A single decoder-only model generates prompt-conditioned retrosynthetic routes and shows measurable gains on depth and required-leaf constraints in the RetroCast/PaRoutes benchmarks while releasing its code.
The syntax of matter: Synthesis planning as the foundation of generative chemistry.ChemRxiv, 2026(0421), 2026
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A review of classical and AI-assisted methods for modeling chemical disorder in atomistic simulations of alloys and complex materials.
A review of generative AI for inverse design of inorganic compounds, analyzing adaptations for their complexity in composition, geometry, symmetry, and electronic structure, with discussion of future benchmarks and synthesizability metrics.
citing papers explorer
-
Project Ariadne: Prompt-Conditioned Route Generation for Synthesis Planning
A single decoder-only model generates prompt-conditioned retrosynthetic routes and shows measurable gains on depth and required-leaf constraints in the RetroCast/PaRoutes benchmarks while releasing its code.
-
Atomistic Modeling of Chemical Disorder in Materials: Bridging Classical Methods and AI-Assisted Approaches
A review of classical and AI-assisted methods for modeling chemical disorder in atomistic simulations of alloys and complex materials.
-
Inverse Design of Inorganic Compounds with Generative AI
A review of generative AI for inverse design of inorganic compounds, analyzing adaptations for their complexity in composition, geometry, symmetry, and electronic structure, with discussion of future benchmarks and synthesizability metrics.