{ "cells": [ { "cell_type": "markdown", "id": "9278795d", "metadata": {}, "source": [ "# Data Sets" ] }, { "cell_type": "markdown", "id": "5842085e", "metadata": {}, "source": [ "## XXX Database\n", "\n", "### Abstract\n", "The XXX data set is a multivariate data set used for efficient and accurate density-based prediction of\n", "macromolecular molecular properties. This data sets consists of 11 different types of Information-theoretic approach quantities (ITA). The rows being the ITA values and the columns being: Shannon, Fisher, Fisher', GBP, E2, E3, R2, R3, G1, G2, G3, iso,\taniso.\n", "\n", "### Download\n", "- [Polar](./data/polar.csv1)\n", "- [nmr](./data/nmr_O.csv1)\n", "- [nucleophiles and electrophiles](./data/nucleophiles_and_electrophiles.xlsx1)\n", "- Currently covered [nucleophiles and electrophiles](./data/nucleophiles_and_electrophiles.zip1) molecule .xyz files.\n", "\n", "### How to cite\n", "When using this dataset, please make sure to cite the following two papers:\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "9d15caae", "metadata": {}, "outputs": [], "source": [ "from mlita import datasets\n", "\n", "# import some data to play with\n", "#zhaos = datasets.load_zhaos()\n", "#X = zhaos.data[:, :2] # we only take the first two features.\n", "#y = zhaos.target" ] }, { "cell_type": "markdown", "id": "b7be344f", "metadata": {}, "source": [ "## Mayr's Database\n", "\n", "\n", "### Abstract\n", "[Mayr's Database Of Reactivity Parameters](https://www.cup.lmu.de/oc/mayr/reaktionsdatenbank/) currently the database contains the reactivity parameters of 1256 nucleophiles and 344 electrophiles.\n", "\n", "According to $log\\ k20°C = sN(N + E)$\n", "\n", "### Download\n", "- Currently covered [nucleophiles](./data/nucleophiles.zip) molecule .xyz files.\n", "\n", "- Currently covered [electronphiles](./data/electronphiles.zip) molecule .xyz files. \n", "\n", "- Covered [nucleophiles](./data/nucleophiles.csv) range: -8.80 ≤ N ≤ 30.82\n", "\n", "- Covered [electrophiles](./data/electrophiles.csv) range: -29.60 ≤ E ≤ 8.02 " ] }, { "cell_type": "code", "execution_count": null, "id": "6e0df5ef", "metadata": {}, "outputs": [], "source": [ "from mlita import datasets\n", "\n", "# import some data to play with\n", "#mayrs = datasets.load_mayrs()\n", "#X = mayrs.data[:, :2] # we only take the first two features.\n", "#y = mayrs.target" ] }, { "cell_type": "code", "execution_count": null, "id": "940a29af", "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": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }