Paper Citation Record · LEDGER

NovoMolGen: Rethinking Molecular Language Model Pretraining

As of 18 August 2026, this Paper Citation Record lists 93 of 93 outbound references and 2 inbound Pith citation observations for arXiv:2508.13408.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.13408 v2

Coverage vector

measured 93 of 93 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T19:07:59.804892Z

measured 95 of 95 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T09:57:12.398344Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T10:37:56.687533Z

Reference resolution

93 of 93 outbound references displayed

verified exact13
verified fuzzy24
unresolved54
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8875ca5e-1886-48f4-978e-66b9a4be1162 · outbound

This paper cites Generative Pre - Training from Molecules , September 2021.

NovoMolGen: Rethinking Molecular Language Model Pretraining Generative Pre - Training from Molecules , September 2021

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:47.409096Z

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Observation ff37e80d-ebcf-4553-801f-9d79491ab86a · outbound

This paper cites Fast, accurate, and reliable molecular docking with QuickVina 2.

NovoMolGen: Rethinking Molecular Language Model Pretraining Fast, accurate, and reliable molecular docking with QuickVina 2

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:47.686279Z

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Observation 1a1471db-7dd0-4ff6-be90-4088f710b6fc · outbound

This paper cites an unresolved cited work.

NovoMolGen: Rethinking Molecular Language Model Pretraining Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:47.851306Z

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Observation fed5d498-0bc4-4a6b-92ba-950fdceae696 · outbound

This paper cites Bemis and Mark A.

NovoMolGen: Rethinking Molecular Language Model Pretraining Bemis and Mark A

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:47.973415Z

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Observation cf8110cd-3a7d-4db5-a3f7-a7eb09d78ccc · outbound

This paper cites Acegen: Reinforcement learning of generative chemical agents for drug discovery.

NovoMolGen: Rethinking Molecular Language Model Pretraining Acegen: Reinforcement learning of generative chemical agents for drug discovery

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:48.113493Z

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Observation f12dcb8c-9aba-4c00-832a-c26313017337 · outbound

This paper cites GNN - FiLM : Graph Neural Networks with Feature -wise Linear Modulation.

NovoMolGen: Rethinking Molecular Language Model Pretraining GNN - FiLM : Graph Neural Networks with Feature -wise Linear Modulation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:48.271646Z

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Observation 44db3cd4-57de-4f7d-98c5-e99d58205e62 · outbound

This paper cites MolGAN: An implicit generative model for small molecular graphs.

NovoMolGen: Rethinking Molecular Language Model Pretraining MolGAN: An implicit generative model for small molecular graphs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:48.449111Z

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Observation 313ccc06-a907-4926-92db-d3d9572d30be · outbound

This paper cites BARTSmiles: Generative Masked Language Models for Molecular Representations.

NovoMolGen: Rethinking Molecular Language Model Pretraining BARTSmiles: Generative Masked Language Models for Molecular Representations

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-05T19:08:10.954751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1cb10d5c-a584-416b-8ba1-46a4f31b052e · outbound

This paper cites Fu, Stefano Ermon, Atri Rudra, and Christopher Ré.

NovoMolGen: Rethinking Molecular Language Model Pretraining Fu, Stefano Ermon, Atri Rudra, and Christopher Ré

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:48.796822Z

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Observation b646976c-9ad1-4470-b075-6b3762ccbd9e · outbound

This paper cites On the Art of Compiling and Using ' Drug - Like ' Chemical Fragment Spaces.

NovoMolGen: Rethinking Molecular Language Model Pretraining On the Art of Compiling and Using ' Drug - Like ' Chemical Fragment Spaces

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:48.970655Z

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Observation 988d0570-3340-46b6-b9ce-5a3891ef325f · outbound

This paper cites The Llama 3 Herd of Models.

NovoMolGen: Rethinking Molecular Language Model Pretraining The Llama 3 Herd of Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:49.239142Z

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Observation 06141fc8-71c1-47d7-956a-5907daf78691 · outbound

This paper cites Durant, Burton A.

NovoMolGen: Rethinking Molecular Language Model Pretraining Durant, Burton A

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:49.359629Z

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Observation 43196b5c-3cc7-4157-9568-0a4ae0749c43 · outbound

This paper cites LIMO : Latent Inceptionism for Targeted Molecule Generation.

NovoMolGen: Rethinking Molecular Language Model Pretraining LIMO : Latent Inceptionism for Targeted Molecule Generation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:49.534681Z

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Observation fd16d9fe-1efa-40fa-93ed-8d1792d42032 · outbound

This paper cites Bioreason: Incentivizing multimodal biological reasoning within a dna-llm model.

NovoMolGen: Rethinking Molecular Language Model Pretraining Bioreason: Incentivizing multimodal biological reasoning within a dna-llm model

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:49.680665Z

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Observation 1183a795-cecd-4db4-bec2-75f221ca5410 · outbound

This paper cites Geometry-enhanced molecular representation learning for property prediction.

NovoMolGen: Rethinking Molecular Language Model Pretraining Geometry-enhanced molecular representation learning for property prediction

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:49.787225Z

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Observation e89fed57-3c46-4bb6-a738-559357ce6ae4 · outbound

This paper cites Domain- Agnostic Molecular Generation with Chemical Feedback.

NovoMolGen: Rethinking Molecular Language Model Pretraining Domain- Agnostic Molecular Generation with Chemical Feedback

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:49.897072Z

Source-reported events for the cited work

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Observation a83da620-0377-44c0-8c8c-792bd0d202c5 · outbound

This paper cites Neural scaling of deep chemical models.

NovoMolGen: Rethinking Molecular Language Model Pretraining Neural scaling of deep chemical models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:50.116672Z

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Observation 0fa081b4-0a1d-46ef-81cb-48deb4f5a831 · outbound

This paper cites Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization.

NovoMolGen: Rethinking Molecular Language Model Pretraining Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:50.235756Z

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Observation c18881a2-6397-48e1-ac47-0d0509dd70a9 · outbound

This paper cites an unresolved cited work.

NovoMolGen: Rethinking Molecular Language Model Pretraining Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:50.334742Z

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Observation 8991cbf0-1843-4d5c-a177-727baf2eda43 · outbound

This paper cites Schoenholz, Patrick F.

NovoMolGen: Rethinking Molecular Language Model Pretraining Schoenholz, Patrick F

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:50.472399Z

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Observation e1096ef8-6118-43bd-a27b-3d69ef081f65 · outbound

This paper cites Bidirectional Molecule Generation with Recurrent Neural Networks.

NovoMolGen: Rethinking Molecular Language Model Pretraining Bidirectional Molecule Generation with Recurrent Neural Networks

Reference 21

Resolution: verified exact; doi, observed 2026-08-05T19:08:04.986582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 913b39a8-0dc8-4e49-b568-ecd9152b8315 · outbound

This paper cites OLMo: Accelerating the Science of Language Models.

NovoMolGen: Rethinking Molecular Language Model Pretraining OLMo: Accelerating the Science of Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:50.614454Z

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Observation 37b60ffc-222e-4480-9d6d-b989c27c9af9 · outbound

This paper cites Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation.

NovoMolGen: Rethinking Molecular Language Model Pretraining Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:50.816649Z

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Observation 10016855-c50b-4b27-90dc-039743739973 · outbound

This paper cites Scaffold splits overestimate virtual screening performance.

NovoMolGen: Rethinking Molecular Language Model Pretraining Scaffold splits overestimate virtual screening performance

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:50.997545Z

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Observation c8efa27a-743e-4dc9-a3c1-6b59fbaa5269 · outbound

This paper cites Iyer, Yihong Ma, Olaf Wiest, Xiangliang Zhang, Wei Wang, Chuxu Zhang, and Nitesh V.

NovoMolGen: Rethinking Molecular Language Model Pretraining Iyer, Yihong Ma, Olaf Wiest, Xiangliang Zhang, Wei Wang, Chuxu Zhang, and Nitesh V

Reference 25

Resolution: verified exact; doi, observed 2026-08-05T19:08:04.604751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 835d0f3a-f597-4ba3-ba76-91731ab92bc0 · outbound

This paper cites Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D.

NovoMolGen: Rethinking Molecular Language Model Pretraining Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:51.194771Z

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Observation 1fb27c38-1b24-4cf5-ad83-591fa707c644 · outbound

This paper cites Denoising diffusion probabilistic models.

NovoMolGen: Rethinking Molecular Language Model Pretraining Denoising diffusion probabilistic models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:51.334828Z

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Observation 5364ea7a-15d7-44de-9701-1e3371cfebd2 · outbound

This paper cites Equivariant Diffusion for Molecule Generation in 3D.

NovoMolGen: Rethinking Molecular Language Model Pretraining Equivariant Diffusion for Molecule Generation in 3D

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:51.524789Z

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Observation d3823e88-3f93-4067-b125-cb456799704f · outbound

This paper cites MDM : Molecular Diffusion Model for 3D Molecule Generation.

NovoMolGen: Rethinking Molecular Language Model Pretraining MDM : Molecular Diffusion Model for 3D Molecule Generation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:51.624763Z

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Observation 52527e07-aa84-4685-a1f6-d01b7e65bc90 · outbound

This paper cites Zinc: a free tool to discover chemistry for biology.

NovoMolGen: Rethinking Molecular Language Model Pretraining Zinc: a free tool to discover chemistry for biology

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:51.814831Z

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Observation 1a280d74-3cd5-4b44-9ac1-85a270d5b28b · outbound

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

NovoMolGen: Rethinking Molecular Language Model Pretraining Chemformer: a pre-trained transformer for computational chemistry

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:51.994748Z

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Observation bb63907c-444b-4ee6-ab45-aa78c2db8791 · outbound

This paper cites Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors.

NovoMolGen: Rethinking Molecular Language Model Pretraining Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:52.159909Z

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Observation e6d81fad-805a-4559-b6fe-a39e6e7839f4 · outbound

This paper cites Mistral 7B.

NovoMolGen: Rethinking Molecular Language Model Pretraining Mistral 7B

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:52.324654Z

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Observation 9f1036be-a4ed-4ae4-83bc-4829ec19a65f · outbound

This paper cites Junction Tree Variational Autoencoder for Molecular Graph Generation.

NovoMolGen: Rethinking Molecular Language Model Pretraining Junction Tree Variational Autoencoder for Molecular Graph Generation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:19.404759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dfe64f6b-9562-435f-9a9e-ae81e7a27037 · outbound

This paper cites Multi- Objective Molecule Generation using Interpretable Substructures.

NovoMolGen: Rethinking Molecular Language Model Pretraining Multi- Objective Molecule Generation using Interpretable Substructures

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:19.024838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a401930b-fc98-448e-9634-1d8daac1a95b · outbound

This paper cites Hierarchical generation of molecular graphs using structural motifs.

NovoMolGen: Rethinking Molecular Language Model Pretraining Hierarchical generation of molecular graphs using structural motifs

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:18.724755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bb3602de-e80e-4035-b876-8de5da7cf05f · outbound

This paper cites Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations.

NovoMolGen: Rethinking Molecular Language Model Pretraining Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:18.394749Z

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Observation 7ab61c8e-8cf6-44bd-b8a4-7354cc462c3f · outbound

This paper cites Kipf and Max Welling.

NovoMolGen: Rethinking Molecular Language Model Pretraining Kipf and Max Welling

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:18.094833Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9ef3cb62-5a5e-4685-92d2-363723d5e3d5 · outbound

This paper cites Chemical space.

NovoMolGen: Rethinking Molecular Language Model Pretraining Chemical space

Reference 39

Resolution: verified exact; doi, observed 2026-08-05T19:08:03.814755Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation af7c6c35-b03d-4c48-8a04-ee145a755796 · outbound

This paper cites Aditya Prakash, and Chao Zhang.

NovoMolGen: Rethinking Molecular Language Model Pretraining Aditya Prakash, and Chao Zhang

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:17.654752Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 13ff30c6-084c-4484-b360-1d51b9f4c62d · outbound

This paper cites Frey, Pascal Friederich, Théophile Gaudin, Alberto Alexander Gayle, Kevin Maik Jablonka, Rafael F.

NovoMolGen: Rethinking Molecular Language Model Pretraining Frey, Pascal Friederich, Théophile Gaudin, Alberto Alexander Gayle, Kevin Maik Jablonka, Rafael F

Reference 41

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T19:08:09.484751Z

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Observation c55b65db-5177-43bb-9e9c-72ad80881afd · outbound

This paper cites Subword Regularization : Improving Neural Network Translation Models with Multiple Subword Candidates.

NovoMolGen: Rethinking Molecular Language Model Pretraining Subword Regularization : Improving Neural Network Translation Models with Multiple Subword Candidates

Reference 42

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Observation a514ee22-df93-4d56-b16a-c05444ccf48a · outbound

This paper cites MolGrow : A Graph Normalizing Flow for Hierarchical Molecular Generation.

NovoMolGen: Rethinking Molecular Language Model Pretraining MolGrow : A Graph Normalizing Flow for Hierarchical Molecular Generation

Reference 43

Resolution: verified exact; doi, observed 2026-08-05T19:08:03.394761Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4722ab2a-fd69-4c0c-8042-a2e2fdd184fa · outbound

This paper cites Neuraldecipher – reverse-engineering extended-connectivity fingerprints ( ECFPs ) to their molecular structures.

NovoMolGen: Rethinking Molecular Language Model Pretraining Neuraldecipher – reverse-engineering extended-connectivity fingerprints ( ECFPs ) to their molecular structures

Reference 44

Resolution: verified exact; doi, observed 2026-08-05T19:08:02.935296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e7031da3-8c4a-4913-8936-cceae3b4e7a5 · outbound

This paper cites Exploring Chemical Space with Score -based Out -of-distribution Generation.

NovoMolGen: Rethinking Molecular Language Model Pretraining Exploring Chemical Space with Score -based Out -of-distribution Generation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:17.274753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e8e635bd-422b-4b5c-b9cc-386c2907db1a · outbound

This paper cites Molecule Generation with Fragment Retrieval Augmentation.

NovoMolGen: Rethinking Molecular Language Model Pretraining Molecule Generation with Fragment Retrieval Augmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:16.909709Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 242a39aa-ddc7-43ec-af44-305da64d3836 · outbound

This paper cites Drug discovery with dynamic goal-aware fragments.

NovoMolGen: Rethinking Molecular Language Model Pretraining Drug discovery with dynamic goal-aware fragments

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:16.594884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 765262cf-5621-4129-a148-cae3ae1896b9 · outbound

This paper cites Limits to depth-efficiencies of self-attention.

NovoMolGen: Rethinking Molecular Language Model Pretraining Limits to depth-efficiencies of self-attention

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:16.194753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4101b0fe-cedb-4d97-b5db-639d58ccb17b · outbound

This paper cites Chemical reaction enhanced graph learning for molecule representation.

NovoMolGen: Rethinking Molecular Language Model Pretraining Chemical reaction enhanced graph learning for molecule representation

Reference 49

Resolution: verified exact; doi, observed 2026-08-05T19:08:02.549929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 07d418f5-0b3a-41a5-bc00-1f53e70141b1 · outbound

This paper cites GeomGCL : Geometric Graph Contrastive Learning for Molecular Property Prediction.

NovoMolGen: Rethinking Molecular Language Model Pretraining GeomGCL : Geometric Graph Contrastive Learning for Molecular Property Prediction

Reference 50

Resolution: verified exact; doi, observed 2026-08-05T19:08:02.295931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c2026c92-6d45-4a1b-8f09-bf090541d381 · outbound

This paper cites A 3D generative model for structure-based drug design.

NovoMolGen: Rethinking Molecular Language Model Pretraining A 3D generative model for structure-based drug design

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:15.844753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2444502f-fd5c-4df7-9877-c5ecd4301ed4 · outbound

This paper cites Masked graph modeling for molecule generation.

NovoMolGen: Rethinking Molecular Language Model Pretraining Masked graph modeling for molecule generation

Reference 52

Resolution: verified exact; doi, observed 2026-08-05T19:08:01.954922Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 13ce5048-e3e4-4a7a-9857-a71434881728 · outbound

This paper cites Provably Powerful Graph Networks.

NovoMolGen: Rethinking Molecular Language Model Pretraining Provably Powerful Graph Networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:15.464760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e1517adc-6704-4388-84b5-93343cb8f394 · outbound

This paper cites Molecule generation using transformers and policy gradient reinforcement learning.

NovoMolGen: Rethinking Molecular Language Model Pretraining Molecule generation using transformers and policy gradient reinforcement learning

Reference 54

Resolution: verified exact; doi, observed 2026-08-05T19:08:01.594013Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a74a20b6-06ee-4318-bcb9-cc3b88fd4c93 · outbound

This paper cites Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe.

NovoMolGen: Rethinking Molecular Language Model Pretraining Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:55.704864Z

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NovoMolGen: Rethinking Molecular Language Model Pretraining DeepSMILES : An Adaptation of SMILES for Use in Machine - Learning of Chemical Structures , September 2018

Reference 57

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This paper cites Molecular de-novo design through deep reinforcement learning.

NovoMolGen: Rethinking Molecular Language Model Pretraining Molecular de-novo design through deep reinforcement learning

Reference 58

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NovoMolGen: Rethinking Molecular Language Model Pretraining The jungle of generative drug discovery: Traps, treasures, and ways out

Reference 59

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NovoMolGen: Rethinking Molecular Language Model Pretraining A Deep Generative Model for Fragment - Based Molecule Generation

Reference 60

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This paper cites Molecular Sets ( MOSES ): A Benchmarking Platform for Molecular Generation Models.

NovoMolGen: Rethinking Molecular Language Model Pretraining Molecular Sets ( MOSES ): A Benchmarking Platform for Molecular Generation Models

Reference 61

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NovoMolGen: Rethinking Molecular Language Model Pretraining Extended- Connectivity Fingerprints

Reference 62

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NovoMolGen: Rethinking Molecular Language Model Pretraining Large-Scale Chemical Language Representations Capture Molecular Structure and Properties

Reference 63

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NovoMolGen: Rethinking Molecular Language Model Pretraining GP-MoLFormer: A Foundation Model For Molecular Generation

Reference 64

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NovoMolGen: Rethinking Molecular Language Model Pretraining Blum, and Jean-Louis Reymond

Reference 65

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NovoMolGen: Rethinking Molecular Language Model Pretraining Proximal Policy Optimization Algorithms

Reference 66

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NovoMolGen: Rethinking Molecular Language Model Pretraining Hunter, Costas Bekas, and Alpha A

Reference 67

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NovoMolGen: Rethinking Molecular Language Model Pretraining Neural Machine Translation of Rare Words with Subword Units

Reference 68

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NovoMolGen: Rethinking Molecular Language Model Pretraining GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders

Reference 69

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NovoMolGen: Rethinking Molecular Language Model Pretraining Chemical language models enable navigation in sparsely populated chemical space

Reference 70

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NovoMolGen: Rethinking Molecular Language Model Pretraining Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 71

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NovoMolGen: Rethinking Molecular Language Model Pretraining Molecular Fingerprints for Robust and Efficient ML-Driven Molecular Generation

Reference 72

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NovoMolGen: Rethinking Molecular Language Model Pretraining Augmented hill-climb increases reinforcement learning efficiency for language-based de novo molecule generation

Reference 73

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NovoMolGen: Rethinking Molecular Language Model Pretraining Promptsmiles: prompting for scaffold decoration and fragment linking in chemical language models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:13.355296Z

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NovoMolGen: Rethinking Molecular Language Model Pretraining Tingle, Khanh G

Reference 75

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This paper cites Genetic algorithms are strong baselines for molecule generation.

NovoMolGen: Rethinking Molecular Language Model Pretraining Genetic algorithms are strong baselines for molecule generation

Reference 76

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NovoMolGen: Rethinking Molecular Language Model Pretraining cMolGPT : A Conditional Generative Pre - Trained Transformer for Target - Specific De Novo Molecular Generation

Reference 77

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NovoMolGen: Rethinking Molecular Language Model Pretraining SMILES , a chemical language and information system

Reference 78

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NovoMolGen: Rethinking Molecular Language Model Pretraining Efficient multi-objective molecular optimization in a continuous latent space

Reference 79

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NovoMolGen: Rethinking Molecular Language Model Pretraining HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 80

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NovoMolGen: Rethinking Molecular Language Model Pretraining MoleculeNet: a benchmark for molecular machine learning

Reference 81

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This paper cites Graph neural networks for automated de novo drug design.

NovoMolGen: Rethinking Molecular Language Model Pretraining Graph neural networks for automated de novo drug design

Reference 82

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NovoMolGen: Rethinking Molecular Language Model Pretraining Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism

Reference 83

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This paper cites How Powerful are Graph Neural Networks ? In International Conference on Learning Representations, September 2018.

NovoMolGen: Rethinking Molecular Language Model Pretraining How Powerful are Graph Neural Networks ? In International Conference on Learning Representations, September 2018

Reference 84

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NovoMolGen: Rethinking Molecular Language Model Pretraining Powers, Ron O

Reference 85

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This paper cites Hit and lead discovery with explorative rl and fragment-based molecule generation.

NovoMolGen: Rethinking Molecular Language Model Pretraining Hit and lead discovery with explorative rl and fragment-based molecule generation

Reference 86

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This paper cites Hit and lead discovery with explorative RL and fragment-based molecule generation.

NovoMolGen: Rethinking Molecular Language Model Pretraining Hit and lead discovery with explorative RL and fragment-based molecule generation

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:12.084752Z

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Observation 078b14f4-0d01-45cd-992c-36cfff34f0e7 · outbound

This paper cites Llasmol: Advancing large language models for chemistry with a large-scale, comprehensive, high-quality instruction tuning dataset.

NovoMolGen: Rethinking Molecular Language Model Pretraining Llasmol: Advancing large language models for chemistry with a large-scale, comprehensive, high-quality instruction tuning dataset

Reference 88

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Observation a8e26dc6-a4bd-4294-875c-6c67182ecafc · outbound

This paper cites MoFlow : An Invertible Flow Model for Generating Molecular Graphs.

NovoMolGen: Rethinking Molecular Language Model Pretraining MoFlow : An Invertible Flow Model for Generating Molecular Graphs

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:59.384751Z

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NovoMolGen: Rethinking Molecular Language Model Pretraining Unresolved cited work

Reference 90

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This paper cites ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling.

NovoMolGen: Rethinking Molecular Language Model Pretraining ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-05T19:07:59.574745Z

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NovoMolGen: Rethinking Molecular Language Model Pretraining BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning

Reference 92

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Observation a8336139-c6f4-4bc9-88c3-1e22f7de96aa · outbound

This paper cites Uni- Mol : A Universal 3D Molecular Representation Learning Framework.

NovoMolGen: Rethinking Molecular Language Model Pretraining Uni- Mol : A Universal 3D Molecular Representation Learning Framework

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:08:11.454746Z

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Pith citing papers

Observation d09399db-bba3-4b79-9711-b7644b4857dd · inbound

FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization cites this paper.

FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization NovoMolGen: Rethinking Molecular Language Model Pretraining

Reference 37

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Observation 51b8db38-d6dc-46ae-88a1-b0c2030b7b81 · inbound

Augmenting Molecular Language Models with Local $n$-gram Memory cites this paper.

Augmenting Molecular Language Models with Local $n$-gram Memory NovoMolGen: Rethinking Molecular Language Model Pretraining

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