging reaction prediction models to generalize to novel chemistry Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:02:06.878458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 903374e9-44a8-4f24-9be1-3e6e15c099ff · outbound

This paper cites Generative AI for designing and validating easily synthesizable and structurally novel antibiotics.

Challenging reaction prediction models to generalize to novel chemistry Generative AI for designing and validating easily synthesizable and structurally novel antibiotics

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.181467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc25a924-8e86-4b06-9022-e6a5788c5a97 · outbound

This paper cites Intriguing properties of neural networks.

Challenging reaction prediction models to generalize to novel chemistry Intriguing properties of neural networks

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.186096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f030306-c45d-4ebd-ab0b-b9ff49a49a52 · outbound

This paper cites State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis.

Challenging reaction prediction models to generalize to novel chemistry State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis

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Resolution: verified exact; doi, observed 2026-08-10T21:02:05.746927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4b9a7192-a068-4199-af21-6b493c7eb9c1 · outbound

This paper cites Unas- sisted noise reduction of chemical reaction data sets.

Challenging reaction prediction models to generalize to novel chemistry Unas- sisted noise reduction of chemical reaction data sets

Reference 80

Resolution: verified exact; doi, observed 2026-08-10T21:02:05.730987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f055882b-c645-4d86-a307-138406575335 · outbound

This paper cites Fast customization of chemical language models to out-of-distribution data sets.

Challenging reaction prediction models to generalize to novel chemistry Fast customization of chemical language models to out-of-distribution data sets

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.205585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0598a5bb-229e-48a8-ba54-c3bf5988ff4e · outbound

This paper cites Real-world molecular out-of-distribution: Specification and investigation.

Challenging reaction prediction models to generalize to novel chemistry Real-world molecular out-of-distribution: Specification and investigation

Reference 82

Resolution: verified exact; doi, observed 2026-08-10T21:02:05.695183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d7c014dd-7558-4877-93ab-fabc47266da4 · outbound

This paper cites Permutation Invariant Graph-to-Sequence Model for Template-Free Ret- rosynthesis and Reaction Prediction.

Challenging reaction prediction models to generalize to novel chemistry Permutation Invariant Graph-to-Sequence Model for Template-Free Ret- rosynthesis and Reaction Prediction

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Resolution: verified exact; doi, observed 2026-08-10T21:02:05.677463Z

Source-reported events for the cited work

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Observation 9d6ae241-f086-4cb5-86fd-c27364dc5f5c · outbound

This paper cites Predictive chemistry: machine learning for reac- tion deployment, reaction development, and reaction discovery.

Challenging reaction prediction models to generalize to novel chemistry Predictive chemistry: machine learning for reac- tion deployment, reaction development, and reaction discovery

Reference 84

Resolution: verified exact; doi, observed 2026-08-10T21:02:05.659142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 66979f08-fbad-49d8-a9e7-9298c7e80b0c · outbound

This paper cites Attention is all you need.

Challenging reaction prediction models to generalize to novel chemistry Attention is all you need

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:02:06.863445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e5764482-8842-4cbe-b21d-181767fa4365 · outbound

This paper cites SYNOPSIS: SYNthesize and OPtimize system in silico.

Challenging reaction prediction models to generalize to novel chemistry SYNOPSIS: SYNthesize and OPtimize system in silico

Reference 86

Resolution: verified exact; doi, observed 2026-08-10T21:02:05.639074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 846ac401-5767-4605-8bb7-25357d4170bb · outbound

This paper cites ChemistGA: A chemical synthesizable accessible molecular generation algorithm for real-world drug discovery.

Challenging reaction prediction models to generalize to novel chemistry ChemistGA: A chemical synthesizable accessible molecular generation algorithm for real-world drug discovery

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Resolution: verified exact; doi, observed 2026-08-10T21:02:05.616902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3af2f178-4f3c-4c23-9f53-60a40091daf9 · outbound

This paper cites Heck reaction prediction using a transformer model based on a transfer learning strategy.

Challenging reaction prediction models to generalize to novel chemistry Heck reaction prediction using a transformer model based on a transfer learning strategy

Reference 88

Resolution: verified exact; doi, observed 2026-08-10T21:02:05.590160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f7eede4e-2649-402a-bd6b-40070ed61171 · outbound

This paper cites From theory to experiment: transformer-based generation enables rapid discovery of novel reactions.

Challenging reaction prediction models to generalize to novel chemistry From theory to experiment: transformer-based generation enables rapid discovery of novel reactions

Reference 89

Resolution: verified exact; doi, observed 2026-08-10T21:02:05.566752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 997562b1-8152-49b3-943c-3cdf2cb30d90 · outbound

This paper cites A short review of chemical reaction database systems, computer-aided synthesis design, reaction prediction and synthetic feasibility.

Challenging reaction prediction models to generalize to novel chemistry A short review of chemical reaction database systems, computer-aided synthesis design, reaction prediction and synthetic feasibility

Reference 90

Resolution: verified exact; doi, observed 2026-08-10T21:02:05.547987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6ea57cea-729b-4cc7-8de5-71b74a4a303f · outbound

This paper cites Neural networks for the prediction of organic chemistry reactions.

Challenging reaction prediction models to generalize to novel chemistry Neural networks for the prediction of organic chemistry reactions

Reference 91

Resolution: verified exact; doi, observed 2026-08-10T21:02:05.525597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 38d6b2e6-7d9b-4264-9625-396a7730c9db · outbound

This paper cites ORDerly: Data sets and benchmarks for chemical reaction data.

Challenging reaction prediction models to generalize to novel chemistry ORDerly: Data sets and benchmarks for chemical reaction data

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:02:06.846513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a126cf86-875e-4daa-b751-f136e1438ae1 · outbound

This paper cites HuggingFace’ s transformers: State-of-the-art natural language processing.arXiv [cs.CL],.

Challenging reaction prediction models to generalize to novel chemistry HuggingFace’ s transformers: State-of-the-art natural language processing.arXiv [cs.CL],

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:02:06.824136Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c28c3e49-9eed-4647-b879-2768b411b146 · outbound

This paper cites MoleculeNet: a benchmark for molecular machine learning.

Challenging reaction prediction models to generalize to novel chemistry MoleculeNet: a benchmark for molecular machine learning

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:05.288640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7d5953f-8e23-4cf0-8fd8-a5a1b7dd9238 · outbound

This paper cites OpenOOD: Benchmarking generalized out-of-distribution detection.

Challenging reaction prediction models to generalize to novel chemistry OpenOOD: Benchmarking generalized out-of-distribution detection

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:02:06.796531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6b299454-0e9e-4e79-b988-76d33769cbef · outbound

This paper cites RetroOOD: Understanding out-of-distribution generalization in retrosynthesis prediction.

Challenging reaction prediction models to generalize to novel chemistry RetroOOD: Understanding out-of-distribution generalization in retrosynthesis prediction

Reference 96

Resolution: verified exact; doi, observed 2026-08-10T21:02:05.431312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ce44b3c9-742a-4759-bc78-bbb280b2cac0 · outbound

This paper cites Retrosynthesis prediction using an end-to-end graph gen- erative architecture for molecular graph editing.

Challenging reaction prediction models to generalize to novel chemistry Retrosynthesis prediction using an end-to-end graph gen- erative architecture for molecular graph editing

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Resolution: malformed identifier; no resolver link, observed 2026-08-10T21:02:05.309242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca5e8492-4451-4efa-afa3-13d4432cee3d · outbound

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Challenging reaction prediction models to generalize to novel chemistry Unresolved cited work

Reference 103

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:02:06.778523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 667f5976-c416-4844-b520-ee96c3ed0cb8 · outbound

This paper cites an unresolved cited work.

Challenging reaction prediction models to generalize to novel chemistry Unresolved cited work

Reference 104

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:02:06.761013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2ababd88-f8d1-42ec-ac15-51e74e4c03c4 · outbound

This paper cites an unresolved cited work.

Challenging reaction prediction models to generalize to novel chemistry Unresolved cited work

Reference 105

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:02:06.745471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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