Code and data for Interactive Molecular Discovery with Natural Language. Related operation guides will be gradually improved in the near future.
Main packages: python==3.9.0; torch==1.13.0; transformers==4.28.0; pandas==1.5.2; numpy==1.23.5; nltk==3.7
Related codes and data instances are in predata/.
getmsk.py: Masked language modeling.getM2T.py: Experimental property prediction & SMILES-to-name.getT2M.py: Literature-to-SMILES mapping correlation.getrdkit.py: Spatial structure prediction.
Related codes and data instances are in data/.
- train/validation/test.txt are ChEBI-20k dataset.
- PCtrain/PCdev/PCtest.txt are PCdes dataset.
- Files under
ChEBI-dia/are the new ChEBI-dia dataset.
We stronly recommend using GPUs for this stage. First create an empty folder save_model/.
For knowledgeable and versatile pre-training, the jbl data files have to be first put under predata/. Change the pth args to combine different pre-training tasks. For instance:
CUDA_VISIBLE_DEVICES=0 python multi_pretraining.py --version base --save_pth save_model/understanding_pre.pt --pth predata/mixmsk.jbl --pth2 predata/preM2T.jbl --pth3 predata/spat.jbl
For molecule understanding tuning, remember to adjust the evaluate step length for different downstream datasets. For instance:
CUDA_VISIBLE_DEVICES=0 python molecule_understanding --pth_train data/PCtrain.txt --pth_dev data/PCdev.txt --pth_test data/PCtest.py --eval_len 700 --init_checkpoint save_model/pre_understanding_base.pt --save_pth save_model/understanding_
For molecule generation tuning, first conduct the fine-tuning, and then separately generate the test result and do evaluation for the specific checkpoint. For instance:
CUDA_VISIBLE_DEVICES=0 python molecule_generation.py --save_pth save_model/generation_noplug_
cd evaluation/
python test.py --init_checkpoint ../save_model/generation_noplug_100000.pt
You can also download the reference checkpoint from our Google Drive and put them under save_model/. Run the demo.py and provide your input according to the hints:
Input 'describe' to trigger molecule understanding of the current molecule, otherwise to use molecule generation. Input 'END' to clear dialog memory.
user: It derives from a tryptamine.
system: choose one molecule to continue. 1. C1=CC=C2C(=C1)C(=CN2)CCN 2. CNCCC1=CNC2=CC=CC=C21 3. C1=CC=C2C(=C1)C(=CN2)CC(C(=O)O)N
user: 2
user: It is an organic phosphate, a tryptamine alkaloid and a secondary amino compound.
system: choose one molecule to continue. 1. C1=CC=C2C(=C1)C(=CN2)CCN 2. C1=CC=C2C(=C1)C(=CN2)CC(C(=O)O)N 3. CNCCC1=CNC2=CC=CC=C21
user: 3
user: describe the current molecule
system: The molecule is a tryptamine alkaloid and a member of tryptamines. It has a role as a metabolite. It derives from a tryptamine.
user: It has a role as a hallucinogen and a fungal metabolite.
system: choose one molecule to continue. 1. C1=CC=C2C(=C1)C(=CN2)CC(C(=O)O)N 2. C[C@H](CC1=CNC2=CC=CC=C21)N[C@@H](C)C(=O)N[C@@H](CC3=CC=CC=C3)C(=O)O 3. C[C@H](CC1=CNC2=CC=CC=C21)OP(=O)(O)O
user: 3
user: END
user: It derives from a formic acid.
system: choose one molecule to continue. 1. CC(C)(COP(=O)(O)OP(=O)(O)OC[C@@H]1[C@H]([C@H]([C@@H](O1)N2C=NC3=C(N=CN=C32)N)O)OP(=O)(O)O)[C@H](C(=O)NCCC(=O)NCCSC=O)O 2. C(=O)[O-] 3. CC(C)(COP(=O)(O)OP(=O)(O)OC[C@@H]1[C@H]([C@H]([C@@H](O1)N2C=NC3=C(N=CN=C32)N)O)OP(=O)(O)O)[C@H](C(=O)NCCC(=O)N)O
user: 2
user: It has a role as a buffer.
system: choose one molecule to continue. 1. C(=O)[O-] 2. C(C(=O)O)N(C(=O)N)CC(=O)O 3. C(C(=O)O)N(CC(=O)O)CC(=O)O
user: 1
user: The molecule is the potassium salt of formic acid.
system: choose one molecule to continue. 1. C(=O)[O-].[K+] 2. [C@@H]([C@H]([C@@H]([C@H](C(=O)[O-])O)O)O)([C@H](C(=O)[O-])O)O.[K+] 3. [C@@H]([C@H]([C@@H]([C@H](C(=O)[O-])O)O)O)O.[K+]
user: 1
user: describe it
system: The molecule is a potassium salt comprising equal numbers of potassium and formic acid ions. It has a role as a fertilizer, a food acidity regulator, a human metabolite and a Saccharomyces cerevisiae metabolite. It contains a formate.
user: END
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