Additional file 1 of Explaining and avoiding failure modes in goal-directed generation of small molecules
Creators: name: Langevin, Maxime
name: Vuilleumier, Rodolphe
name: Bianciotto, Marc
Publisher: name: figshare
Year: 2022
Subjects: subject: Artificial Intelligence and Image Processing
subject: FOS: Computer and information sciences

description: Additional file 1: Table S1. Selection of questionable active compounds from the DRD2 dataset. Figure S1. Random set of the final samples generated on the ALDH1 dataset with the graph genetic algorithm. Figure S2. Random set of the final samples generated on the ALDH1 dataset with the SMILES-LSTM algorithm. Those chemical structures are highly non drug-like due to e.g., the repetition of tetrazole rings, long chains with sulphur-sulphur bonds and other heteroaromatic-heteroaromatic single bonds. Figure S3. Random set of the final samples generated on the ALDH1 dataset with the MSO algorithm. Figure S4. Random set of the final samples generated on the JAK2 (with modified architecture for the predictive model) dataset with the graph genetic algorithm. Figure S5. Random set of the final samples generated on the JAK2 (with modified architecture for the predictive model) dataset with the SMILES-LSTM algorithm. We observed similar non drug-like patterns as in Additional file 1: Figure S2. Figure S6. Random set of the final samples generated on the JAK2 (with modified architecture for the predictive model) dataset with the MSO algorithm. Figure S7. Sopt and Smc in the DRD2, EGFR and JAK2 Topliss-augmented datasets. From top to bottom: hexbin plots (log scale) of data control as a function of optimization score; Mean Average Difference between Smc and Sopt as a function of Sopt (at absciss x, the MAD plotted is the MAD for molecules with optimization scores higher than x); distribution of Smc (95 CI) as a function of optimization score. For the second and third row, the lines and boxplots stop at absciss xmax for which there is no more samples with optimization scores higher than xmax. Figure S8. Sopt and Smc in the ALDH1 and JAK2 modified Topliss-augmented datasets. From top to bottom: hexbin plots (log scale) of data control as a function of optimization score; Mean Average Difference between Smc and Sopt as a function of Sopt (at absciss x, the MAD plotted is the MAD for molecules with optimization scores higher than x); distribution of Sdc (95 CI) as a function of optimization score. For the second and third row, the lines and boxplots stop at absciss xmax for which there is no more samples with optimization scores higher than xmax. Figure S9. Sopt and Sdc in the DRD2, EGFR and JAK2 datasets without Topliss augmentation. The fact that only a few molecules have a high Sopt makes the evaluation of related quantities noisier than with data augmentation. From top to bottom: hexbin plots (log scale) of data control as a function of optimization score; Mean Average Difference between Sdc and Sopt as a function of Sopt (at absciss x, the MAD plotted is the MAD for molecules with optimization scores higher than x); distribution of Smc (95 CI) as a function of optimization score. For the second and third row, the lines and boxplots stop at absciss xmax for which there is no more samples with optimization scores higher than xmax. Figure S10. Sopt and Sdc in the DRD2 (ROC-AUC: 0.78, Average Precision: 0.2), EGFR (ROC-AUC:0.93, Average Precision: 0.67) and JAK2 (ROC-AUC:0.69, Average Precision: 0.41) Topliss-augmented datasets, when the classifiers use a combination of physico-chemical descriptors (number of hydrogen bonds donors and acceptors, number of rings, number of rotatable bonds, total polar surface area, Crippen descriptors (ClogP and molar refractivity), molecular weight, fraction of SP3 carbons, the ratio of atoms in the Murcko scaffold on the total number of heavy atoms, the number of heavy atoms, the maximum and minimum cycle size, the minimal, maximal and total charge and the number of chiral centers, as implemented in the RDKit). From top to bottom: hexbin plots (log scale) of data control as a function of optimization score; Mean Average Difference between Sdc and Sopt as a function of Sopt (at absciss x, the MAD plotted is the MAD for molecules with optimization scores higher than x); distribution of Smc (95 CI) as a function of optimization score. For the second and third row, the lines and boxplots stop at absciss xmax for which there is no more samples with optimization scores higher than xmax. Figure S11. Sopt and Sdc in the DRD2 (ROC-AUC: 0.95, Average Precision: 0.56), EGFR (ROC-AUC:0.88, Average Precision: 0.24) and JAK2 (ROC-AUC:0.67, Average Precision: 0.37) Topliss-augmented datasets, when the classifiers use Atom-Pair descriptors (as implemented in the RDKit). From top to bottom: hexbin plots (log scale) of data control as a function of optimization score; Mean Average Difference between Sdc and Sopt as a function of Sopt (at absciss x, the MAD plotted is the MAD for molecules with optimization scores higher than x); distribution of Smc (95 CI) as a function of optimization score. For the second and third row, the lines and boxplots stop at absciss xmax for which there is no more samples with optimization scores higher than xmax.