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molecular representation

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

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/xzenglab/resources-for-ddis-prediction-using-dl

공식 코드

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

https://github.com/yaoyao-yaoyao-cell/mssm-gnn

공식 코드

Molecular Graph Representation Learning via Structural Similarity Information

https://github.com/yikunpku/atomas

공식 코드

Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation

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

공식 코드

MUBen: Benchmarking the Uncertainty of Molecular Representation Models

https://github.com/yuewan2/molmcl

공식 코드

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

https://github.com/zabaras/GSVAE

공식 코드

Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder

https://github.com/zaixizhang/MGSSL

공식 코드

Graph-based Molecular Representation Learning

https://github.com/zengkaipeng/UAlign

공식 코드

UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignment

https://github.com/zhaoningyu1996/hm-gnn

공식 코드

Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks

https://github.com/zhaoningyu1996/motifpiece

공식 코드

MotifPiece: A Data-Driven Approach for Effective Motif Extraction and Molecular Representation Learning

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

공식 코드

Few-Shot Graph Learning for Molecular Property Prediction

https://github.com/zhousongh/hipm

공식 코드

Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property Prediction

https://github.com/BrightBlueCheese/Modeling-of-Time-varying-Wireless-Communication-Channel-with-Fading-and-Shadowing

The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA

https://github.com/MindCode-4/code-13/tree/main/riemann-noise

Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs

https://github.com/MindSpore-scientific/code-14/tree/main/riemann-noise

Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs

https://github.com/MindSpore-scientific/code-5/tree/main/riemann-noise

Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs

https://github.com/SylwiaNowakowska/LLM_Fine_Tuning_Molecular_Properties

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

https://github.com/XuHangkun/Compound-Protein-Interaction

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

https://github.com/anonymous20201002/chemprop

Analyzing Learned Molecular Representations for Property Prediction

https://github.com/bachnguyente/temporal-mgn

Multiresolution Equivariant Graph Variational Autoencoder

https://github.com/deepchem/deepchem

Self-Supervised Graph Transformer on Large-Scale Molecular Data

https://github.com/deepmodeling/Uni-Mol

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction

https://github.com/dptech-corp/Uni-Mol

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction

https://github.com/jbr-ai-labs/lipophilicity-prediction

Analyzing Learned Molecular Representations for Property Prediction

https://github.com/seyonechithrananda/selfies-mirror

Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation

https://github.com/tingweidaniel/chemprop_atomicfp_save_depth_final

Analyzing Learned Molecular Representations for Property Prediction

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