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Data sets and machine learning models for: Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates
<p>The datasets and final machine learning model files for the manuscript "Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates". Citation should refer directly to the manuscript:</p> <ul> <li>Chung, Y.; Green, W. H. Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates. <em>Chemical Science </em><strong>2024,</strong> doi: <a href="https://doi.org/10.1039/D3SC05353A">10.1039/D3SC05353A</a></li> </ul> <p>To use the machine learning models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML">https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML</a>. </p> <p>Detailed information can be found in README.md file.</p> <p><br><strong>Details on the files</strong></p> <p>In the pretraining and finetuning set csv files, each column represents:</p> <ol> <li>rxn_smiles: atom-mapped reaction SMILES</li> <li>solvent_smiles: solvent SMILES</li> <li>ddGsolv: solvation free energy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)</li> <li>ddHsolv: solvation enthalpy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)</li> <li>dGsolv_reactant: solvation free energy of reactant(s) at 298K in kcal/mol (additional feature)</li> <li>dGsolv_product: solvation free energy of product(s) at 298K in kcal/mol (additional feature)</li> <li>dHsolv_reactant: solvation enthalpy of reactant(s) at 298K in kcal/mol (additional feature)</li> <li>dHsolv_product: solvation enthalpy of product(s) at 298K in kcal/mol (additional feature)</li> </ol> <p><strong>Data sets under 'RxnSolvKSE_dataset_v1.1.zip'</strong></p> <ul> <li>pretraining_set: contains the dataset used for pre-training <ul> <li>all_data: contains all calculated data <ul> <li>pretraining_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: contains both main prediction targets and additional feature for reaction-solvent pairs</li> <li>pretraining_solvent_info.csv: list of all solvents</li> <li>pretraining_unique_rxn.csv: list of all reactions, both forward and reverse directions</li> </ul> </li> <li>chosen_500k_data: contains the chosen 500k data <ul> <li>pretraining_rxn_solvent_ddGsolv_ddHsolv_500k.csv: contains main prediction targets for reaction-solvent pairs</li> <li>pretraining_features_react_prod_dGsolv_dHsolv_500k.csv: contains additional features for reaction-solvent pairs</li> <li>train_test_split: contains the 5-fold random split training and test sets.</li> </ul> </li> </ul> </li> <li>finetuning_set: contains the dataset used for fine-tuning <ul> <li>all_data: contains all calculated data <ul> <li>finetuning_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: constains both main prediction targets and additional features for reaction-solvent pairs. The rxn_key column indicates whether the reaction is bimolecular hydrogen abstraction (bihabs), unimolecular hydrogen migration (intrahabs), or radical addition to a multiple bond (raddition). The 'fwd' and 'rev' each indicate forward and reverse reactions.</li> <li>finetuning_solvent_info.csv: list of all solvents</li> <li>finetuning_unique_rxn.csv: list of all reactions, both forward and reverse directions</li> </ul> </li> <li>chosen_data: contains chosen data <ul> <li>finetuning_rxn_solvent_ddGsolv_ddHsolv_chosen.csv: contains main prediction targets for reaction-solvent pairs</li> <li>finetuning_features_react_prod_dGsolv_dHsolv_chosen.csv: contains additional features for reaction-solvent pairs</li> </ul> </li> </ul> </li> <li>experimental_set: contains the experimental rate constant data used to test the model. The original experimental data can be found at <a href="../record/7747557">https://zenodo.org/record/7747557</a>. <ul> <li> expt_rxn_atom_mapped_smiles.csv: contains the atom-mapped reaction SMILES used for the experimental data.</li> <li>expt_data_collected.xlsx: contains all experimental data and detailed information</li> <li>expt_rxn_solv_smiles_with_features_all.csv: contains the computed additional features for the experimental reaction-solvent pairs.</li> </ul> </li> </ul> <p><strong>Machine learning model files under 'RxnSolvKSE_ML_model_files.zip'</strong></p> <ul> <li>Contains the Chemprop machine learning model files for predicting ddGsolv and ddHsolv for a reaction-solvent pair. It takes atom-mapped reaction SMILES and solvent SMILES as inputs.</li> <li>To use these ML models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML">https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML</a></li> </ul>
Implementation Trial of Predictive Modeling to Enhance Diagnosis and Improve Treatment in Pediatric Septic Shock
ClinicalTrials.gov study NCT05065333. IPD Sharing: YES. Countries: 1. Publications: 2.
Development and Validation of a Deep Learning-Based Survival Prediction Model for Pediatric Glioma Patients: A Retrospective Study Using the SEER Database and Chinese Data
ClinicalTrials.gov study NCT06199388. IPD Sharing: NO. Countries: 1. Publications: 2.
Construction of a Predictive Model of Gangrenous Cholecystitis Based on Machine Learning
ClinicalTrials.gov study NCT06399081. IPD Sharing: NO. Countries: 1. Publications: 3.
Use of Predictive Modeling to Improve Operating Room Scheduling Efficiency
ClinicalTrials.gov study NCT01892865. IPD Sharing: NO. Countries: 1. Publications: 1.
Using Clinical Prediction Models to Improve Treatment for Patients With Chronic Obstructive Pulmonary Disease (COPD)
ClinicalTrials.gov study NCT05309356. IPD Sharing: YES. Countries: 1. Publications: 1.
Coronary Imaging and Metabolic Indicators-Based Risk Prediction Model for Coronary Artery Disease(CMI-RiskCAD)
ClinicalTrials.gov study NCT07353762. IPD Sharing: NO. Countries: 1. Publications: 1.
Development and Evaluation of High Risk Group Prediction Model in T1 Stage Renal Cell Cancer Using Molecular Biomarkers
ClinicalTrials.gov study NCT03694912. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Prediction Model of Improvement of Disturbance of Consciousness in Patients With Hydrocephalus After Shunt Operation
ClinicalTrials.gov study NCT05237102. IPD Sharing: YES. Countries: 1. Publications: 3.
Predicting Ipsilesional Motor Deficits in Stroke With Dynamic Dominance Model
ClinicalTrials.gov study NCT03634397. IPD Sharing: YES. Countries: 1. Publications: 2.
Risk Factors and Prediction Model of Cancer-associated Venous Thromboembolism
ClinicalTrials.gov study NCT05729464. IPD Sharing: UNDECIDED. Countries: 1. Publications: 11.
Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence
ClinicalTrials.gov study NCT07328997. IPD Sharing: YES. Countries: 1. Publications: 30.
Evaluation of an Artificial Intelligence Model for the Prediction of Human Blastocyst Ploidy Without Invasive Procedures
ClinicalTrials.gov study NCT06762704. IPD Sharing: YES. Countries: 1. Publications: 23.
Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI
ClinicalTrials.gov study NCT06831357. IPD Sharing: NO. Countries: 1. Publications: 8.
Predicting Pathological Complete Response in Esophageal Squamous Cell Carcinoma Using a Multimodal Model Integrating Clinical, Radiomics, and Deep Learning Features
ClinicalTrials.gov study NCT07181850. IPD Sharing: NO. Countries: 1. Publications: 4.
The Prediction of Recurrence Lumbar Disc Herniation At L5-S1 Level Through Machine Learning Models Based on Endoscopic Discectomy Via the Interlaminar Approach
ClinicalTrials.gov study NCT06833099. IPD Sharing: NO. Countries: 1. Publications: 20.
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer
ClinicalTrials.gov study NCT06092450. IPD Sharing: NO. Countries: 1. Publications: 1.
SCI-VIP: Predictive Outcome Model Over Time for Employment (PrOMOTE)
ClinicalTrials.gov study NCT01141647. IPD Sharing: NO. Countries: 1. Publications: 5.
Predictive Executive Functioning Models Using Interactive Tangible-Graphical Interface Devices
ClinicalTrials.gov study NCT01711372. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control
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