AI RESEARCH
Symbolic Neural Generation with Applications to Lead Discovery in Drug Design
arXiv CS.AI
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ArXi:2510.23379v2 Announce Type: replace-cross We investigate a relatively under-explored class of hybrid neurosymbolic models that integrate symbolic learning with neural reasoning to construct data generators meeting formal correctness criteria. In Symbolic Neural Generators (SNGs), symbolic learners examine logical specifications of feasible data from a small set of instances -- sometimes just one. Each specification in turn constrains the conditional information supplied to a neural-based generator, which rejects any instance violating the symbolic specification.