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Molecular Property Prediction

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하위 태스크: 4
평가표: 18
보존된 평가 기록: 224

Molecular property prediction is the task of predicting the properties of a molecule from its structure.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/smu-tao-group/ADMET_XGBoost

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Accurate ADMET Prediction with XGBoost

https://github.com/snap-stanford/pretrain-gnns/

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Strategies for Pre-training Graph Neural Networks

https://github.com/snudatalab/AugWard

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AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification

https://github.com/sshaghayeghs/llama-vs-chatgpt

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Can Large Language Models Understand Molecules?

https://github.com/sshaghayeghs/llama-vs-gpt

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Can Large Language Models Understand Molecules?

https://github.com/swansonk14/chemprop

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Analyzing Learned Molecular Representations for Property Prediction

https://github.com/tech-srl/how_attentive_are_gats

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How Attentive are Graph Attention Networks?

https://github.com/tencent-ailab/grover

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Self-Supervised Graph Transformer on Large-Scale Molecular Data

https://github.com/teslacool/unifiedmolpretrain

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Unified 2D and 3D Pre-Training of Molecular Representations

https://github.com/thilinicooray/deepgcfx

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Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning

https://github.com/tkipf/pygcn

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Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/tsa87/semi-jtvae

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Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction

https://github.com/uds-lsv/domain-adaptation-molecular-transformers

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Transformers for molecular property prediction: Domain adaptation efficiently improves performance

https://github.com/uhlerlab/infocore

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Removing Biases from Molecular Representations via Information Maximization

https://github.com/ur-whitelab/mol.dev

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Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks

https://github.com/vencent-won/sggrl

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Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry

https://github.com/vishaldeyiiest/graphta

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Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific Adaptation

https://github.com/volkamerlab/transformers4mpp_review

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Transformers for molecular property prediction: Lessons learned from the past five years

https://github.com/vsomnath/graph_space_gps

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Isotropic Gaussian Processes on Finite Spaces of Graphs

https://github.com/weihua916/powerful-gnns

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How Powerful are Graph Neural Networks?

https://github.com/wz-create/batmannet

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BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular Representation

https://github.com/xian-sh/mol-mamba

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MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights

https://github.com/xiaocui3737/geomclip

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GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules

https://github.com/yasir-ghunaim/efficient-jmp

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Towards Faster and More Compact Foundation Models for Molecular Property Prediction

https://github.com/yasir-ghunaim/kano-chebi

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Large-Scale Knowledge Integration for Enhanced Molecular Property Prediction

https://github.com/yifeiwang15/acml

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Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning

https://github.com/yinghao-li/muben

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MUBen: Benchmarking the Uncertainty of Molecular Representation Models

https://github.com/yinhanhe123/new_LLM4GNNExplanation

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Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction

https://github.com/youjibiying/gtot-tuning

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Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport

https://github.com/yuanqidu/leftnet

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A new perspective on building efficient and expressive 3D equivariant graph neural networks

https://github.com/yueming-yin/gafse

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An open unified deep graph learning framework for discovering drug leads

https://github.com/yuewan2/molmcl

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Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation

https://github.com/yuyangw/MolCLR

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Molecular Contrastive Learning of Representations via Graph Neural Networks

https://github.com/zaixizhang/MGSSL

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Motif-based Graph Self-Supervised Learning for Molecular Property Prediction

https://github.com/zhangtia16/molgraph-lardo

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Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small Models

https://github.com/zhichunguo/Meta-MGNN

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Few-Shot Graph Learning for Molecular Property Prediction

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