FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization

Recently,
deep generative models have been regarded as promising
tools in fragment-based drug design (FBDD). Despite the growing interest
in these models, they still face challenges in generating molecules
with desired properties in low data regimes. In this study, we propose
a novel flow-based autoregressive model named FFLOM for linker and
R-group design. In a large-scale benchmark evaluation on ZINC, CASF,
and PDBbind test sets, FFLOM achieves state-of-the-art performance
in terms of validity, uniqueness, novelty, and recovery of the generated
molecules and can recover over 92% of the original molecules in the
PDBbind test set (with at least five atoms). FFLOM also exhibits excellent
potential applicability in several practical scenarios encompassing
fragment linking, PROTAC design, R-group growing, and R-group optimization.
In all four cases, FFLOM can perfectly reconstruct the ground-truth
compounds and generate over 74% of molecules with novel fragments,
some of which have higher binding affinity than the ground truth.