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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/hubiodatalab/selformer

공식 코드

SELFormer: Molecular Representation Learning via SELFIES Language Models

https://github.com/hytruongson/mgvae

공식 코드

Multiresolution Equivariant Graph Variational Autoencoder

https://github.com/ibm/molformer

공식 코드

Large-Scale Chemical Language Representations Capture Molecular Structure and Properties

https://github.com/janweinreich/eml

공식 코드

Encrypted machine learning of molecular quantum properties

https://github.com/jcchan23/CoMPT

공식 코드

Learning Attributed Graph Representations with Communicative Message Passing Transformer

https://github.com/jiaor17/3D-MGP

공식 코드

Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs

https://github.com/jiaor17/3d-emgp

공식 코드

Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs

https://github.com/juanniwu/t-smiles

공식 코드

t-SMILES: A Scalable Fragment-based Molecular Representation Framework for De Novo Molecule Generation

https://github.com/junxia97/awesome-pretrain-on-molecules

공식 코드

A Systematic Survey of Chemical Pre-trained Models

https://github.com/junxia97/mole-bert

공식 코드

A Systematic Survey of Chemical Pre-trained Models

https://github.com/kexinhuang12345/MolTrans

공식 코드

MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction

https://github.com/kunalghosh/multi_fidelity_prediction_gp

공식 코드

Active Learning of Molecular Data for Task-Specific Objectives

https://github.com/lars-research/pacia

공식 코드

PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction

https://github.com/liugangcode/InfoAlign

공식 코드

Learning Molecular Representation in a Cell

https://github.com/lsj2408/Transformer-M

공식 코드

One Transformer Can Understand Both 2D & 3D Molecular Data

https://github.com/luoyk1999/molecular-homology

공식 코드

Improving Self-supervised Molecular Representation Learning using Persistent Homology

https://github.com/oleneyl/geometry-aware-transformer

공식 코드

GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning

https://github.com/pharmolix/openbiomed

공식 코드

Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge

https://github.com/radh55sh/BAPULM

공식 코드

BAPULM: Binding Affinity Prediction using Language Models

https://github.com/scir-hi/moltailor

공식 코드

MolTailor: Tailoring Chemical Molecular Representation to Specific Tasks via Text Prompts

https://github.com/seyonechithrananda/bert-loves-chemistry

공식 코드

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

https://github.com/siriusxiao62/2DNMRGym

공식 코드

2DNMRGym: An Annotated Experimental Dataset for Atom-Level Molecular Representation Learning in 2D NMR via Surrogate Supervision

https://github.com/smiles724/brot

공식 코드

Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport

https://github.com/smiles724/instructbio

공식 코드

InstructBio: A Large-scale Semi-supervised Learning Paradigm for Biochemical Problems

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

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Can Large Language Models Understand Molecules?

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

공식 코드

Can Large Language Models Understand Molecules?

https://github.com/sunshy-1/gdiffretro

공식 코드

GDiffRetro: Retrosynthesis Prediction with Dual Graph Enhanced Molecular Representation and Diffusion Generation

https://github.com/swansonk14/chemprop

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Analyzing Learned Molecular Representations for Property Prediction

https://github.com/sxkdz/marcel

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Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks

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/thomas0809/textreact

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Predictive Chemistry Augmented with Text Retrieval

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

공식 코드

Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction

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

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

https://github.com/victorzxy/graphac

공식 코드

Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration

https://github.com/victosdur/ectformoleculelearningtask

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Molecular Machine Learning Using Euler Characteristic Transforms

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