M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery | Paper Citation Record

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This paper cites GPT-4 Technical Report.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery GPT-4 Technical Report

Reference 1

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This paper cites Extracting structured data from organic synthesis procedures using a fine-tuned large language model.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Extracting structured data from organic synthesis procedures using a fine-tuned large language model

Reference 2

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This paper cites Geom, energy-annotated molecular conformations for property prediction and molecular generation.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Geom, energy-annotated molecular conformations for property prediction and molecular generation

Reference 3

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This paper cites New substructure filters for removal of pan assay interference compounds (pains) from screening libraries and for their exclusion in bioassays.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery New substructure filters for removal of pan assay interference compounds (pains) from screening libraries and for their exclusion in bioassays

Reference 4

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This paper cites Molgpt: molecular generation using a transformer-decoder model.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Molgpt: molecular generation using a transformer-decoder model

Reference 5

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This paper cites Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Reference 6

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This paper cites The properties of known drugs.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery The properties of known drugs

Reference 7

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This paper cites Language models are few-shot learners.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Language models are few-shot learners

Reference 8

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This paper cites Artificial intelligence for drug discovery: Resources, methods, and applications.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Artificial intelligence for drug discovery: Resources, methods, and applications

Reference 9

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This paper cites ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 10

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This paper cites Molecular representations in ai-driven drug discovery: a review and practical guide.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Molecular representations in ai-driven drug discovery: a review and practical guide

Reference 11

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This paper cites On the art of compiling and using’drug-like’chemical fragment spaces.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery On the art of compiling and using’drug-like’chemical fragment spaces

Reference 12

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This paper cites Glm: General language model pretraining with autoregressive blank infilling.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Glm: General language model pretraining with autoregressive blank infilling

Reference 13

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This paper cites The Llama 3 Herd of Models.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery The Llama 3 Herd of Models

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This paper cites Molecular structure input on the web.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Molecular structure input on the web

Reference 15

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This paper cites Molecular representation learning with language models and domain-relevant auxiliary tasks.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Molecular representation learning with language models and domain-relevant auxiliary tasks

Reference 16

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M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Unresolved cited work

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This paper cites Probabilistic transformer: Modelling ambiguities and distributions for rna folding and molecule design.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Probabilistic transformer: Modelling ambiguities and distributions for rna folding and molecule design

Reference 18

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This paper cites Admetlab 3.0: an updated comprehensive online admet prediction platform enhanced with broader coverage, improved performance, api functionality and decision support.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Admetlab 3.0: an updated comprehensive online admet prediction platform enhanced with broader coverage, improved performance, api functionality and decision support

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This paper cites Sample efficiency matters: a benchmark for practical molecular optimization.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Sample efficiency matters: a benchmark for practical molecular optimization

Reference 20

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This paper cites Chembl: a large-scale bioactivity database for drug discovery.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Chembl: a large-scale bioactivity database for drug discovery

Reference 21

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This paper cites Prefix-tree decoding for predicting mass spectra from molecules.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Prefix-tree decoding for predicting mass spectra from molecules

Reference 22

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This paper cites Diffusing on two levels and optimizing for multiple properties: A novel approach to generating molecules with desirable properties.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Diffusing on two levels and optimizing for multiple properties: A novel approach to generating molecules with desirable properties

Reference 23

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This paper cites What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks

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M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Unresolved cited work

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery LoRA: Low-Rank Adaptation of Large Language Models

Reference 26

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This paper cites Principles of early drug discovery.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Principles of early drug discovery

Reference 27

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This paper cites Zinc- a free database of commercially available compounds for virtual screening.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Zinc- a free database of commercially available compounds for virtual screening

Reference 28

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This paper cites Comprehensive assessment of nine target prediction web services: which should we choose for target fishing? Briefings in Bioinformatics, 24(2):bbad014, 2023.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Comprehensive assessment of nine target prediction web services: which should we choose for target fishing? Briefings in Bioinformatics, 24(2):bbad014, 2023

Reference 29

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M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Pubchem substance and compound databases

Reference 30

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M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Molecule generation by principal subgraph mining and assembling

Reference 31

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This paper cites Natural questions: a benchmark for question answering research.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Natural questions: a benchmark for question answering research

Reference 32

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This paper cites Rdkit documentation.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Rdkit documentation

Reference 33

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This paper cites Effective drug–target interaction prediction with mutual interaction neural network.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Effective drug–target interaction prediction with mutual interaction neural network

Reference 34

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This paper cites Deep learning methods for molecular representation and property prediction.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Deep learning methods for molecular representation and property prediction

Reference 35

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This paper cites Git- mol: A multi-modal large language model for molecular science with graph, image, and text.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Git- mol: A multi-modal large language model for molecular science with graph, image, and text

Reference 36

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This paper cites A quantitative analysis of knowledge-learning preferences in large language models in molecular science.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery A quantitative analysis of knowledge-learning preferences in large language models in molecular science

Reference 37

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This paper cites A group symmetric stochastic differential equation model for molecule multi-modal pretraining.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery A group symmetric stochastic differential equation model for molecule multi-modal pretraining

Reference 38

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This paper cites Multi-modal molecule structure–text model for text-based retrieval and editing.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Multi-modal molecule structure–text model for text-based retrieval and editing

Reference 39

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This paper cites Dynamicbind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Dynamicbind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model

Reference 40

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This paper cites GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction

Reference 41

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M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery The Natural Language Decathlon: Multitask Learning as Question Answering

Reference 42

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This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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This paper cites Open babel: An open chemical toolbox.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Open babel: An open chemical toolbox

Reference 44

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This paper cites Training language models to follow instructions with human feedback.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Training language models to follow instructions with human feedback

Reference 45

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This paper cites Molecular sets (moses): a benchmarking platform for molecular generation models.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Molecular sets (moses): a benchmarking platform for molecular generation models

Reference 46

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This paper cites A Call for Clarity in Reporting BLEU Scores.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery A Call for Clarity in Reporting BLEU Scores

Reference 47

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This paper cites Quantum chemistry structures and properties of 134 kilo molecules.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Quantum chemistry structures and properties of 134 kilo molecules

Reference 48

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Observation 61ba9ff8-b6a9-452e-b6ea-31ed34c468f7 · outbound

This paper cites Self-supervised graph transformer on large-scale molecular data.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Self-supervised graph transformer on large-scale molecular data

Reference 49

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Observation e0f1d78f-a1f0-4144-946f-d3fbb3fb7c87 · outbound

This paper cites Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17

Reference 50

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Observation dea4ce02-d5a9-41c0-abf2-d120a41abfb7 · outbound

This paper cites A comprehensive map of molecular drug targets.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery A comprehensive map of molecular drug targets

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:23:45.064868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 54c61ef4-6e38-44fe-b54e-f7a1e7db5ae7 · outbound

This paper cites Equivariant flow matching with hybrid probability transport for 3d molecule generation.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Equivariant flow matching with hybrid probability transport for 3d molecule generation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T20:23:44.627447Z

Source-reported events for the cited work

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Observation 64e0b337-53db-4f52-aed0-59a572294ecf · outbound

This paper cites Evidence-based absorption, distribution, metabolism, excretion (adme) and its interplay with alternative toxicity methods.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Evidence-based absorption, distribution, metabolism, excretion (adme) and its interplay with alternative toxicity methods

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:23:45.040899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f6d4ca9e-25f1-440d-a945-f02d77584ce3 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T20:23:44.635762Z

Source-reported events for the cited work

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Observation aecb832e-a49c-447d-a200-4cd90ae35831 · outbound

This paper cites Deep learning approaches for de novo drug design: An overview.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Deep learning approaches for de novo drug design: An overview

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:23:45.026238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cacbdd2e-2d84-4e15-801e-dd8fcbb64373 · outbound

This paper cites Multitask joint strategies of self- supervised representation learning on biomedical networks for drug discovery.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Multitask joint strategies of self- supervised representation learning on biomedical networks for drug discovery

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:23:45.011938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 820a7390-bed4-4b94-8261-a18a73d3c97d · outbound

This paper cites MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:23:44.721154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 49787b15-2cd1-409a-9e6c-2db199eccf78 · outbound

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

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Moleculenet: a benchmark for molecular machine learning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:23:44.998084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 82e11962-525f-4820-a242-e8aebac133ff · outbound

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

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Hit and lead discovery with explorative rl and fragment-based molecule generation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:23:44.984715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 78188a9b-8257-4aaa-b1d1-ce8cf87a8468 · outbound

This paper cites Quandb: a quantum chemical property database towards enhancing 3d molecular representation learning.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Quandb: a quantum chemical property database towards enhancing 3d molecular representation learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:23:44.969962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ff217361-f230-4c9e-9109-eb2ccbf8b6d9 · outbound

This paper cites Drugassist: A large language model for molecule optimization.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Drugassist: A large language model for molecule optimization

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:23:44.955140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 77d442c8-e3be-4e62-9325-205d4e5a274d · outbound

This paper cites A unified drug–target interaction prediction framework based on knowledge graph and recommendation system.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery A unified drug–target interaction prediction framework based on knowledge graph and recommendation system

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:23:44.941330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1ba6f37b-9012-4b37-8d24-930721ef07cd · outbound

This paper cites Multimodal molecular pretraining via modality blending.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Multimodal molecular pretraining via modality blending

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:23:44.926749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 85fd23a8-12c8-49d1-902c-8a6fb97c0548 · outbound

This paper cites Moflow: an invertible flow model for generating molecular graphs.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Moflow: an invertible flow model for generating molecular graphs

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:23:44.913107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1cf5ca1f-428b-49a2-9119-4089a62eb854 · outbound

This paper cites A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:23:44.898807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Reference resolution

Outbound references

Pith citing papers