DOI: 10.5281/zenodo.17960302
title: Supplemental data of ChEMBL-Derived Benchmark Dataset and Computational Results of Boltz-2-Based Binding Affinity Prediction

description:  

Supplemental data of a paper entitled "ChEMBL-Derived Benchmark Dataset and Computational Results of Boltz-2-Based Binding Affinity Prediction". 

Data S1 The amino acid sequence of the target proteins. The data contain protein targets with their UniProt IDs and target names, the corresponding PDB entries and strand IDs, and the amino acid sequences. They also contain sequence length and the sequence identity (%) for each listed target.

Data S2 The benchmark dataset and prediction results using NIM-light. The data contain compounds associated with a target ID (UniProt ID), including each compound’s ChEMBL ID and SMILES string, along with the experimental pChEMBL value. They also contain the prediction results (confidence score, predicted affinity value and predicted binding probability) with the compound structural similarity and basic molecular properties (MW and number of RB, HA, HBD, and HBA).

Data S3 A KNIME workflow for creating the benchmark dataset

Data S4 The calculation time of the affinity predictions. The data contain calculation times for the affinity predictions using the original Boltz-2 , NIM-original, and NIM-light. The time for each target is listed with its amino acid sequence, and sequence length.

Data S5 Dataset A and B and the prediction results using the original Boltz-2, NIM-original, and NIM-light. The data contain experimental pChEMBL values for compound–target pairs in the dataset B. They also contain the prediction results (confidence score, predicted affinity value, and predicted binding probability) of the original Boltz-2, NIM-original, and NIM-light settings. The rows that contain prediction results of the original Boltz-2 setting correspond to the dataset A.

Data S6 Benchmark dataset for predictive performance analysis and the prediction results. The data contain experimental pChEMBL values for compound–target pairs in the benchmark dataset for predictive performance analysis with the prediction results (confidence score, predicted affinity value, predicted binding probability) using NIM-light. They also contain the prediction error (difference between the predicted affinity and pChEMBL value), compound structural similarity, and basic molecular properties (MW and number of RB, HA, HBD, and HBA).

Data S7 Figures of relationships between the pChEMBL and predicted affinity values for each target

Data S8 Predictive performances for each target. The data contain the five performance metrics (Pearson r, Spearman ρ, Kendall τ, MAE, and RMSE) of the compound–target affinity prediction task for each target.

Data S9 Figures of relationships between the sequence identities and prediction errors for each target

Data S10 Figures of relationships between the confidence scores and prediction errors for each target

Data S11 Figures of relationships between the compound structural similarities and prediction errors for each target

Data S12 Figures of relationships between the MW and prediction errors for each target

Data S13 Figures of relationships between the numbers of RB and prediction errors for each target

Data S14 Figures of relationships between the numbers of HA and prediction errors for each target

Data S15 Figures of relationships between the numbers of HBD and prediction errors for each target

Data S16 Figures of relationships between the numbers of HBA and prediction errors for each target

Data S17 Prediction results for a subset of ChEMBL34. The data contain experimental pChEMBL values for compound–target pairs in a subset of ChEMBL34 with the prediction results (confidence score, predicted affinity value, predicted binding probability) using NIM-light.

 

#File names 

Data S1: Data_S1_target_aaseq.csv

Data S2: Data_S2_benchmark_dataset.csv

Data S3: Data_S3_workflow_for_dataset_preparation.knwf

Data S4: Data_S4_prediction_calc_time.csv

Data S5: Data_S5_dataset_AB.csv

Data S6: Data_S6_benchmark_dataset_for_predictive_performance.csv

Data S7: Data_S7_figures_pChEMBL_prediction_for_each_target.zip

Data S8: Data_S8_predictive_performances_for_each_target.csv

Data S9: Data_S9_figures_seq_identity_prediction_error_for_each_target.zip

Data S10: Data_S10_figures_confidence_score_prediction_error_for_each_target.zip

Data S11: Data_S11_figures_compound_sim_prediction_error_for_each_target.zip

Data S12: Data_S12_figures_MW_prediction_error_for_each_target.zip

Data S13: Data_S13_figures_nRB_prediction_error_for_each_target.zip

Data S14: Data_S14_figures_nHA_prediction_error_for_each_target.zip

Data S15: Data_S15_figures_nHBD_prediction_error_for_each_target.zip

Data S16: Data_S16_figures_nHBA_prediction_error_for_each_target.zip

Data S17: Data_S17_ChEMBL34_results.csv