ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction | Paper Citation Record

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ChemCrow: Augmenting large-language models with chemistry tools cites this paper.

ChemCrow: Augmenting large-language models with chemistry tools ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 30

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Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks cites this paper.

Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 4

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Regression with Large Language Models for Materials and Molecular Property Prediction cites this paper.

Regression with Large Language Models for Materials and Molecular Property Prediction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 1

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NPGPT: Natural Product-Like Compound Generation with GPT-based Chemical Language Models cites this paper.

NPGPT: Natural Product-Like Compound Generation with GPT-based Chemical Language Models ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 18

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction cites this paper.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 11

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Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry cites this paper.

Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 161

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MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language Model cites this paper.

MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language Model ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 3

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SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision cites this paper.

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 2021

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M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery cites this paper.

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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Dual-Modality Representation Learning for Molecular Property Prediction cites this paper.

Dual-Modality Representation Learning for Molecular Property Prediction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 3

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Challenging reaction prediction models to generalize to novel chemistry cites this paper.

Challenging reaction prediction models to generalize to novel chemistry ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 2020

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Similarity-Quantized Relative Difference Learning for Improved Molecular Activity Prediction cites this paper.

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ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models cites this paper.

ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 5

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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases cites this paper.

From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 17

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ReactEmbed: A Plug-and-Play Module for Unifying Protein-Molecule Representations Guided by Biochemical Reaction Networks cites this paper.

ReactEmbed: A Plug-and-Play Module for Unifying Protein-Molecule Representations Guided by Biochemical Reaction Networks ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning cites this paper.

FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 1

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Docking-Aware Attention: Dynamic Protein Representations through Molecular Context Integration cites this paper.

Docking-Aware Attention: Dynamic Protein Representations through Molecular Context Integration ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 5

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Graph-based Molecular In-context Learning Grounded on Morgan Fingerprints cites this paper.

Graph-based Molecular In-context Learning Grounded on Morgan Fingerprints ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 2020

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Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language cites this paper.

Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 2020

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A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML cites this paper.

A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 54

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ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models cites this paper.

ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 71

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Polymer Data Challenges in the AI Era: Bridging Gaps for Next-Generation Energy Materials cites this paper.

Polymer Data Challenges in the AI Era: Bridging Gaps for Next-Generation Energy Materials ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 2020

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Transformers in Protein: A Survey cites this paper.

Transformers in Protein: A Survey ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 65

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Uncovering Bottlenecks and Optimizing Scientific Lab Workflows with Cycle Time Reduction Agents cites this paper.

Uncovering Bottlenecks and Optimizing Scientific Lab Workflows with Cycle Time Reduction Agents ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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SmellNet: A Large-scale Dataset for Real-world Smell Recognition cites this paper.

SmellNet: A Large-scale Dataset for Real-world Smell Recognition ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 9

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Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation cites this paper.

Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning cites this paper.

CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 44

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OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning cites this paper.

OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 28

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Federated Learning from Molecules to Processes: A Perspective cites this paper.

Federated Learning from Molecules to Processes: A Perspective ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 127

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A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools cites this paper.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 100

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DeepRetro: Retrosynthetic Pathway Discovery using Iterative LLM Reasoning cites this paper.

DeepRetro: Retrosynthetic Pathway Discovery using Iterative LLM Reasoning ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 35

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MODA: A Unified 3D Diffusion Framework for Multi-Task Target-Aware Molecular Generation cites this paper.

MODA: A Unified 3D Diffusion Framework for Multi-Task Target-Aware Molecular Generation ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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A Foundation Model for Material Fracture Prediction cites this paper.

A Foundation Model for Material Fracture Prediction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation cites this paper.

Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 39

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Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI cites this paper.

Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Molecular Machine Learning in Chemical Process Design cites this paper.

Molecular Machine Learning in Chemical Process Design ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 85

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Valid Property-Enhanced Contrastive Learning for Targeted Optimization & Resampling for Novel Drug Design cites this paper.

Valid Property-Enhanced Contrastive Learning for Targeted Optimization & Resampling for Novel Drug Design ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 62

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Towards a Physics Foundation Model cites this paper.

Towards a Physics Foundation Model ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Adaptive Minds: Empowering Agents with LoRA-as-Tools cites this paper.

Adaptive Minds: Empowering Agents with LoRA-as-Tools ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Foundation Models for Discovery and Exploration in Chemical Space cites this paper.

Foundation Models for Discovery and Exploration in Chemical Space ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 107

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FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics cites this paper.

FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 9

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SIGMA: Semantic Identifier Grouping for Molecular Autoregression cites this paper.

SIGMA: Semantic Identifier Grouping for Molecular Autoregression ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 2023

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NOSE: Neural Olfactory-Semantic Embedding with Tri-Modal Orthogonal Contrastive Learning cites this paper.

NOSE: Neural Olfactory-Semantic Embedding with Tri-Modal Orthogonal Contrastive Learning ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 11

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Lit2Vec: A Reproducible Workflow for Building a Legally Screened Chemistry Corpus from S2ORC for Downstream Retrieval and Text Mining cites this paper.

Lit2Vec: A Reproducible Workflow for Building a Legally Screened Chemistry Corpus from S2ORC for Downstream Retrieval and Text Mining ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 12

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When Active Learning Falls Short: An Empirical Study on Chemical Reaction Extraction cites this paper.

When Active Learning Falls Short: An Empirical Study on Chemical Reaction Extraction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 38

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Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction cites this paper.

Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Observation 9b677c4e-9e30-487a-8078-f58ae6f491fb · inbound

Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction cites this paper.

Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 7

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SPADE: Faster Drug Discovery by Learning from Sparse Data cites this paper.

SPADE: Faster Drug Discovery by Learning from Sparse Data ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 3

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Molecules Meet Language: Confound-Aware Representation Learning and Chemical Property Steering in Transformer-VAE Latent Spaces cites this paper.

Molecules Meet Language: Confound-Aware Representation Learning and Chemical Property Steering in Transformer-VAE Latent Spaces ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 12

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Molecules Meet Language: Confound-Aware Representation Learning and Chemical Property Steering in Transformer-VAE Latent Spaces cites this paper.

Molecules Meet Language: Confound-Aware Representation Learning and Chemical Property Steering in Transformer-VAE Latent Spaces ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 13

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Can LLMs Predict Polymer Physics Just by Reading Synthesis and Processing Prose? cites this paper.

Can LLMs Predict Polymer Physics Just by Reading Synthesis and Processing Prose? ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 5

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Observation 5e00b309-9d2e-4aef-a264-6113d8a61947 · inbound

From Syntax to Semantics: Unveiling the Emergence of Chirality in SMILES Translation Models cites this paper.

From Syntax to Semantics: Unveiling the Emergence of Chirality in SMILES Translation Models ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 11

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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 ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction cites this paper.

Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Observation 465d15f9-a395-4df9-b2c8-68d24adc6130 · inbound

Training distribution determines the ceiling of drug-blind cancer sensitivity prediction cites this paper.

Training distribution determines the ceiling of drug-blind cancer sensitivity prediction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Observation d37fc5af-fe83-4e85-a6f5-856e787487b1 · inbound

AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation cites this paper.

AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Observation d7d08f91-834c-4184-89ef-c1a32f4a359f · inbound

When Tabular Foundation Models Transfer Across Modalities: A Systematic Evaluation Across 95 Datasets, 7 Modalities, and Two Regimes cites this paper.

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Observation cb5f82c4-c20d-40af-80a4-0eae626966c0 · inbound

MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry cites this paper.

MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 46

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Observation 27fc41f3-28b9-4722-88dc-9dd0b730bba5 · inbound

GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction cites this paper.

GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Observation d30a2ed4-9474-48ad-aff4-25a8b67da997 · inbound

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

Augmenting Molecular Language Models with Local $n$-gram Memory ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Observation a8aab07c-0ebe-49ab-97c0-3af518ac75cf · inbound

Contextualizing Biological Language Models across Modalities via Logit-Space Contrastive Alignment cites this paper.

Contextualizing Biological Language Models across Modalities via Logit-Space Contrastive Alignment ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 43

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Observation 0beb2121-f24c-402a-8c45-348c1e5453a1 · inbound

Contextualizing Biological Language Models across Modalities via Logit-Space Contrastive Alignment cites this paper.

Contextualizing Biological Language Models across Modalities via Logit-Space Contrastive Alignment ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 44

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Observation 98e78bc2-1d88-4b81-863d-16a9dd7ab1d1 · inbound

A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysis cites this paper.

A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysis ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Observation 5a4e5e17-b837-40b4-9312-193e9f024126 · inbound

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements cites this paper.

Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Resolution: verified exact; arxiv_id, observed 2026-07-04T09:59:45.791091Z

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Observation 144364f9-2c4f-4758-b2fc-cd373d4f4e31 · inbound

What Does a Chemical Language Model Know About Molecules? cites this paper.

What Does a Chemical Language Model Know About Molecules? ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Resolution: verified exact; arxiv_id, observed 2026-07-04T09:49:44.741796Z

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Observation d9c14e0c-1be4-4b19-a8ff-f69296b700ca · inbound

Towards Generalizable and Evidential Nuclear Magnetic Resonance-Based Molecular Structure Elucidation via Large Language Model Agent cites this paper.

Towards Generalizable and Evidential Nuclear Magnetic Resonance-Based Molecular Structure Elucidation via Large Language Model Agent ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Observation 9614f837-fb01-4273-8333-eb380e47ac58 · inbound

Modeling Cell-Cycle-Aware Single-Cell Drug Perturbation Responses cites this paper.

Modeling Cell-Cycle-Aware Single-Cell Drug Perturbation Responses ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Resolution: verified exact; arxiv_id, observed 2026-07-01T12:35:44.093689Z

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Observation 06ba9ae0-a941-4787-8afb-cf44a0369c13 · inbound

Probing Chemical Language Models: Effects of Pre-training and Fine-tuning cites this paper.

Probing Chemical Language Models: Effects of Pre-training and Fine-tuning ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:18:43.065942Z

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Observation ff31abc1-a090-47b4-8f6c-20553f5e6792 · inbound

Multimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES Embeddings cites this paper.

Multimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES Embeddings ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 15

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Observation 4e3c232d-2942-44ef-8295-1b43c68ef264 · inbound

A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It cites this paper.

A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Resolution: verified exact; local_arxiv, observed 2026-07-11T02:07:43.444303Z

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Observation 429126b3-9497-4c5c-b132-890d30dab33d · inbound

Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools cites this paper.

Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 24

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Observation 6a9bf859-3c94-42c2-9418-349d795a6edc · inbound

Trustworthy Protein-Ligand Binding Affinity Prediction via Reliability-Aware Multi-Engine Fusion cites this paper.

Trustworthy Protein-Ligand Binding Affinity Prediction via Reliability-Aware Multi-Engine Fusion ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 8

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Observation d502ff06-32b3-4bb3-9bc2-f8334b22f5a5 · inbound

ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction cites this paper.

ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Observation 593dc983-43f6-4cd3-9796-e58ce5e7ab2c · inbound

OLEDLM: A Unified Language Model for OLED Molecular Design cites this paper.

OLEDLM: A Unified Language Model for OLED Molecular Design ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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MS-GPT: Rethinking MS/MS De Novo Structure Elucidation as Spectrum-Induced Posterior Querying of a Molecule-Language Model cites this paper.

MS-GPT: Rethinking MS/MS De Novo Structure Elucidation as Spectrum-Induced Posterior Querying of a Molecule-Language Model ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Observation 052a68bc-b7ab-4127-a2b1-6086d5d31119 · inbound

Beyond Predictive Accuracy: A Reliability-Aware Audit of Molecular Representations for Human Olfaction cites this paper.

Beyond Predictive Accuracy: A Reliability-Aware Audit of Molecular Representations for Human Olfaction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Observation 9cbf7b27-3dd6-414c-ae4d-cc9a5aa7f654 · inbound

Persistent Manifold Learning of Protein Properties cites this paper.

Persistent Manifold Learning of Protein Properties ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction cites this paper.

Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Resolution: unresolved; no resolver link, observed 2026-07-31T18:32:14.122104Z

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ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density cites this paper.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language cites this paper.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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Multi-Granular Rationale-Guided Molecular LLM for Property Prediction cites this paper.

Multi-Granular Rationale-Guided Molecular LLM for Property Prediction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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