Supplementary Information for In silico QSAR and design of chalcone derivatives for HT-29 colorectal cancer: MLR and ANN approaches

This repository contains the supplementary data, raw datasets, and computational workflows supporting the research article: "In silico QSAR and design of chalcone derivatives for HT-29 colorectal cancer: MLR and ANN approaches" by Tony Nyo et al., published in Discover Chemistry. The study presents a comparative analysis of Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models to predict the anticancer activity of 193 chalcone derivatives against the HT-29 colorectal cancer cell line. The dataset is provided to ensure transparency, reproducibility, and validation of the QSAR models described in the main publication. Contents of this Repository: 1. Supplementary Tables:- Table S1: Complete dataset of 193 chalcone derivatives with SMILES notations and experimental pIC50 values.- Table S3: ANOVA statistics for the Stepwise MLR model.- Table S4: Correlation matrices showing inter-correlation between the 27 selected molecular descriptors.- Table S5: Comparative performance data for all tested ANN architectures (13i–4N–1O to 13i–10N–1O).- Table S6: Standardized regression coefficients and statistical significance for all 27 descriptors.- Table S7: Detailed Wilcoxon Signed-Rank Test results comparing absolute errors of MLR and ANN models. 2. Supplementary Files:- File S1: Full library of designed compounds with their calculated descriptors and predicted activities.- File S2: Full statistical calculation workflows for all validation parameters (R², Q², RMSEP, MAPE, etc.). 3. Supplementary Figures:- Figure S1: ANN Architecture (13i–8N–1O) illustrating the optimized feed-forward multilayer perceptron configuration.- Figure S2: Full ProTox-III Toxicity Prediction Profiles for the lead candidate Modifikasi_W_136. Funding:This research received no specific external grant funding. Software licenses and computational resources were provided through institutional support at Lambung Mangkurat University, Indonesia. Related Publication:Nyo, T., Triyasmono, L., & Santoso, U. T. (2025). In silico QSAR and design of chalcone derivatives for HT-29 colorectal cancer: MLR and ANN approaches. Discover Chemistry. License:Creative Commons Attribution 4.0 International (CC BY 4.0).