Additional file 1 of LOGICS: Learning optimal generative distribution for designing de novo chemical structures
Creators: name: Bae, Bongsung
name: Bae, Haelee
name: Nam, Hojung
Publisher: name: figshare
Year: 2024
Subjects: subject: Biophysics
subject: Biochemistry
subject: Pharmacology
subject: Chemical Sciences not elsewhere classified
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified

description: Additional file 1: Figure S1. Bioactivity label density plot of the KOR (orange) and PIK3CA (navy blue) bioassay datasets. Figure S2. PIK3CA bioactivity optimization performance comparison of GPC methods using three different sets of test set actives formed by each activity cutoff (&gt; 6.0, &gt; 8.0, &gt; 10.0). (a) FCD and (b) OTD are calculated with each test set actives described in Table S1. Figure S3. Example of FCD and OTD between synthetic 2-D data points of target and generations. Two modes are assumed at (1,1) and (11,11) for the target distribution (blue). The points of generation1 (orange) were formed by adding (1,-1) to the target points. The points of generation2 (green) were sampled from the Gaussian distribution with the mean and covariance calculated by the target points. The 2-D Euclidean distance was used for OTD calculation. Figure S4. Predictor regression performance on KOR and PIK3CA test sets. The x-axis represents the pIC50 or pKx value of the test set molecule, and the y-axis represents the predicted activity by the predictor. The red dotted line is the regression line of true-to-predicted values. Figure S5. General overview of generator-predictor collaboration (GPC). Figure S6. Performance plot of GPC models during the fine-tuning phase of the KOR activity optimization case. PwSim, FCD, and OTD were calculated with the test set actives. The x-axis corresponds to the number of iterations in the fine-tuning. The vertical dotted line is the best-stopping epoch under the conditions: (1) PredAct &gt; 7.0, (2) minimum FCD × OTD on the validation set actives. Figure S7. Performance plot of GPC models during the fine-tuning phase of the PIK3CA activity optimization case. PwSim, FCD, and OTD were calculated with the test set actives. The x-axis corresponds to the number of iterations used in the fine-tuning. The vertical dotted line represents the best-stopping epoch under the conditions: (1) PredAct &gt; 8.0, (2) minimum FCD × OTD on the validation set actives. Figure S8. Density plots of docking scores (kcal/mol) on KOR (PDB ID: 4DJH) and PIK3CA (PDB ID: 8EXL) with generated compounds. a, b Docking score distribution of 4,000 generations from the prior generator (orange) and LOGICS fine-tuned (blue) for KOR and PIK3CA, respectively. Two-tailed t-test between the two distributions is performed to evaluate the p-values. Figure S9. Retrosynthetic prediction of the generated molecules shown in Fig. 6. The synthetic routes for (a), (b) generated molecules optimized for KOR activity, (c), (d). Table S1. The number of test set actives depending on the activity cutoff in the PIK3CA experiment. Table S2. Predictor model performance on test and validation sets for each protein target. Table S3. Reward function hyperparameters for policy gradient methods. Table S4. Tests of statistical significance in FCD and OTD metrics between LOGICS and the second-best models in Table 2. Table S5. Predictor performance with additional KOR datasets of varying reduced sizes and restricted structural diversity. Table S6. Performance of the LOGICS framework with the additional KOR datasets with reduced sizes and scaffold split. Table S7. Performance of REINVENT model for different σ and β reward function parameter pairs. Table S8. Performance comparison from the ablation study for the proposed LOGICS framework on the KOR and PIK3CA bioactivity optimization. Table S9. Performance comparison of the original ReLeaSE and ReLeaSE + on KOR bioactivity optimization.