ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models | Paper Citation Record

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This paper cites Interpretable bilinear attention network with domain adaptation improves drug–target prediction.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Interpretable bilinear attention network with domain adaptation improves drug–target prediction

Reference 1

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This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 2

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This paper cites Unifying Molecular and Textual Representations via Multi-task Language Modelling.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Unifying Molecular and Textual Representations via Multi-task Language Modelling

Reference 3

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This paper cites Unifying molecular and textual representations via multi-task language modelling.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Unifying molecular and textual representations via multi-task language modelling

Reference 4

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This paper cites Group contribution and machine learning approaches to predict abraham so- lute parameters, solvation free energy, and solvation enthalpy.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Group contribution and machine learning approaches to predict abraham so- lute parameters, solvation free energy, and solvation enthalpy

Reference 5

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This paper cites Se (3) equivariant graph neural networks with complete local frames.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Se (3) equivariant graph neural networks with complete local frames

Reference 6

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This paper cites Mmgnn: A molecular merged graph neural network for explainable solvation free energy prediction.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Mmgnn: A molecular merged graph neural network for explainable solvation free energy prediction

Reference 7

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This paper cites A new perspective on building efficient and expressive 3d equivariant graph neural networks.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models A new perspective on building efficient and expressive 3d equivariant graph neural networks

Reference 8

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Observation c87ec0c9-32ae-4b66-91d2-77c67f0e75cc · outbound

This paper cites Convolutional Networks on Graphs for Learning Molecular Fingerprints.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Convolutional Networks on Graphs for Learning Molecular Fingerprints

Reference 9

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This paper cites Translation between Molecules and Natural Language.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Translation between Molecules and Natural Language

Reference 11

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This paper cites Text2mol: Cross-modal molecule retrieval with natural language queries.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Text2mol: Cross-modal molecule retrieval with natural language queries

Reference 12

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Observation d7e44263-d70b-4a2f-9889-49d138eee4c4 · outbound

This paper cites MolTC: Towards Molecular Relational Modeling In Language Models.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models MolTC: Towards Molecular Relational Modeling In Language Models

Reference 13

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This paper cites Core: Automatic molecule optimization using copy & refine strategy.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Core: Automatic molecule optimization using copy & refine strategy

Reference 14

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This paper cites Se (3)-transformers: 3d roto-translation equivariant attention networks.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Se (3)-transformers: 3d roto-translation equivariant attention networks

Reference 15

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This paper cites Gemnet: Universal directional graph neural networks for molecules.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Gemnet: Universal directional graph neural networks for molecules

Reference 16

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This paper cites Directional Message Passing for Molecular Graphs.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Directional Message Passing for Molecular Graphs

Reference 17

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This paper cites Neural message passing for quantum chemistry.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Neural message passing for quantum chemistry

Reference 18

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This paper cites Mathematical correlations for describing solute transfer into functionalized alkane solvents containing hydroxyl, ether, ester or ketone solvents.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Mathematical correlations for describing solute transfer into functionalized alkane solvents containing hydroxyl, ether, ester or ketone solvents

Reference 19

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This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 20

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This paper cites Inductive representation learning on large graphs.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Inductive representation learning on large graphs

Reference 21

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This paper cites Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik

Reference 22

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This paper cites Deeppurpose: A deep learning library for drug–target interaction prediction.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Deeppurpose: A deep learning library for drug–target interaction prediction

Reference 23

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This paper cites Moltrans: Molecular interaction transformer for drug–target interaction prediction.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Moltrans: Molecular interaction transformer for drug–target interaction prediction

Reference 24

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This paper cites Prediction of protein–protein interaction using graph neural networks.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Prediction of protein–protein interaction using graph neural networks

Reference 25

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This paper cites Learning from protein structure with geometric vector perceptrons.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Learning from protein structure with geometric vector perceptrons

Reference 26

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This paper cites Experimental database of optical properties of organic compounds.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Experimental database of optical properties of organic compounds

Reference 27

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This paper cites Shoemaker, Paul A.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Shoemaker, Paul A

Reference 28

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This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Semi-Supervised Classification with Graph Convolutional Networks

Reference 29

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This paper cites Kipf and Max Welling.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Kipf and Max Welling

Reference 30

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This paper cites Selfies and the future of molecular string representations.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Selfies and the future of molecular string representations

Reference 31

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This paper cites Na, Sungwon Kim, Junseok Lee, and Chanyoung Park.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Na, Sungwon Kim, Junseok Lee, and Chanyoung Park

Reference 32

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This paper cites Conditional Graph Information Bottleneck for Molecular Relational Learning.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Conditional Graph Information Bottleneck for Molecular Relational Learning

Reference 33

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This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 34

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This paper cites 3d-molm: Towards 3d molecule-text interpretation in language models.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models 3d-molm: Towards 3d molecule-text interpretation in language models

Reference 35

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This paper cites DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs

Reference 36

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This paper cites Bilinear cnn models for fine-grained visual recognition.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Bilinear cnn models for fine-grained visual recognition

Reference 37

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This paper cites Kgnn: Knowledge graph neural network for drug-drug interaction prediction.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Kgnn: Knowledge graph neural network for drug-drug interaction prediction

Reference 38

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This paper cites Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

Reference 39

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This paper cites Spherical message passing for 3d molecular graphs.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Spherical message passing for 3d molecular graphs

Reference 40

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This paper cites MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter

Reference 41

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This paper cites An autoregressive flow model for 3d molecular geometry generation from scratch.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models An autoregressive flow model for 3d molecular geometry generation from scratch

Reference 42

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This paper cites Min- nesota solvation database (mnsol) version 2012.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Min- nesota solvation database (mnsol) version 2012

Reference 43

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This paper cites Freesolv: a database of experimental and calculated hydration free energies, with input files.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Freesolv: a database of experimental and calculated hydration free energies, with input files

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:35.449092Z

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This paper cites Estimation of solvation quantities from experimental thermodynamic data: Development of the comprehensive compsol databank for pure and mixed solutes.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Estimation of solvation quantities from experimental thermodynamic data: Development of the comprehensive compsol databank for pure and mixed solutes

Reference 45

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This paper cites Graphdta: Predicting drug–target binding affinity with graph neural networks.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Graphdta: Predicting drug–target binding affinity with graph neural networks

Reference 46

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This paper cites Extracting protein- protein interactions (ppis) from biomedical literature using attention-based relational context information.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Extracting protein- protein interactions (ppis) from biomedical literature using attention-based relational context information

Reference 47

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This paper cites BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations

Reference 48

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This paper cites Mcl-dti: Using drug multimodal information and bi-directional cross-attention learning method for predicting drug–target interaction.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Mcl-dti: Using drug multimodal information and bi-directional cross-attention learning method for predicting drug–target interaction

Reference 49

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This paper cites Gated fusion network for single image dehazing.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Gated fusion network for single image dehazing

Reference 50

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This paper cites Considerations for drug interactions on qtc in exploratory covid-19 treatment.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Considerations for drug interactions on qtc in exploratory covid-19 treatment

Reference 51

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This paper cites Deep learning improves prediction of drug–drug and drug–food interactions.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Deep learning improves prediction of drug–drug and drug–food interactions

Reference 52

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Observation a4df42c8-fb3b-4dc7-9d0d-0c4bb07ecc88 · outbound

This paper cites ReactionT5: a large-scale pre-trained model towards application of limited reaction data.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models ReactionT5: a large-scale pre-trained model towards application of limited reaction data

Reference 53

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ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models E (n) equivariant graph neural networks

Reference 54

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ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Equivariant message passing for the prediction of tensorial properties and molecular spectra

Reference 55

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This paper cites Schnet: A continuous-filter convolutional neural net- work for modeling quantum interactions.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Schnet: A continuous-filter convolutional neural net- work for modeling quantum interactions

Reference 56

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This paper cites Flexmol: A flexible toolkit for benchmarking molecular relational learning.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Flexmol: A flexible toolkit for benchmarking molecular relational learning

Reference 57

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ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Gerstein

Reference 58

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This paper cites Data-driven prediction of drug effects and interactions.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Data-driven prediction of drug effects and interactions

Reference 59

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ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Galactica: A Large Language Model for Science

Reference 60

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ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 61

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This paper cites Origins of complex solvent effects on chemical reactivity and computational tools to investigate them: a review.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Origins of complex solvent effects on chemical reactivity and computational tools to investigate them: a review

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:33.711366Z

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This paper cites Attention is all you need.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Attention is all you need

Reference 63

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ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Graph attention networks

Reference 64

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This paper cites Transfer learning for solvation free energies: From quantum chemistry to experiments.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Transfer learning for solvation free energies: From quantum chemistry to experiments

Reference 65

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This paper cites 3dprotdta: A deep learning model for drug-target affinity prediction based on residue-level protein graphs.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models 3dprotdta: A deep learning model for drug-target affinity prediction based on residue-level protein graphs

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:33.257434Z

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This paper cites Pre-training of equivariant graph matching networks with conformation flexibility for drug binding.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Pre-training of equivariant graph matching networks with conformation flexibility for drug binding

Reference 67

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This paper cites Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism

Reference 68

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This paper cites How powerful are graph neural networks? In Proceedings of the International Conference on Learning Representations (ICLR), 2019.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models How powerful are graph neural networks? In Proceedings of the International Conference on Learning Representations (ICLR), 2019

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:32.778444Z

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ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Representation learning on graphs with jumping knowledge networks

Reference 70

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This paper cites Attentionsitedti: An interpretable graph-based model for drug-target interaction prediction using nlp sentence-level relation classification.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Attentionsitedti: An interpretable graph-based model for drug-target interaction prediction using nlp sentence-level relation classification

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:32.487588Z

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Observation 1bbc4a61-92be-42b2-8480-7b6ae8ea94fc · outbound

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

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:32.355514Z

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ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Unresolved cited work

Reference 73

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This paper cites Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data

Reference 74

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Observation 50d17c8b-b306-4c6b-b28b-c64fefdc8bc5 · outbound

This paper cites Coatgin: Marrying convolution and attention for graph-based molecule property prediction.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Coatgin: Marrying convolution and attention for graph-based molecule property prediction

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:31.713411Z

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Observation d9195ab8-448c-48b3-afc9-9c4f3f9a9ec4 · outbound

This paper cites Protein representation learning by geometric structure pretraining.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Protein representation learning by geometric structure pretraining

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:31.459934Z

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Observation 792550ad-6834-48be-8d5e-12f66bc2a7b6 · outbound

This paper cites Graph-augmented Convolutional Networks on Drug-Drug Interactions Prediction.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Graph-augmented Convolutional Networks on Drug-Drug Interactions Prediction

Reference 77

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Observation 3c3278ed-3c7a-439c-9f37-3e5df401a06d · outbound

This paper cites Learning motif-based graphs for drug–drug interaction prediction via local–global self-attention.Nature Machine Intelligence, 6:1094–1105, 2024.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Learning motif-based graphs for drug–drug interaction prediction via local–global self-attention.Nature Machine Intelligence, 6:1094–1105, 2024

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:31.242376Z

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Observation 5a8edd4f-cd15-4206-9f4e-51f8a9f8f9ae · outbound

This paper cites Uni-mol: a universal 3d molecular representation learning framework.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Uni-mol: a universal 3d molecular representation learning framework

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:31.082420Z

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Observation 32e41aa4-0896-402b-b592-fc0f3dcff84b · outbound

This paper cites Uni-mol: A universal 3d molecular representation learning framework.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Uni-mol: A universal 3d molecular representation learning framework

Reference 80

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Observation 58ca6935-9625-45a0-b3c1-cee91f7fad14 · outbound

This paper cites A self-attention–based neural network for three-dimensional multivariate modeling and its skillful enso predictions.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models A self-attention–based neural network for three-dimensional multivariate modeling and its skillful enso predictions

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:30.438206Z

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Observation 3e1bda22-f295-470d-9707-7955826df336 · outbound

This paper cites Datadta: A multi- feature and dual-interaction aggregation framework for drug–target binding affinity prediction.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Datadta: A multi- feature and dual-interaction aggregation framework for drug–target binding affinity prediction

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:30.197058Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ea6b493a-71ae-4801-b5bb-395d77a0a2dd · outbound

This paper cites root": "data/DDI/DeepDDI/.

ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models root": "data/DDI/DeepDDI/

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:29.952090Z

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