תכנון מולקולרי הפוך באופטימיזציה בייסית סגורה עם סורוגטים LLM

תקציר מקורי באנגליתarXiv:2608.22967v2 Announce Type: replace Abstract: Practical molecular inverse design is rarely a one-shot generation problem; it often takes the form of closed-loop candidate-pool enrichment, where under a limited oracle budget the goal is to increase the fraction of generated molecules that match a desired property profile. Bayesian optimization (BO) offers a natural framework for this setting, yet standard Gaussian-process surrogates typically operate in compressed continuous embeddings, which discard the substructural and reference-similarity signals that chemists naturally use to decide where to look next. We propose BoMolLLM, a closed-loop framework in which the surrogate, rather than the generator, is treated as the locus of design choice, and instantiate it with a frozen large lan