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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/BenevolentAI/MolBERT

공식 코드

Molecular representation learning with language models and domain-relevant auxiliary tasks

https://github.com/BiomedSciAI/biomed-multi-view

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Multi-view biomedical foundation models for molecule-target and property prediction

https://github.com/BrightBlueCheese/transformers_and_chemistry

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The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA

https://github.com/Data-reindeer/MolScaling

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Uncovering Neural Scaling Laws in Molecular Representation Learning

https://github.com/ExpectozJJ/Persistent-Ollivier-Ricci-Curvature

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Ollivier persistent Ricci curvature (OPRC) based molecular representation for drug design

https://github.com/GSK-AI/mocop

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Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation

https://github.com/HirahTang/REMO

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Contextual Molecule Representation Learning from Chemical Reaction Knowledge

https://github.com/IDEA-XL/SubgDiff

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SubGDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learning

https://github.com/Lirain21/UniMatch

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UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery

https://github.com/MarkusFerdinandDablander/QSAR-activity-cliff-experiments

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Exploring QSAR Models for Activity-Cliff Prediction

https://github.com/Navantock/SE3Set

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SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning

https://github.com/PKUterran/HamNet

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HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks

https://github.com/PaddlePaddle/PaddleHelix

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LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction

https://github.com/Yinghao-Li/UncertaintyBenchmark

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MUBen: Benchmarking the Uncertainty of Molecular Representation Models

https://github.com/ZJU-Fangyin/KCL

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Molecular Contrastive Learning with Chemical Element Knowledge Graph

https://github.com/Zhenger959/MoleculeCLA

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MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis

https://github.com/agave233/GeomGCL

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GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction

https://github.com/ai-hpc-research-team/git-mol

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GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text

https://github.com/andreimano/MolMix

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MolMix: A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning

https://github.com/aspuru-guzik-group/janus

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JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design

https://github.com/aspuru-guzik-group/selfies

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Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation

https://github.com/azureleon1/molspectra

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MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra

https://github.com/bbrighttaer/jova_baselines

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Multi-View Self-Attention for Interpretable Drug-Target Interaction Prediction

https://github.com/c-tl/geomformer

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GeoMFormer: A General Architecture for Geometric Molecular Representation Learning

https://github.com/chao1224/geossl

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Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching

https://github.com/chao1224/se3ddm

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Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching

https://github.com/cs-bio/mol-gdl

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Molecular geometric deep learning

https://github.com/data-reindeer/nsl_mrl

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Uncovering Neural Scaling Laws in Molecular Representation Learning

https://github.com/dengjianyuan/respite_mpp

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Unraveling Key Elements Underlying Molecular Property Prediction: A Systematic Study

https://github.com/dhroth/c5t5

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C5T5: Controllable Generation of Organic Molecules with Transformers

https://github.com/fengshikun/frad

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Fractional Denoising for 3D Molecular Pre-training

https://github.com/fengshikun/unimap

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UniMAP: Universal SMILES-Graph Representation Learning

https://github.com/geommoldesign/hmr

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Learning Harmonic Molecular Representations on Riemannian Manifold

https://github.com/gitter-lab/chemlml

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Chemical Language Model Linker: blending text and molecules with modular adapters

https://github.com/henrium/molsets

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MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling

https://github.com/hhaji/funqg

공식 코드

FunQG: Molecular Representation Learning Via Quotient Graphs

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