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@ljmartin
Created March 1, 2024 00:28
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "28c889cc-a1b9-4faf-a101-e3fb3bff51cc",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from rdkit import Chem\n",
"from rdkit.Chem import AllChem\n",
"import numpy as np\n",
"from tqdm.notebook import tqdm\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "b1a24470-3faa-4336-96da-ff0aba7150cb",
"metadata": {},
"source": [
"# load tautobase dataset used in 10.1039/d1sc01185e\n",
"\n",
"Paper is: [Fitting quantum machine learning potentials to experimental free energy data: predicting tautomer ratios in solution](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409483/), Marcus Wieder, Josh Fass, and John D. Chodera"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "383a5c79-00a1-4976-a6ff-1aae333060c4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['name', 't1-smiles', 't2-smiles', 'dG'], dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>t1-smiles</th>\n",
" <th>t2-smiles</th>\n",
" <th>dG</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>SAMPLmol2</td>\n",
" <td>OC1=CC=C2C=CC=CC2=N1</td>\n",
" <td>O=C1NC2=C(C=CC=C2)C=C1</td>\n",
" <td>-6.10000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>SAMPLmol4</td>\n",
" <td>OC1=CC2=C(C=CC=C2)C=N1</td>\n",
" <td>O=C1NC=C2C=CC=CC2=C1</td>\n",
" <td>-2.30000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>tp_1000</td>\n",
" <td>Oc1nccnc1</td>\n",
" <td>O=C1NC=CN=C1</td>\n",
" <td>-2.59217</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name t1-smiles t2-smiles dG\n",
"0 SAMPLmol2 OC1=CC=C2C=CC=CC2=N1 O=C1NC2=C(C=CC=C2)C=C1 -6.10000\n",
"1 SAMPLmol4 OC1=CC2=C(C=CC=C2)C=N1 O=C1NC=C2C=CC=CC2=C1 -2.30000\n",
"2 tp_1000 Oc1nccnc1 O=C1NC=CN=C1 -2.59217"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tauts = pd.read_csv('https://raw.githubusercontent.com/choderalab/neutromeratio/master/data/ani_tautobase_subset.txt')\n",
"tauts = tauts.rename({' dG_{tau} [kcal/mol]':'dG', ' t1-smiles':'t1-smiles', ' t2-smiles':'t2-smiles'},axis=1)\n",
"print(tauts.columns)\n",
"tauts.head(3)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c06e7bd9-2a69-4966-943c-b9f308299360",
"metadata": {},
"outputs": [],
"source": [
"tauts['t1'] = [Chem.MolFromSmiles(i) for i in tauts['t1-smiles']]\n",
"tauts['t2'] = [Chem.MolFromSmiles(i) for i in tauts['t2-smiles']]"
]
},
{
"cell_type": "markdown",
"id": "84705e83-4678-4642-bc18-1e9c07fe638e",
"metadata": {},
"source": [
"# fingerprint the tauts\n",
"just use morgan for now. Larger always better to reduce bit collisions. Most bits will cancel out in the fitting algo, so overfitting is less of a concern. Can apply L2 regularization if desired, but I didn't. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9287abc0-18da-4be7-8363-14897c99d3b6",
"metadata": {},
"outputs": [],
"source": [
"fp1s = []\n",
"fp2s = []\n",
"for m1, m2 in zip(tauts['t1'], tauts['t2']):\n",
" fp1s.append(\n",
" np.array(AllChem.GetMorganFingerprintAsBitVect(mol=m1, radius=3, nBits=2048*8).ToList())\n",
" )\n",
" fp2s.append(\n",
" np.array(AllChem.GetMorganFingerprintAsBitVect(mol=m2, radius=3, nBits=2048*8).ToList())\n",
" )\n",
"fp1s = np.vstack(fp1s)\n",
"fp2s = np.vstack(fp2s)"
]
},
{
"cell_type": "markdown",
"id": "d8386001-533f-49c0-beb2-d075b4c4b8a8",
"metadata": {},
"source": [
"# fitting algo:\n",
"just using regression here. Not using sklearn because the loss function is a _relative_ energy between two pairs, i.e. tautomer2-tautomer1. So use JAX instead. \n",
"\n",
"note to keep dataset size the same as Wieder et al, where a neural net was used, I'm using a training set, validation set, and test set. Sizes are the same as in the paper. Training stops when the RMSE gets worse on the validation set. May have mixed up the terminology for validation and test, but they are the same size so as long as they are applied consistently (no peeking) the terminology doesn't affect the result. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f8b54847-bfc9-49bc-aa90-a4fe21d3d4dd",
"metadata": {},
"outputs": [],
"source": [
"from jax import numpy as jnp\n",
"from jax import value_and_grad\n",
"from sklearn.metrics import root_mean_squared_error\n",
"\n",
"def makepred(params, x1, x2):\n",
" #commented out biases (intercepts) because they cancel out.\n",
" y_pred1 = jnp.dot(x1, params['weights'])# + params['bias']\n",
" y_pred2 = jnp.dot(x2, params['weights'])# + params['bias']\n",
" #added single bias here after subtracting the taut energy:\n",
" y_pred = y_pred2-y_pred1 + params['bias']\n",
" return y_pred\n",
" \n",
"def ridge_regression(params, x1, x2, y, alpha):\n",
" y_pred = makepred(params, x1, x2)\n",
" mse = jnp.mean((y - y_pred)**2)\n",
" # L2 reg:\n",
" regularization_term = 0.5 * alpha * jnp.sqrt(jnp.sum(params['weights'] ** 2))\n",
" total_loss = mse + regularization_term\n",
" return total_loss\n",
"\n",
"def update_params(params, grads, learning_rate):\n",
" new_params = {}\n",
" for key in params.keys():\n",
" new_params[key] = params[key] - learning_rate * grads[key]\n",
" return new_params\n",
"\n",
"def fitmodel(alpha, x1, x2, y, val_x1, val_x2, val_y):\n",
" params = {'weights': np.random.randn(x1.shape[1])/10,'bias': 0.}\n",
" learning_rate = 0.1\n",
" losses = []\n",
" validation_rmses = []\n",
"\n",
" loss = ridge_regression(params, x1, x2, y, alpha)\n",
" preds = makepred(params, val_x1, val_x2)\n",
" validation_rmse = root_mean_squared_error(val_y, preds)\n",
" losses.append(loss)\n",
" validation_rmses.append(validation_rmse)\n",
" \n",
" for _ in tqdm(range(1500)):\n",
" value, grads = value_and_grad(ridge_regression)(params, x1, x2, y, alpha)\n",
" preds = makepred(params, val_x1, val_x2)\n",
" rmse = root_mean_squared_error(val_y, preds)\n",
" \n",
" if rmse>validation_rmses[-1]:\n",
" break\n",
" validation_rmses.append(rmse)\n",
" losses.append(value)\n",
" params = update_params(params, grads, learning_rate)\n",
" return params, losses, validation_rmses"
]
},
{
"cell_type": "markdown",
"id": "c3812ea6-6757-40fb-bc47-615fe660b031",
"metadata": {},
"source": [
"# run monte carlo cross validation\n",
"15 runs of randomly selected train/test/val indices"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "12e8fd21-3fc5-4db5-bbe8-f34440875664",
"metadata": {},
"outputs": [
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{
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"source": [
"actual_scores = []\n",
"pred_scores = []\n",
"losses = []\n",
"validation_rmses = []\n",
"for i in tqdm(range(15)):\n",
" idx = np.arange(len(tauts))\n",
" np.random.shuffle(idx)\n",
"\n",
" test_idx = idx[:71] #wieder etal used 71 cmpds as the test set\n",
" train_idx = idx[71:212+71] #212 compounds as training set\n",
" val_idx = idx[212+71:] #71 cmpds as validation set. \n",
"\n",
" params, loss, validation_rmse = fitmodel(\n",
" 0, #alpha - higher means more regularization. Zero regularization seems to be best anyway. \n",
" fp1s[train_idx], \n",
" fp2s[train_idx], \n",
" tauts.iloc[train_idx]['dG'].values,\n",
" fp1s[val_idx],\n",
" fp2s[val_idx],\n",
" tauts.iloc[val_idx]['dG'].values,\n",
" )\n",
" losses.append(loss)\n",
" validation_rmses.append(validation_rmse)\n",
" pred_score = makepred(params, fp1s[test_idx], fp2s[test_idx],)\n",
" pred_scores.append(pred_score)\n",
" actual_scores.append(tauts.iloc[test_idx]['dG'].values)"
]
},
{
"cell_type": "markdown",
"id": "ac178f3e-7db1-4b47-80dc-22ad10faa361",
"metadata": {},
"source": [
"# plot stuff"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4a20f020-ac97-42c9-8002-3f55a15d7624",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for l in losses:\n",
" plt.plot(l)\n",
"plt.yscale('log')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b378b21a-d2c7-408e-99e4-f9ddf98f6ea8",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for vrmse in validation_rmses:\n",
" plt.plot(vrmse)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4bdb3ca5-4525-48c7-b01f-08990789467b",
"metadata": {},
"outputs": [],
"source": [
"rmses = [root_mean_squared_error(j,k) for j,k in zip(actual_scores, pred_scores)]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "5a7d62d8-37cc-411d-98ed-d2279dd77bd3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1476ae650>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from seaborn import kdeplot\n",
"\n",
"#bootstrapping interval\n",
"samples = [np.random.choice(rmses, 15) for _ in range(5000)]\n",
"means = np.array([s.mean() for s in samples])\n",
"lo = means[means.argsort()][100]\n",
"hi = means[means.argsort()][5000-100]\n",
"\n",
"kdeplot(rmses)\n",
"\n",
"plt.axvline(np.mean(rmses), c='k', linestyle='--',label=f'Mean RMSE={np.around(np.mean(rmses), 2)}')\n",
"y_min, y_max = plt.ylim()\n",
"plt.fill_betweenx([y_min, y_max], lo, hi, color='grey',alpha=0.2, \n",
" label=f\"95% Bootstrap\\nlow={np.around(lo, 2)}, high={np.around(hi,2)}\")\n",
"plt.vlines(rmses, y_min, y_max, alpha=0.3, label='10-fold MCCV')\n",
"plt.ylim(y_min, y_max)\n",
"plt.xlabel('RMSE')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e53ad650-8e2a-4255-8417-a50c3f268da5",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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