Skip to main content
scientific-deep-chemistry 深層学習分子特性予測スキル。DeepChem による GCN/MPNN/AttentiveFP
分子特性予測・MoleculeNet ベンチマーク・ChemBERTa/GROVER
事前学習モデル・分子フィンガープリントフィーチャライザ。
Aller à l'installation Skills Marketplace Découvrez et explorez les compétences IA créées par la communauté.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Afficher les détails du prompt Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
npx skills add https://github.com/nahisaho/satori --skill scientific-deep-chemistryLa commande reste sur une seule ligne. Faites défiler horizontalement pour la vérifier avant de la copier.
Vous préférez une copie locale ? Téléchargez les fichiers actuellement disponibles dans SkillsMP.
Télécharger Zip Téléchargement... Métiers associés SOC
Basé sur la classification professionnelle SOC
name scientific-deep-chemistry description 深層学習分子特性予測スキル。DeepChem による GCN/MPNN/AttentiveFP
分子特性予測・MoleculeNet ベンチマーク・ChemBERTa/GROVER
事前学習モデル・分子フィンガープリントフィーチャライザ。
tu_tools [{"key":"chembl","name":"ChEMBL","description":"化学的活性・化合物データ検索"}]
Scientific Deep Chemistry
DeepChem を活用した深層学習ベース分子特性予測パイプラインを提供する。
グラフニューラルネットワーク (GCN/MPNN/AttentiveFP)、MoleculeNet
ベンチマーク、事前学習モデル (ChemBERTa/GROVER)。
When to Use
分子の ADMET/物性を深層学習で予測するとき
MoleculeNet ベンチマークデータセットを使うとき
GCN / MPNN / AttentiveFP モデルを訓練するとき
ChemBERTa で分子表現学習を行うとき
毒性予測 (Tox21, ToxCast) を行うとき
薬理活性予測の分子特徴量を生成するとき
Quick Start
1. MoleculeNet データセット読込み
import deepchem as dc
import numpy as np
import pandas as pd
def load_moleculenet (dataset_name="delaney" , featurizer="GraphConv" ,
split="scaffold" ):
"""
MoleculeNet ベンチマークデータセット読込み。
Parameters:
dataset_name: str — データセット名
("delaney", "tox21", "bbbp", "hiv", "muv", "pcba",
"sider", "clintox", "freesolv", "lipo")
featurizer: str — 特徴量化手法
("GraphConv", "ECFP", "Weave", "MolGraphConv")
split: str — 分割方法 ("scaffold", "random", "stratified")
K-Dense: deepchem
"""
loader_map = {
"delaney"
"tox21"
"bbbp"
"hiv"
"muv"
"pcba"
"sider"
"clintox"
"freesolv"
"lipo"
if
not
in
raise
f"Unknown dataset: {dataset_name} "
print
f"MoleculeNet '{dataset_name} ':"
print
f" Tasks: {len (tasks)} "
print
f" Train: {len (train)} , Valid: {len (valid)} , Test: {len (test)} "
print
f" Featurizer: {featurizer} , Split: {split} "
return
2. GCN モデル訓練 def train_gcn (train_data, valid_data, tasks, n_epochs=50 ,
learning_rate=0.001 , batch_size=64 ):
"""
Graph Convolutional Network (GCN) モデル訓練。
Parameters:
train_data: dc.data.Dataset — 訓練データ
valid_data: dc.data.Dataset — 検証データ
tasks: list — タスク名リスト
n_epochs: int — エポック数
"""
model = dc.models.GraphConvModel(
n_tasks=len (tasks),
mode="classification" if len (tasks) > 1 else "regression" ,
batch_size=batch_size,
learning_rate=learning_rate,
)
for epoch in range (n_epochs):
loss = model.fit(train_data, nb_epoch=1 )
if (epoch + 1 ) % 10 == 0 :
metric = dc.metrics.Metric(
dc.metrics.roc_auc_score if len (tasks) > 1
else dc.metrics.pearson_r2_score
)
train_score = model.evaluate(train_data, [metric])
valid_score = model.evaluate(valid_data, [metric])
print (f" Epoch {epoch+1 } : "
f"train={list (train_score.values())[0 ]:.4 f} , "
f"valid={list (valid_score.values())[0 ]:.4 f} " )
return model
3. MPNN モデル訓練 def train_mpnn (train_data, valid_data, tasks, n_epochs=50 ,
learning_rate=0.001 ):
"""
Message Passing Neural Network (MPNN) 訓練。
Parameters:
train_data: dc.data.Dataset — GraphConv 特徴量訓練データ
valid_data: dc.data.Dataset — 検証データ
tasks: list — タスク名リスト
"""
model = dc.models.MPNNModel(
n_tasks=len (tasks),
mode="classification" if len (tasks) > 1 else "regression" ,
learning_rate=learning_rate,
node_out_feats=64 ,
edge_hidden_feats=128 ,
num_step_message_passing=3 ,
)
model.fit(train_data, nb_epoch=n_epochs)
metric = dc.metrics.Metric(
dc.metrics.roc_auc_score if len (tasks) > 1
else dc.metrics.pearson_r2_score
)
valid_score = model.evaluate(valid_data, [metric])
print (f"MPNN: valid score = {list (valid_score.values())[0 ]:.4 f} " )
return model
4. AttentiveFP モデル訓練 def train_attentivefp (train_data, valid_data, tasks, n_epochs=50 ,
learning_rate=0.001 , num_layers=2 ):
"""
AttentiveFP (Attention-based Fingerprint) 訓練。
Parameters:
train_data: dc.data.Dataset — 訓練データ
valid_data: dc.data.Dataset — 検証データ
tasks: list — タスク名
num_layers: int — GATレイヤー数
"""
model = dc.models.AttentiveFPModel(
n_tasks=len (tasks),
mode="classification" if len (tasks) > 1 else "regression" ,
learning_rate=learning_rate,
num_layers=num_layers,
graph_feat_size=200 ,
num_timesteps=2 ,
)
model.fit(train_data, nb_epoch=n_epochs)
metric = dc.metrics.Metric(
dc.metrics.roc_auc_score if len (tasks) > 1
else dc.metrics.pearson_r2_score
)
valid_score = model.evaluate(valid_data, [metric])
print (f"AttentiveFP: valid score = {list (valid_score.values())[0 ]:.4 f} " )
return model
5. ChemBERTa 分子表現学習 def chemberta_embeddings (smiles_list, model_name="seyonec/ChemBERTa-zinc-base-v1" ):
"""
ChemBERTa で SMILES → 分子埋込みベクトル。
Parameters:
smiles_list: list — SMILES 文字列リスト
model_name: str — HuggingFace モデル名
"""
from transformers import AutoTokenizer, AutoModel
import torch
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
model.eval ()
embeddings = []
batch_size = 32
for i in range (0 , len (smiles_list), batch_size):
batch = smiles_list[i:i+batch_size]
inputs = tokenizer(batch, padding=True , truncation=True ,
max_length=512 , return_tensors="pt" )
with torch.no_grad():
outputs = model(**inputs)
cls_emb = outputs.last_hidden_state[:, 0 , :].numpy()
embeddings.append(cls_emb)
embeddings = np.vstack(embeddings)
print (f"ChemBERTa: {len (smiles_list)} molecules → "
f"{embeddings.shape[1 ]} D embeddings" )
return embeddings
6. モデル比較ベンチマーク def benchmark_models (dataset_name="tox21" , models_to_test=None ,
n_epochs=30 ):
"""
複数モデルのベンチマーク比較。
Parameters:
dataset_name: str — MoleculeNet データセット
models_to_test: list — テストモデル名
n_epochs: int — エポック数
"""
if models_to_test is None :
models_to_test = ["GCN" , "MPNN" , "AttentiveFP" ]
results = {}
for model_name in models_to_test:
featurizer = "GraphConv" if model_name != "ECFP_RF" else "ECFP"
tasks, (train, valid, test), transformers = load_moleculenet(
dataset_name, featurizer=featurizer
)
is_classification = len (tasks) > 1 or dataset_name in [
"tox21" , "bbbp" , "hiv" , "sider" , "clintox"
]
if model_name == "GCN" :
model = train_gcn(train, valid, tasks, n_epochs=n_epochs)
elif model_name == "MPNN" :
model = train_mpnn(train, valid, tasks, n_epochs=n_epochs)
elif model_name == "AttentiveFP" :
model = train_attentivefp(train, valid, tasks, n_epochs=n_epochs)
else :
continue
metric = dc.metrics.Metric(
dc.metrics.roc_auc_score if is_classification
else dc.metrics.pearson_r2_score
)
test_score = model.evaluate(test, [metric])
results[model_name] = list (test_score.values())[0 ]
print (f"\nBenchmark on '{dataset_name} ':" )
for name, score in sorted (results.items(), key=lambda x: -x[1 ]):
print (f" {name} : {score:.4 f} " )
return results
7. 分子特性予測パイプライン def molecular_prediction_pipeline (smiles_list, property_name="solubility" ,
model_type="AttentiveFP" ):
"""
SMILES → 分子特性予測 統合パイプライン。
Parameters:
smiles_list: list — SMILES リスト
property_name: str — 予測対象物性
model_type: str — 使用モデル
"""
property_dataset = {
"solubility" : "delaney" ,
"toxicity" : "tox21" ,
"bbb_penetration" : "bbbp" ,
"hiv_activity" : "hiv" ,
"lipophilicity" : "lipo" ,
"solvation_energy" : "freesolv" ,
}
dataset_name = property_dataset.get(property_name, "delaney" )
tasks, (train, valid, test), transformers = load_moleculenet(
dataset_name, featurizer="GraphConv"
)
if model_type == "GCN" :
model = train_gcn(train, valid, tasks)
elif model_type == "AttentiveFP" :
model = train_attentivefp(train, valid, tasks)
else :
model = train_mpnn(train, valid, tasks)
featurizer = dc.feat.MolGraphConvFeaturizer()
features = featurizer.featurize(smiles_list)
pred_dataset = dc.data.NumpyDataset(X=features)
predictions = model.predict(pred_dataset)
results = []
for smi, pred in zip (smiles_list, predictions):
results.append({
"smiles" : smi,
"prediction" : float (pred[0 ]) if pred.ndim > 1 else float (pred),
"property" : property_name,
"model" : model_type,
})
df = pd.DataFrame(results)
print (f"Predictions: {len (df)} molecules, property='{property_name} '" )
return df
パイプライン統合 cheminformatics → deep-chemistry → drug-target-profiling
(RDKit/SMILES) (GCN/MPNN/FP) (ChEMBL/標的)
│ │ ↓
molecular-docking ───────┘ admet-pharmacokinetics
(AutoDock/Vina) │ (ADMET予測)
↓
md-simulation
(分子動力学検証)
パイプライン出力 ファイル 説明 次スキル results/predictions.csv分子特性予測値 → drug-target-profiling results/benchmark.jsonモデルベンチマーク結果 — results/embeddings.npyChemBERTa 埋込み → cheminformatics results/model/訓練済みモデル → admet-pharmacokinetics
ToolUniverse 連携 TU Key ツール名 連携内容 chemblChEMBL 化学的活性・化合物データ検索
