A Review on Fragment-based De Novo 2D Molecule Generation

arXiv:2405.05293v1 Announce Type: cross
Abstract: In the field of computational molecule generation, an essential task in the discovery of new chemical compounds, fragment-based deep generative models are a leading approach, consistently achieving state-of-the-art results in molecular design benchmarks as of 2023. We present a detailed comparative assessment of their architectures, highlighting their unique approaches to molecular fragmentation and generative modeling. This review also includes comparisons of output quality, generation speed, and the current limitations of specific models. We also highlight …

abstract architectures art arxiv assessment benchmarks chemical compounds computational cs.lg deep generative models design discovery generative generative models highlighting q-bio.bm results review state type