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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/vencent-won/sggrl

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

https://github.com/vertaix/llm-prop

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LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions

https://github.com/vertaix/llm4mat-bench

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LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction

https://github.com/vishaldeyiiest/graphta

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

https://github.com/vmoro1/multimat

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Multimodal Foundation Models for Material Property Prediction and Discovery

https://github.com/volkamerlab/transformers4mpp_review

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

https://github.com/vsomnath/graph_space_gps

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Isotropic Gaussian Processes on Finite Spaces of Graphs

https://github.com/wdimmy/graphretrieval

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An Empirical Study of Retrieval-enhanced Graph Neural Networks

https://github.com/wenhao-gao/synformer

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Generative Artificial Intelligence for Navigating Synthesizable Chemical Space

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

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

https://github.com/xbfu/FedVN

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Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning

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

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

https://github.com/xiangyan93/molecules-enumerate

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Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation

https://github.com/xiaocui3737/geomclip

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GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules

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

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Molecular Graph Representation Learning via Structural Similarity Information

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

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Towards Faster and More Compact Foundation Models for Molecular Property Prediction

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

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Large-Scale Knowledge Integration for Enhanced Molecular Property Prediction

https://github.com/yifeiwang15/acml

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

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

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MUBen: Benchmarking the Uncertainty of Molecular Representation Models

https://github.com/yingtiny/bandgap_prediction_roberta

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Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap Prediction

https://github.com/yinhanhe123/new_LLM4GNNExplanation

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

https://github.com/yoniLc/GeometricTransformerMolecule

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Geometric Transformer for End-to-End Molecule Properties Prediction

https://github.com/ytg7146/DU_CNN

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Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environment

https://github.com/yuanqidu/leftnet

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

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

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An open unified deep graph learning framework for discovering drug leads

https://github.com/yuewan2/molmcl

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

https://github.com/yuyangw/MolCLR

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Molecular Contrastive Learning of Representations via Graph Neural Networks

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

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

https://github.com/zaixizhang/MGSSL

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

https://github.com/zcao0420/moformer

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MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property Prediction

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

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

https://github.com/zhangtr10/PolyLLMem

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Multimodal machine learning with large language embedding model for polymer property prediction

https://github.com/zhao-ht/gimlet

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GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning

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

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Few-Shot Graph Learning for Molecular Property Prediction

https://github.com/zhitao-wang/plnlp

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Pairwise Learning for Neural Link Prediction

https://github.com/zhousongh/hipm

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

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