arXiv subjects

Nguyen Xuan-Vu

Publications and source records attributed to Nguyen Xuan-Vu.

4 recordsLinked to original sources

arXiv2026

Steerable Single-step Retrosynthesis with Electron-pushing Discrete Flow Matching and Feynman-Kac Steering

Expert chemists perform retrosynthetic analysis in a strategic manner, where each reaction in a route is employed with a specific purpose, combined with mechanistic reasoning represented as electron-pushing arrows to visualize how bonds are formed or broken via electron redistribution. Yet contemporary single-step retrosynthesis models exhibit neither of these properties: their learned reaction distribution is fixed to that of the training data, and reactions are predicted via de-novo generation of the precursors or molecular graph edits, omitting the electron redistribution that governs the chemical transformations. We introduce Verso MAELLE, the first electron-pushing-based single-step retrosynthetic model, representing disconnections as continuous-time Markov chains (CTMC) comprised of edits in the electron space, and is trainable using a discrete flow matching formulation. Being a generative model, Verso MAELLE is optionally steerable at inference time through the Feynman-Kac (FK) framework, using rewards that promote certain types of disconnections. We show that besides in-distribution learning, Verso MAELLE can be steered toward cyclization reactions which are scaffold-constructive but underrepresented in the training corpus, via a ring-breaking reward. As a proof of concept, we show how the electron-pushing reasoning, learned from patented data, combined with a domino incentivizing reward, could steer Verso MAELLE toward creative and strategic disconnections, demonstrated on the key domino sequence that was used to synthesize Punctaporonin U, a complex natural product of the sesquiterpene family.

physics.chem-ph↗

arXiv2026

Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation

Chemical reactions are fundamentally transformations in electron space, yet most machine learning approaches model them either through de novo generation of product molecules or through heuristic graph edits that operate directly on molecular topology. We introduce MAELLE (MechAnistic Edit fLow-matching on eLectron rEarrangements), which instead models reactions as discrete flow matching over electron occupation vectors. Concretely, we formulate the reactant-to-product mapping as a Continuous-time Markov Chain (CTMC) over the graph-structured integer-valued electron occupation space defined on all bonding, non-bonding, and hydrogen sites. To construct the interpolants between the reactants and products, we generalize the discrete flow matching mixture path to an edit-based formulation, where the electron moves are interpolated using Optimal Transport, yielding a mechanism-like set of moves without elementary step annotations. MAELLE achieves competitive performance on the USPTO-480K benchmark compared with leading reaction prediction models. Beyond in-distribution learning, we evaluate robustness across two out-of-distribution settings - structural complexity and reaction type - and find that MAELLE maintains strong performance where existing methods degrade. Finally, because the learned flow operates over the full electron redistribution, MAELLE naturally recovers mechanistic trajectories that align with known chemistry and can predict side products of a reaction.

cs.AI↗

arXiv2025

Synthelite: Chemist-aligned and feasibility-aware synthesis planning with LLMs

Computer-aided synthesis planning (CASP) has long been envisioned as a complementary tool for synthetic chemists. However, existing frameworks often lack mechanisms to allow interaction with human experts, limiting their ability to integrate chemists' insights. In this work, we introduce Synthelite, a synthesis planning framework that uses large language models (LLMs) to directly propose retrosynthetic transformations. Synthelite can generate end-to-end synthesis routes by harnessing the intrinsic chemical knowledge and reasoning capabilities of LLMs, while allowing expert intervention through natural language prompts. Our experiments demonstrate that Synthelite can flexibly adapt its planning trajectory to diverse user-specified constraints, achieving up to 95\% success rates in both strategy-constrained and starting-material-constrained synthesis tasks. Additionally, Synthelite exhibits the ability to account for chemical feasibility during route design. We envision Synthelite to be both a useful tool and a step toward a paradigm where LLMs are the central orchestrators of synthesis planning.

cs.AI↗

arXiv2025

TempRe: Template generation for single and direct multi-step retrosynthesis

Retrosynthesis planning remains a central challenge in molecular discovery due to the vast and complex chemical reaction space. While traditional template-based methods offer tractability, they suffer from poor scalability and limited generalization, and template-free generative approaches risk generating invalid reactions. In this work, we propose TempRe, a generative framework that reformulates template-based approaches as sequence generation, enabling scalable, flexible, and chemically plausible retrosynthesis. We evaluated TempRe across single-step and multi-step retrosynthesis tasks, demonstrating its superiority over both template classification and SMILES-based generation methods. On the PaRoutes multi-step benchmark, TempRe achieves strong top-k route accuracy. Furthermore, we extend TempRe to direct multi-step synthesis route generation, providing a lightweight and efficient alternative to conventional single-step and search-based approaches. These results highlight the potential of template generative modeling as a powerful paradigm in computer-aided synthesis planning.

cs.LG↗