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https://github.com/js-ish/dooc
Digtal Organoid On Chips
https://github.com/js-ish/dooc
ai organ-on-chip organoids
Last synced: about 2 months ago
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Digtal Organoid On Chips
- Host: GitHub
- URL: https://github.com/js-ish/dooc
- Owner: js-ish
- License: apache-2.0
- Created: 2024-05-14T07:58:41.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-07-31T09:32:39.000Z (5 months ago)
- Last Synced: 2024-07-31T10:48:39.881Z (5 months ago)
- Topics: ai, organ-on-chip, organoids
- Language: Python
- Homepage:
- Size: 322 KB
- Stars: 0
- Watchers: 2
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DOoC
## Train
```python
import random
import torch
from torch import nn
import torch.optim as optim
from moltx import tokenizers as tkz
from moltx.models import AdaMRTokenizerConfig
from dooc import models, datasets
```
### Regression
```python
# Regression datasets
tokenizer = tkz.MoltxTokenizer.from_pretrain(
conf=AdaMRTokenizerConfig.Prediction
)
ds = datasets.MutSmiReg(smi_tokenizer=tokenizer)
smiles = ["c1cccc1c", "CC[N+](C)(C)Cc1ccccc1Br"]
mutations = [[random.choice([0, 1]) for _ in range(3008)]] * 2
# mutations contains 0/1 encoding information of the genome
values = [0.85, 0.78]
mut_x, smi_tgt, out = ds(mutations, smiles, values)
# Regression train
model = models.MutSmiReg()
model.load_pretrained_ckpt(
mut_ckpt='path/to/drugcell.pt',
smi_ckpt='path/to/moltx.ckpt'
)
mse_loss = nn.MSELoss()
optimizer = optim.Adam(
model.parameters(),
lr=1e-04,
foreach=False
)
optimizer.zero_grad()
pred = model(mut_x=mut_x, smi_tgt=smi_tgt)
loss = mse_loss(pred, out)
loss.backward()
optimizer.step()
torch.save(model.state_dict(), '/path/to/mutsmireg.ckpt')
```
### Pairwise
```python
# Pairwise datasets
tokenizer = tkz.MoltxTokenizer.from_pretrain(
conf=AdaMRTokenizerConfig.Prediction
)
ds = datasets.MutSmisPairwiseRank(smi_tokenizer=tokenizer)
smiles = [["c1cccc1c", "CC[N+](C)(C)Cc1ccccc1Br"],
["CC[N+](C)(C)Cc1ccccc1Br", "CN(Cc1oc2ccccc2c1C)C(=O)\C=C\c1cnc2NC(=O)CCc2c1"]]
mutations = [[random.choice([0, 1]) for _ in range(3008)]] * 2
# mutations contains 0/1 encoding information of the genome
values = [[0.85, 0.78]] * 2
mut_x, smi_tgt, out = ds(mutations, smiles, values)
# Pairwise train
model = models.MutSmisRank()
model.load_pretrained_ckpt(
mut_ckpt='path/to/drugcell.pt',
smi_ckpt='path/to/moltx.ckpt'
)
# Pairwise loss
loss_func = torch.nn.BCEWithLogitsLoss()
optimizer = optim.Adam(
model.parameters(),
lr=1e-04,
foreach=False
)
optimizer.zero_grad()
pred = model(mut_x=mut_x, smi_tgt=smi_tgt)
loss = loss_func(pred[:,0] - pred[:,1], out)
loss.backward()
optimizer.step()
torch.save(model.state_dict(), '/path/to/mutsmipairwise.ckpt')
```
### Listwise
```python
# Listwise datasets
tokenizer = tkz.MoltxTokenizer.from_pretrain(
conf=AdaMRTokenizerConfig.Prediction
)
ds = datasets.MutSmisListwiseRank(smi_tokenizer=tokenizer)
smiles = [["c1cccc1c", "CC[N+](C)(C)Cc1ccccc1Br", "CN(Cc1oc2ccccc2c1C)C(=O)\C=C\c1cnc2NC(=O)CCc2c1"]] * 2
mutations = [[random.choice([0, 1]) for _ in range(3008)]] * 2
# mutations contains 0/1 encoding information of the genome
values = [[0.85, 0.78, 0.79]] * 2
mut_x, smi_tgt, out = ds(mutations, smiles, values)
mut_x, smi_tgt, out = mut_x.squeeze(0), smi_tgt.squeeze(0), out.squeeze(0)
# Listwise train
model = models.MutSmisRank()
model.load_pretrained_ckpt(
mut_ckpt='path/to/drugcell.pt',
smi_ckpt='path/to/moltx.ckpt'
)
# Listwise loss
loss_func = dooc_list_loss.ListNetLoss()
optimizer = optim.Adam(
model.parameters(),
lr=1e-04,
foreach=False
)
optimizer.zero_grad()
pred = model(mut_x=mut_x, smi_tgt=smi_tgt)
loss = loss_func(pred, out)
loss.backward()
optimizer.step()
torch.save(model.state_dict(), '/path/to/mutsmilistwise.ckpt')
```
## Inference
```python
import random
from moltx import tokenizers as tkz
from moltx.models import AdaMRTokenizerConfig
from dooc import pipelines, models
# Regression
tokenizer = tkz.MoltxTokenizer.from_pretrain(
conf=AdaMRTokenizerConfig.Prediction
)
model = models.MutSmiReg()
model.load_ckpt('/path/to/mutsmireg.ckpt')
pipeline = pipelines.MutSmiReg(
smi_tokenizer=tokenizer, model=model
)
mutations = [random.choice([0, 1]) for _ in range(3008)]
smiles = "CC[N+](C)(C)Cc1ccccc1Br"
predict = pipeline(mut=mutations, smi=smiles) # e.g. 0.85
# Rank
tokenizer = tkz.MoltxTokenizer.from_pretrain(
conf=AdaMRTokenizerConfig.Prediction
)
model = models.MutSmisRank()
model.load_ckpt('/path/to/mutsmirank.ckpt')
pipeline = pipelines.MutSmisRank(smi_tokenizer=tokenizer, model=model)
mutations = [random.choice([0, 1]) for _ in range(3008)]
smiles = ["c1cccc1c", "CC[N+](C)(C)Cc1ccccc1Br", "CN(Cc1oc2ccccc2c1C)C(=O)\C=C\c1cnc2NC(=O)CCc2c1"]
predict = pipeline(mut=mutations, smis=smiles) # e.g. ["CN(Cc1oc2ccccc2c1C)C(=O)\C=C\c1cnc2NC(=O)CCc2c1", "CC[N+](C)(C)Cc1ccccc1Br", "c1cccc1c"]
```