Additional file 1 of Harnessing Shannon entropy-based descriptors in machine learning models to enhance the prediction accuracy of molecular properties
Creators: name: Guha, Rajarshi
name: Velegol, Darrell
Publisher: name: figshare
Year: 2024
Subjects: subject: Biochemistry
subject: Genetics
subject: FOS: Biological sciences
subject: Neuroscience
subject: Biotechnology
subject: Chemical Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Plant Biology

description: Additional file 1. Figures and Tables: Fig.S1. Lower correlation of Shannon entropy (SMILES) to other standard descriptors. Fig.S2. Shannon entropies based on standard tokens derived from string representations (SMILES, SMARTS, INCHIKEY etc.) of molecules are efficient descriptors for deep neural network-based property predictions. Fig.S3. A hybrid neural network combining MLP and CNN models shows comparable prediction performance to only MLP-based models in both classification and regression problems. Fig.S4. Schematic with a stepwise depiction of the used algorithm of the hybrid MLP + 3D GNN-based model to predict the logP values of binding molecules to the p53-binding protein Mdm2. Fig.S5. Deep neural network model with MLP and 2D GNN architectures in an ensemble performs better than only the 2D GNN based model and the prediction accuracy depends on the connections from MLP layers to the final, hybrid model. Fig.S6. Schematic presentations of (-2,-4) and (-2,-3) connections from the last few MLP layers to the final model of the hybrid MLP + 2D/ 3D GNN-based deep neural network. Table S1a. An example of numerical reduction of a molecule in the form of Shannon entropy. Table S1b. An example of numerical reduction of a molecule in the form of fractional Shannon entropy. Table S2. Stereochemistry-sensitive numerical reduction of molecules in the form of Shannon entropies by using a combination of SMILES and InChiKey strings. Table S3. Network performance metrics for prediction of IC50 values of binding molecules to tissue factor pathway inhibitor (target: pCheMBL/MW, MLP-based deep neural model). Table S4. Network performance metrics for prediction of BEI values of binding molecules to the tissue factor pathway inhibitor (target: BEI /MW, MLP-based deep neural model). Table S5. Network performance metrics for prediction of IC50 values of binding molecules to tissue factor pathway inhibitor in tandem approach (target: pCheMBL/MW, MLP-based deep neural model). Table S6. Network performance metrics for prediction of Ki values of binding molecules to coagulation factor 11 (target: pCheMBL/MW, MLP-based deep neural model). Table S7. Network performance metrics for prediction of Ki values of binding molecules to coagulation factor 11 (target: pCheMBL/MW, MLP-based deep neural models). Table S8. Network performance metrics for toxicity classification as per Ames mutagenicity (target: toxicity binary classification, MLP-based &amp; CNN&amp;MLP –based deep neural models). Table S9. Network performance metrics for prediction of Ki values of binding molecules to coagulation factor 11 (target: pCheMBL/MW, MLP-based and CNN&amp;MLP –based deep neural models). Table S10. Network performance metrics for prediction of partition coefficient (logP) values of binding molecules to the p53-binding protein Mdm2 (target: logP) and IC50 (pCheMBL) values of target ID CHEMBL4691. Table S11. Network performance metrics for prediction of BEI and pChEMBL values across different target datasets using MLP-based deep neural network architecture. Table S12. Comparison of Morgan, SEF, SHED, SEF+Morgan and SEF+SHED descriptors in random forest regression-based models. Table S13. Features used in constructing SEF descriptors for optimum performance in random forest regression-based models. Table S14. Comparison of Morgan, SEF and SHED descriptors in random forest regression-based models with completely random y-label targets.