MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization

ArXi:2603.24382v1 Announce Type: new Despite deep learning's success in chemistry, its impact is hindered by a lack of interpretability and an inability to resolve activity cliffs, where minor structural nuances trigger drastic property shifts. Current representation learning, bound by the similarity principle, often fails to capture these structural-activity discontinuities. To address this, we