metadata
configs:
- config_name: default
data_files:
- split: train
path: Prepared Dataset for ML/train-*
dataset_info:
features:
- name: >-
Catalyst_CC(C)c1cc(C(C)C)c(-c2ccccc2[PH-](C(C)(C)C)C(C)(C)C)c(C(C)C)c1.O=S(=O)([O-])C(F)(F)F.[NH-]c1ccccc1-c1ccccc1[Pd+3]
dtype: bool
- name: >-
Catalyst_CC(C)c1cc(C(C)C)c(-c2ccccc2[PH-](C2CCCCC2)C2CCCCC2)c(C(C)C)c1.O=S(=O)([O-])C(F)(F)F.[NH-]c1ccccc1-c1ccccc1[Pd+3]
dtype: bool
- name: >-
Catalyst_COc1ccc(OC)c([PH-](C(C)(C)C)C(C)(C)C)c1-c1c(C(C)C)cc(C(C)C)cc1C(C)C.O=S(=O)([O-])C(F)(F)F.[NH-]c1ccccc1-c1ccccc1[Pd+3]
dtype: bool
- name: >-
Catalyst_COc1ccc(OC)c([PH-](C23CC4CC(CC(C4)C2)C3)C23CC4CC(CC(C4)C2)C3)c1-c1c(C(C)C)cc(C(C)C)cc1C(C)C.O=S(=O)([O-])C(F)(F)F.[NH-]c1ccccc1-c1ccccc1[Pd+3]
dtype: bool
- name: Aryl Halide_Brc1ccccn1
dtype: bool
- name: Aryl Halide_Brc1cccnc1
dtype: bool
- name: Aryl Halide_CCc1ccc(Br)cc1
dtype: bool
- name: Aryl Halide_CCc1ccc(Cl)cc1
dtype: bool
- name: Aryl Halide_CCc1ccc(I)cc1
dtype: bool
- name: Aryl Halide_COc1ccc(Br)cc1
dtype: bool
- name: Aryl Halide_COc1ccc(Cl)cc1
dtype: bool
- name: Aryl Halide_COc1ccc(I)cc1
dtype: bool
- name: Aryl Halide_Clc1ccccn1
dtype: bool
- name: Aryl Halide_Clc1cccnc1
dtype: bool
- name: Aryl Halide_FC(F)(F)c1ccc(Br)cc1
dtype: bool
- name: Aryl Halide_FC(F)(F)c1ccc(Cl)cc1
dtype: bool
- name: Aryl Halide_FC(F)(F)c1ccc(I)cc1
dtype: bool
- name: Aryl Halide_Ic1ccccn1
dtype: bool
- name: Aryl Halide_Ic1cccnc1
dtype: bool
- name: Base_CCN=P(N=P(N(C)C)(N(C)C)N(C)C)(N(C)C)N(C)C
dtype: bool
- name: Base_CN(C)C(=NC(C)(C)C)N(C)C
dtype: bool
- name: Base_CN1CCCN2CCCN=C12
dtype: bool
- name: Additives_CCOC(=O)c1cc(C)no1
dtype: bool
- name: Additives_CCOC(=O)c1cc(C)on1
dtype: bool
- name: Additives_CCOC(=O)c1cc(OC)no1
dtype: bool
- name: Additives_CCOC(=O)c1ccon1
dtype: bool
- name: Additives_CCOC(=O)c1cnoc1
dtype: bool
- name: Additives_CCOC(=O)c1cnoc1C
dtype: bool
- name: Additives_COC(=O)c1cc(-c2ccco2)on1
dtype: bool
- name: Additives_COC(=O)c1cc(-c2cccs2)on1
dtype: bool
- name: Additives_COC(=O)c1ccno1
dtype: bool
- name: Additives_Cc1cc(-c2ccccc2)on1
dtype: bool
- name: Additives_Cc1cc(-n2cccc2)no1
dtype: bool
- name: Additives_Cc1cc(C)on1
dtype: bool
- name: Additives_Cc1ccno1
dtype: bool
- name: Additives_Cc1ccon1
dtype: bool
- name: Additives_Fc1cccc(F)c1-c1ccno1
dtype: bool
- name: Additives_c1ccc(-c2ccno2)cc1
dtype: bool
- name: Additives_c1ccc(-c2ccon2)cc1
dtype: bool
- name: Additives_c1ccc(-c2cnoc2)cc1
dtype: bool
- name: Additives_c1ccc(-c2ncno2)cc1
dtype: bool
- name: Additives_c1ccc(CN(Cc2ccccc2)c2ccno2)cc1
dtype: bool
- name: Additives_c1ccc(CN(Cc2ccccc2)c2ccon2)cc1
dtype: bool
- name: Additives_c1ccc2nocc2c1
dtype: bool
- name: Additives_c1ccc2oncc2c1
dtype: bool
- name: yield
dtype: float64
splits:
- name: train
num_bytes: 58751
num_examples: 4312
download_size: 83432
dataset_size: 58751
BIOINF595 W2025 Bioactivity Project Dataset
Author: Carl Mauro
The reaction data used in this project is from the following publication, accessed through the Open Reaction Database (https://open-reaction-database.org/). The original data is used under an MIT license, and is under copyright by the original authors (see LICENSE.txt file for details).
Ahneman, D. T.; Estrada, J. G.; Lin, S.; Dreher, S. D.; Doyle, A. G.
Predicting Reaction Performance in C–N Cross-Coupling Using Machine Learning.
Science 2018, 360 (6385), 186–190. https://doi.org/10.1126/science.aar5169.
Includes python scripts used to download the dataset, sanitize molecular SMILES strings, then train an H2O AutoML model on the data to predict reaction yields.
The original, unchanged dataset downloaded directly from the ORD data repository is stored under the "Original Dataset" directory. The processed data with sanitized SMILES strings is stored under the "Sanitized Dataset" directory. The dataset prepared using one-hot encoding (to enable the training of the H2O AutoML model) is stored under the "Prepared Data" directory.
Scripts are stored in the src/ directory and should be used in numerical order by name. The purpose of each script is described below:
01.install_packages.py --> This script includes all python packages used across all scripts.
The user should check which packages they do not yet have installed and install any missing ones.
It is recommended that all packages be installed to a unique Conda environment set up for handling this dataset & associated ML model.
02.download_dataset.py --> This script is used to download the dataset directly from the ORD data repository on GitHub.
Further details can be found at https://github.com/open-reaction-database
03.sanitize_data.py --> This script uses the MolVS package to convert the molecular SMILES strings in the original dataset into canonical SMILES strings
(i.e., to perform'sanitization'). The user should input the original dataset saved as a .csv file (Here, "Ahneman_ORD_Data.csv").
The script will output a new .csv file ("Sanitized_Ahneman_ORD_Data.csv") that is identical in structure to the original,
but with the sanitized SMILES strings.
04.prepare_data_for_ML.py --> This script takes the sanitized dataset as an input and performs one-hot enconding in order to prepare the data to be used in the
H2O AutoML model. A new .csv file ("Prepared_Data.csv") is created to save the dataset after one-hot encoding.
05.run_autoML_updated.py --> This script takes in the one-hot encoded reaction data and splits it into training and test sets (70%/30%).
The data is used to train an H2O AutoML model (maximum 8 models, omitting stacked ensemble models). After training
the H2O AutoML model, the best-performing model suggested by AutoML is selected and analyzed by SHAP analysis. A loss curve
is also generated for the model, along with a plot comparing the predicted reaction yields from the validation set to the actual
yields included in the original dataset.
06.upload_to_huggingface.py --> This script was used to upload datasets used and generated for this project to this Huggingface repository.
The datasets package must be installed to run this script.