Papers with Code 아카이브
Property Prediction
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Property prediction involves forecasting or estimating a molecule's inherent physical and chemical properties based on information derived from its structural characteristics. It facilitates high-throughput evaluation of an extensive array of molecular properties, enabling the virtual screening of compounds. Additionally, it provides the means to predict the unknown attributes of new molecules, thereby bolstering research efficiency and reducing development times.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gmum/chienn
공식 코드ChiENN: Embracing Molecular Chirality with Graph Neural Networks
https://github.com/gmum/progrest
공식 코드ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction
https://github.com/gomes-lab/H-CLMP
공식 코드Materials Representation and Transfer Learning for Multi-Property Prediction
https://github.com/google-research-datasets/tpu_graphs
공식 코드TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
https://github.com/google-research/google-research/tree/master/slot_attention
공식 코드https://github.com/graphcore/ogb-lsc-pcqm4mv2
공식 코드GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction
https://github.com/gregory-kyro/hac-net
공식 코드https://github.com/hannesstark/3dinfomax
공식 코드https://github.com/hhaji/funqg
공식 코드FunQG: Molecular Representation Learning Via Quotient Graphs
https://github.com/hicai-zju/gs-meta
공식 코드Graph Sampling-based Meta-Learning for Molecular Property Prediction
https://github.com/hkunlp/rsa
공식 코드Retrieved Sequence Augmentation for Protein Representation Learning
https://github.com/hongluzhou/dhypr
공식 코드https://github.com/hongshuh/matinformer
공식 코드https://github.com/huaxiuyao/mlti
공식 코드https://github.com/hubiodatalab/selformer
공식 코드SELFormer: Molecular Representation Learning via SELFIES Language Models
https://github.com/hwwang55/MolR
공식 코드https://github.com/ibm/graph-space-gps
공식 코드https://github.com/ibm/graph_space_gps
공식 코드https://github.com/ibm/molformer
공식 코드Improving Molecular Properties Prediction Through Latent Space Fusion
https://github.com/ibm/regression-transformer
공식 코드https://github.com/idruglab/hignn
공식 코드https://github.com/ihalage/finder
공식 코드Formula graph self-attention network for representation-domain independent materials discovery
https://github.com/imatge-upc/CartNet
공식 코드https://github.com/influencefunctional/mxtaltools
공식 코드https://github.com/ishan-kumar2/configurational-polymer-fingerprint
공식 코드Coarse-Grained Configurational Polymer Fingerprints for Property Prediction using Machine Learning
https://github.com/ivanniu/inversiongnn
공식 코드InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization
https://github.com/j-adamczyk/apis_tox_dataset
공식 코드ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey bees
https://github.com/jacksonburns/chemeleon
공식 코드Descriptor-based Foundation Models for Molecular Property Prediction
https://github.com/jacksonburns/fastprop
공식 코드https://github.com/jfpuget/nvidia-pcqm4mv2
공식 코드Heterogenous Ensemble of Models for Molecular Property Prediction
https://github.com/jinhojsk515/SPMM
공식 코드Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model
https://github.com/johnathan-xie/sma
공식 코드Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
https://github.com/jonathanbalasingham/periodic-set-transformer
공식 코드Accelerating Material Property Prediction using Generically Complete Isometry Invariants
https://github.com/jpfolch/catechol_solvent_selection
공식 코드The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
https://github.com/junxia97/awesome-pretrain-on-molecules
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.