Binary Classification on MoleculeNet BBBP (test)
0.8ROC AUCMolXPT
Evaluation Results
| Method | Links | |
|---|---|---|
| MolXPTModel Category=Single-task Specialist Models2024.02 | 0.8 | |
| BioT5Model Category=Single-task Specialist Models2024.02 | 0.777 | |
| SPD-Sheaf2026.04 | 0.774 | |
| BioT5+Model Category=LLM-based Generalist Models2024.02 | 0.765 | |
| ChemBERTa-2Pre-training Task=MTR, Pre-training Dataset Size=5M, Evaluation Protocol=Fine-tuned2022.09 | 0.742 | |
| ChemBERTa-2Pre-training strategy=MTR, Pre-training size=5M2026.05 | 0.742 | |
| MolCLRModel Category=Single-task Specialist Models2024.02 | 0.738 | |
| SMPT2026.04 | 0.734 | |
| ChemBERTa-2Pre-training Task=MTR, Pre-training Dataset Size=10M, Evaluation Protocol=Fine-tuned2022.09 | 0.733 | |
| ChemBERTa-2Pre-training strategy=MTR, Pre-training size=10M2026.05 | 0.733 | |
| Uni-MolModel Category=Single-task Specialist Models2024.02 | 0.729 | |
| MolFMModel Category=Single-task Specialist Models2024.02 | 0.729 | |
| Uni-Mol2026.04 | 0.729 | |
| ChemBERTa-2Pre-training Task=MTR, Pre-training Dataset Size=77M, Evaluation Protocol=Fine-tuned2022.09 | 0.728 | |
| Mol-GDL2026.04 | 0.728 | |
| ChemBERTa-2Pre-training strategy=MTR, Pre-training size=77M2026.05 | 0.728 | |
| GraphMVP-CModel Category=Single-task Specialist Models2024.02 | 0.724 | |
| GEMModel Category=Single-task Specialist Models2024.02 | 0.724 | |
| GraphMVP2026.04 | 0.724 | |
| GEM2026.04 | 0.724 | |
| MolCLR2026.04 | 0.722 | |
| Chem-GMNetPre-training strategy=scratch, k=82026.05 | 0.722 | |
| EGNN2026.04 | 0.721 | |
| RFEvaluation Protocol=Fine-tuned2022.09 | 0.7194 | |
| Random ForestPre-training strategy=None2026.05 | 0.719 | |
| D-MPNN2026.04 | 0.71 | |
| Chem-GMNetPre-training strategy=scratch, k=102026.05 | 0.71 | |
| SchNet2026.04 | 0.708 | |
| MGSSLModel Category=Single-task Specialist Models2024.02 | 0.705 | |
| MoMuModel Category=Single-task Specialist Models2024.02 | 0.705 | |
| ChemBERTa-2Pre-training Task=MLM, Pre-training Dataset Size=5M, Evaluation Protocol=Fine-tuned2022.09 | 0.701 | |
| ChemBERTa-2Pre-training strategy=MLM, Pre-training size=5M2026.05 | 0.701 | |
| InstructMol-GS-6.9BModel Category=LLM-based Generalist Models2024.02 | 0.7 | |
| GROVEModel Variant=base2026.04 | 0.7 | |
| ChemBERTa-2Pre-training Task=MLM, Pre-training Dataset Size=77M, Evaluation Protocol=Fine-tuned2022.09 | 0.698 | |
| ChemBERTa-2Pre-training strategy=MLM, Pre-training size=77M2026.05 | 0.698 | |
| Chem-GMNetPre-training strategy=MLM, Pre-training size=10M2026.05 | 0.698 | |
| D-MPNNEvaluation Protocol=Fine-tuned2022.09 | 0.697 | |
| GraphCLModel Category=Single-task Specialist Models2024.02 | 0.697 | |
| N-GramClassifier=RF2026.04 | 0.697 | |
| D-MPNN (Chemprop)Pre-training strategy=None2026.05 | 0.697 | |
| ChemBERTa-2Pre-training Task=MLM, Pre-training Dataset Size=10M, Evaluation Protocol=Fine-tuned2022.09 | 0.696 | |
| ChemBERTa-2Pre-training strategy=MLM, Pre-training size=10M2026.05 | 0.696 | |
| GROVEModel Variant=large2026.04 | 0.695 | |
| N-GramClassifier=XGB2026.04 | 0.691 | |
| PretrainGNN2026.04 | 0.687 | |
| GCNEvaluation Protocol=Fine-tuned2022.09 | 0.676 | |
| GCNPre-training strategy=None2026.05 | 0.676 | |
| KV-PLMModel Category=Single-task Specialist Models2024.02 | 0.669 | |
| AttentiveFP2026.04 | 0.663 | |
| Galactica-120BModel Category=LLM-based Generalist Models2024.02 | 0.661 | |
| Llama-2-7B-chatModel Category=LLM-based Generalist Models, tuning=LoRA2024.02 | 0.656 | |
| ChemBERTa-1Evaluation Protocol=Fine-tuned2022.09 | 0.643 | |
| ChemBERTa-1Pre-training strategy=MLM2026.05 | 0.643 | |
| InstructMol-G-6.9BModel Category=LLM-based Generalist Models2024.02 | 0.64 | |
| Vicuna-v1.3-7BModel Category=LLM-based Generalist Models, tuning=LoRA2024.02 | 0.601 | |
| Galactica-30BModel Category=LLM-based Generalist Models2024.02 | 0.596 | |
| Galactica-6.7BModel Category=LLM-based Generalist Models2024.02 | 0.535 | |
| Vicuna-v1.5-13B-16kModel Category=LLM-based Generalist Models, Shot=4-shot2024.02 | 0.527 |