Papers with Code 아카이브

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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

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보존된 평가 기록

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코드

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

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

공식 코드

Self-Supervised Graph Transformer on Large-Scale Molecular Data

https://github.com/teslacool/unifiedmolpretrain

공식 코드

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

https://github.com/thilinicooray/deepgcfx

공식 코드

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

https://github.com/tkipf/pygcn

공식 코드

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/tongzhugroup/mddatasetbuilder

공식 코드

Neural Network Based in Silico Simulation of Combustion Reactions

https://github.com/torchmd/torchmd-net

공식 코드

TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials

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

공식 코드

Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction

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

공식 코드

Transformers for molecular property prediction: Domain adaptation efficiently improves performance

https://github.com/uhlerlab/infocore

공식 코드

Removing Biases from Molecular Representations via Information Maximization

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

공식 코드

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

https://github.com/usccolumbia/materialsuq

공식 코드

Materials Property Prediction with Uncertainty Quantification: A Benchmark Study

https://github.com/usccolumbia/tsdnn

공식 코드

Semi-supervised teacher-student deep neural network for materials discovery

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

공식 코드

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

https://github.com/vishaldeyiiest/graphta

공식 코드

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

https://github.com/volkamerlab/transformers4mpp_review

공식 코드

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

https://github.com/vsomnath/graph_space_gps

공식 코드

Isotropic Gaussian Processes on Finite Spaces of Graphs

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

공식 코드

How Powerful are Graph Neural Networks?

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

공식 코드

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

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

공식 코드

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

https://github.com/xiaocui3737/geomclip

공식 코드

GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules

https://github.com/xqh19970407/invdesflow-al

공식 코드

InvDesFlow-AL: Active Learning-based Workflow for Inverse Design of Functional Materials

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

공식 코드

Towards Faster and More Compact Foundation Models for Molecular Property Prediction

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

공식 코드

Large-Scale Knowledge Integration for Enhanced Molecular Property Prediction

https://github.com/yifeiwang15/acml

공식 코드

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

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

공식 코드

MUBen: Benchmarking the Uncertainty of Molecular Representation Models

https://github.com/yinhanhe123/new_LLM4GNNExplanation

공식 코드

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

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

공식 코드

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

https://github.com/yuanqidu/leftnet

공식 코드

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

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

공식 코드

An open unified deep graph learning framework for discovering drug leads

https://github.com/yuewan2/molmcl

공식 코드

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

https://github.com/yuyangw/MolCLR

공식 코드

Molecular Contrastive Learning of Representations via Graph Neural Networks

https://github.com/yyt-2378/automat

공식 코드

AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use

https://github.com/zaixizhang/MGSSL

공식 코드

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

https://github.com/zetayue/MXMNet

공식 코드

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures

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

공식 코드

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

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

공식 코드

Few-Shot Graph Learning for Molecular Property Prediction

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.