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코드

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

https://github.com/divelab/AIRS/tree/main/OpenMat/PotNet

공식 코드

Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction

https://github.com/divelab/MoleculeProp

공식 코드

Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery

https://github.com/divelab/MoleculeX

공식 코드

Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks

https://github.com/dmirlab-group/sci

공식 코드

Identifying Semantic Component for Robust Molecular Property Prediction

https://github.com/dmis-lab/molpla

공식 코드

MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints

https://github.com/dmitrykazhdan/gci

공식 코드

GCI: A (G)raph (C)oncept (I)nterpretation Framework

https://github.com/dongkikim95/d-sla

공식 코드

Graph Self-supervised Learning with Accurate Discrepancy Learning

https://github.com/dqwang122/rlhex

공식 코드

Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning

https://github.com/duyhominhnguyen/conan-fgw

공식 코드

Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks

https://github.com/einae-nd/moama-dev

공식 코드

Motif-aware Attribute Masking for Molecular Graph Pre-training

https://github.com/fabiankruger/molprivacy

공식 코드

Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy

https://github.com/fanyun-sun/InfoGraph

공식 코드

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

https://github.com/fengshikun/unimap

공식 코드

UniMAP: Universal SMILES-Graph Representation Learning

https://github.com/g-benton/learning-invariances

공식 코드

Learning Invariances in Neural Networks

https://github.com/gaingod-xu/acmlproject

공식 코드

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

https://github.com/genentech/molcap-arena

공식 코드

MolCap-Arena: A Comprehensive Captioning Benchmark on Language-Enhanced Molecular Property Prediction

https://github.com/georgelamb19/chempropBayes

공식 코드

Bayesian Graph Neural Networks for Molecular Property Prediction

https://github.com/gmh14/geo-deg

공식 코드

Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction

https://github.com/gmum/chienn

공식 코드

ChiENN: Embracing Molecular Chirality with Graph Neural Networks

https://github.com/gmum/progrest

공식 코드

ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction

https://github.com/graphcore/ogb-lsc-pcqm4mv2

공식 코드

GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction

https://github.com/hannesstark/3dinfomax

공식 코드

3D Infomax improves GNNs for Molecular Property Prediction

https://github.com/henrium/molsets

공식 코드

MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling

https://github.com/hhaji/funqg

공식 코드

FunQG: Molecular Representation Learning Via Quotient Graphs

https://github.com/hicai-zju/gs-meta

공식 코드

Graph Sampling-based Meta-Learning for Molecular Property Prediction

https://github.com/hubiodatalab/selformer

공식 코드

SELFormer: Molecular Representation Learning via SELFIES Language Models

https://github.com/ibm/graph-space-gps

공식 코드

Isotropic Gaussian Processes on Finite Spaces of Graphs

https://github.com/ibm/graph_space_gps

공식 코드

Isotropic Gaussian Processes on Finite Spaces of Graphs

https://github.com/ibm/molformer

공식 코드

Large-Scale Chemical Language Representations Capture Molecular Structure and Properties

https://github.com/idruglab/hignn

공식 코드

HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise Attention

https://github.com/imatge-upc/CartNet

공식 코드

A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation

https://github.com/intellabs/matsciml

공식 코드

MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling

https://github.com/j-adamczyk/apis_tox_dataset

공식 코드

ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey bees

https://github.com/jacksonburns/chemeleon

공식 코드

Descriptor-based Foundation Models for Molecular Property Prediction

https://github.com/jacksonburns/fastprop

공식 코드

Generalizable, Fast, and Accurate DeepQSPR with fastprop

https://github.com/janosh/dielectrics

공식 코드

Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials

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