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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/seojin-kim/hi-mol

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Data-Efficient Molecular Generation with Hierarchical Textual Inversion

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

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

https://github.com/sgfin/molecule_ge_coordinated_embeddings

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Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profiles

https://github.com/shamim-hussain/tgt

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Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers

https://github.com/shehzaidi/pre-training-via-denoising

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Pre-training via Denoising for Molecular Property Prediction

https://github.com/shenyanghuang/tgb

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Temporal Graph Benchmark for Machine Learning on Temporal Graphs

https://github.com/sheriftawfikabbas/crystalfeatures

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Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors

https://github.com/shichangzh/gstarx

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GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games

https://github.com/shiningsunnyday/induction

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Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages

https://github.com/sichao-li/diverse-explanations-physical-sciences

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Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical Sciences

https://github.com/sjiang87/deephyper

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Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search

https://github.com/slautin/2025_vae_dkl

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Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model

https://github.com/smiles724/brot

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Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport

https://github.com/snap-research/largegt

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Graph Transformers for Large Graphs

https://github.com/snap-stanford/pretrain-gnns/

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Strategies for Pre-training Graph Neural Networks

https://github.com/snudatalab/AugWard

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AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification

https://github.com/soumyasanyal/mt-cgcnn

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MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction

https://github.com/srivathsanb14/multipeptide

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Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties

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

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Can Large Language Models Understand Molecules?

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

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Can Large Language Models Understand Molecules?

https://github.com/swansonk14/chemprop

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Analyzing Learned Molecular Representations for Property Prediction

https://github.com/symmetryadvantage/wyckofftransformer

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Wyckoff Transformer: Generation of Symmetric Crystals

https://github.com/temporal-graphs-negative-sampling/tgb

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Temporal graph models fail to capture global temporal dynamics

https://github.com/tencent-ailab/grover

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Self-Supervised Graph Transformer on Large-Scale Molecular Data

https://github.com/teslacool/unifiedmolpretrain

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Unified 2D and 3D Pre-Training of Molecular Representations

https://github.com/thilinicooray/deepgcfx

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Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning

https://github.com/thunlp-mt/symbol2language

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Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language Models

https://github.com/tldr-group/HR-Dv2

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Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation

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

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Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction

https://github.com/uds-lsv/domain-adaptation-molecular-transformers

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Transformers for molecular property prediction: Domain adaptation efficiently improves performance

https://github.com/uhlerlab/infocore

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Removing Biases from Molecular Representations via Information Maximization

https://github.com/ur-whitelab/llms-in-science

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A Review of Large Language Models and Autonomous Agents in Chemistry

https://github.com/ur-whitelab/mol.dev

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Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks

https://github.com/usccolumbia/deeperGATGNN

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Scalable deeper graph neural networks for high-performance materials property prediction

https://github.com/usccolumbia/materialsuq

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Materials Property Prediction with Uncertainty Quantification: A Benchmark Study

https://github.com/usccolumbia/md-hit

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MD-HIT: Machine learning for materials property prediction with dataset redundancy control

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