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Molecular Property Prediction 벤치마크

Molecular Property Prediction on SIDER

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ROC-AUC

54 63.28 72.56 81.83 91.11 2016-11 2026-09 IterRefLSTM — 70.4 (2016-11-10) IterRefLSTM — 70.4 (2016-11-10) N-GramRF — 66.8 (2018-06-24) N-GramXGB — 65.5 (2018-06-24) N-GramRF — 66.8 (2018-06-24) N-GramXGB — 65.5 (2018-06-24) D-MPNN — 57.0 (2019-04-02) D-MPNN — 57.0 (2019-04-02) PretrainGNN — 62.7 (2019-05-29) PretrainGNN — 62.7 (2019-05-29) GROVER (large) — 65.4 (2020-06-18) GROVER (base) — 64.8 (2020-06-18) GROVER (large) — 65.4 (2020-06-18) GROVER (base) — 64.8 (2020-06-18) ChemRL-GEM — 67.2 (2021-06-11) ChemRL-GEM — 67.2 (2021-06-11) Uni-Mol — 65.9 (2022-09-08) Uni-Mol — 65.9 (2022-09-08) GAL 120B — 63.2 (2022-11-16) GAL 30B — 61.3 (2022-11-16) GAL 125M — 55.9 (2022-11-16) GAL 6.7B — 55.9 (2022-11-16) GAL 1.3B — 54.0 (2022-11-16) GAL 120B — 63.2 (2022-11-16) GAL 30B — 61.3 (2022-11-16) GAL 125M — 55.9 (2022-11-16) GAL 6.7B — 55.9 (2022-11-16) GAL 1.3B — 54.0 (2022-11-16) SPMM — 64.7 (2022-11-19) SPMM — 64.7 (2022-11-19) MolXPT — 71.7 (2023-05-18) MolXPT — 71.7 (2023-05-18) BioAct-Het — 91.11 (2023-10-15) BioAct-Het — 91.11 (2023-10-15) S-CGIB — 64.03 (2025-02-20) S-CGIB — 64.03 (2025-02-20) Deep-CBN — 78.2 (2025-02-28) Deep-CBN — 78.2 (2025-02-28) IterRefLSTM — 70.4 (2016-11-10) MolXPT — 71.7 (2023-05-18) BioAct-Het — 91.11 (2023-10-15)
RankModel ROC-AUC PaperCodeYear
1 BioAct-Het 91.11 BioAct-Het: A Heterogeneous Siamese Neural Network for Bioactivity Prediction Using Novel Bioactivity Representatio CBRC-lab/BioAct-Het 2023
2 Deep-CBN 78.2 Integrating convolutional layers and biformer network with forward-forward and backpropagation training akianfar/Deep-CBN 2025
3 MolXPT 71.7 MolXPT: Wrapping Molecules with Text for Generative Pre-training zequnl/molxpt 2023
4 IterRefLSTM 70.40 Low Data Drug Discovery with One-shot Learning 2016
5 ChemRL-GEM 67.2 ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction 2021
6 N-GramRF 66.8 N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules chao1224/n_gram_graph 2018
7 Uni-Mol 65.9 Uni-Mol: A Universal 3D Molecular Representation Learning Framework dptech-corp/Uni-Mol 2022
8 N-GramXGB 65.5 N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules chao1224/n_gram_graph 2018
9 GROVER (large) 65.4 Self-Supervised Graph Transformer on Large-Scale Molecular Data deepchem/deepchem · tencent-ailab/grover · dengjianyuan/respite_mpp 2020
10 GROVER (base) 64.8 Self-Supervised Graph Transformer on Large-Scale Molecular Data deepchem/deepchem · tencent-ailab/grover · dengjianyuan/respite_mpp 2020
11 SPMM 64.7 Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model jinhojsk515/SPMM 2022
12 S-CGIB 64.03±1.04 Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck NSLab-CUK/S-CGIB 2025
13 GAL 120B 63.2 Galactica: A Large Language Model for Science paperswithcode/galai 2022
14 PretrainGNN 62.7 Strategies for Pre-training Graph Neural Networks snap-stanford/pretrain-gnns · snap-stanford/pretrain-gnns · gnn4dr/DRKG · +8 2019
15 GAL 30B 61.3 Galactica: A Large Language Model for Science paperswithcode/galai 2022
16 D-MPNN 57.0 Analyzing Learned Molecular Representations for Property Prediction swansonk14/chemprop · jbr-ai-labs/lipophilicity-prediction · anonymous20201002/chemprop · +1 2019
17 GAL 125M 55.9 Galactica: A Large Language Model for Science paperswithcode/galai 2022
17 GAL 6.7B 55.9 Galactica: A Large Language Model for Science paperswithcode/galai 2022
19 GAL 1.3B 54.0 Galactica: A Large Language Model for Science paperswithcode/galai 2022
20 BioAct-Het 91.11 BioAct-Het: A Heterogeneous Siamese Neural Network for Bioactivity Prediction Using Novel Bioactivity Representatio CBRC-lab/BioAct-Het 2023
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