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Rose-STL-Lab / LIMO

generative model for drug discovery
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Why is the data presented in the code I run different from what is presented in the paper. Could you help me take a look #13

Closed zhanghan1234 closed 11 months ago

zhanghan1234 commented 1 year ago

parser.add_argument('--prop', choices=['logp', 'penalized_logp', 'qed', 'sa', 'binding_affinity', 'multi_objective_binding_affinity'], default='qed') parser.add_argument('--num_mols', type=int, default=100000) parser.add_argument('--top_k', type=int, default=3) parser.add_argument('--sa_cutoff', type=float, default=5.5) parser.add_argument('--qed_cutoff', type=float, default=0.4) parser.add_argument('--optim_steps', type=int, default=22)#原来是10 parser.add_argument('--autodock_executable', type=str, default='AutoDock-GPU/bin/autodock_gpu_128wi')#这个必须有 parser.add_argument('--protein_file', type=str, default='1err/1err.maps.fld')#这个必须指定，还可以换成2iik/2iik.maps.fld args = parser.parse_args() These are the hyperparameter I use, vae.pt and qed.pt are provided by you

zhanghan1234 commented 1 year ago

Table1

PeterEckmann1 commented 1 year ago

Thanks for your interest in the paper! I'd need some more details before I can say why your results are different. What results are you currently getting for the QED task? I would expect some small variations from the numbers in the paper, but a large difference would indicate something else is going on.

zhanghan1234 commented 1 year ago

Thanks for your interest in the paper! I'd need some more details before I can say why your results are different. What results are you currently getting for the QED task? I would expect some small variations from the numbers in the paper, but a large difference would indicate something else is going on.

python generate_molecules.py --prop penalized_logp generating molecules: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [01:38<00:00, 9.82s/it] 16.639477 CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC1CC1 16.639477 CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC1CC1 17.468054 CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC=CCC

python generate_molecules.py --prop qed generating molecules: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [01:35<00:00, 9.54s/it] calculating QED: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [00:14<00:00, 671.45it/s] 0.8635939 CC=CC1=CC1NC(=O)C2CCCCCCCC3=CC=CC=C23 0.8734434 CC=CC1=CC1NC(=O)C2CCCC=CC=CC3=CC=CC=C23 0.88593656 CC=CC1=CC1NC(=O)C2CC=CC=CC=CC3=CC=CC=C23 Hello. This is the result of my run. I did not change the hyperparameter, but directly used the provided code to get the hyperparameter. I didn't train my own VAE model, but instead used the provided VAE.PT and the provided QED.PT. Can you help me check which step I made a mistake.