arXiv AI

URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment

arXiv:2607. 04688v1 Announce Type: cross Abstract: Synthesis planning aiming to find pathways of reactions for a target molecule is one of the most important and challenging tasks in drug discovery.

arXiv AI

Jun 2

When Single Answer Is Not Enough: Rethinking Single-Step Retrosynthesis Benchmarks for LLMs

arXiv:2602. 03554v2 Announce Type: replace-cross Abstract: Recent progress has expanded the use of large language models (LLMs) in drug discovery, including synthesis planning.

By Bogdan Zagribelnyy, Ivan Ilin, Maksim Kuznetsov, Nikita Bondarev, Mathieu Reymond, Roman Schutski, Thomas MacDougall, Rim Shayakhmetov, Zulfat Miftakhutdinov, Mikolaj Mizera, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov

llms benchmarks

arXiv AI

Jun 9

RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction

arXiv:2603. 12666v2 Announce Type: replace-cross Abstract: Retrosynthesis prediction aims to identify reactants that can synthesize a given product molecule.

By Hanbum Ko, Chanhui Lee, Ye Rin Kim, Rodrigo Hormazabal, Sehui Han, Sungbin Lim, Sungwoong Kim

llms reinforcement-learning fine-tuning

arXiv AI

1d ago

RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction

arXiv:2608. 16111v1 Announce Type: cross Abstract: Retrosynthesis is a cornerstone of drug discovery and organic synthesis.

By Mianzhi Liu, Fan Xiao, Zhiliang Yu, Huayang Huang, Yuke Li, Yi Yang, Wenbo Liu, Yu Wu

rag

arXiv AI

Jul 17

RetroAgent: Harnessing LLMs to Search Over Structured Memory for Agentic Retrosynthesis Planning

arXiv:2607. 14512v1 Announce Type: new Abstract: Multi-step retrosynthesis planning seeks to decompose a target molecule into commercially available building blocks through a sequence of feasible reactions.

By Yanqiao Zhu, Jingru Gan, Xiaoqi Sun, Fang Sun, Yidan Shi, Md Mofijul Islam, Chao Shang, Wenhao Gao, Connor W. Coley, Yizhou Sun, Wei Wang

llms agents benchmarks

arXiv AI

Aug 10

Strategy-first synthesis planning for complex natural products

arXiv:2608. 07454v1 Announce Type: cross Abstract: The total synthesis of a complex molecule is among the most demanding intellectual and experimental feats in chemistry: a chemist must plan many steps ahead for how to assemble simple building blocks into an intricate target, devise backup strategies, and anticipate procedural challenges.

By Daniel Armstrong, Xuan-Vu Nguyen, Octavian Susanu, Gabriel Gibberd, Th\'eo A. Neukomm, Tadd\"aus Strunden, Dan Forster, Morgane Delattre, Shawn Teh, Cl\'ement Rols, John Federice, Hayden Leatherwood, M. Lavelle Barnes, Maarten R. Dobbelaere, Peter Wipf, Jon T. Njardarson, Jieping Zhu, Philippe Schwaller

llms agents benchmarks

arXiv Machine Learning

Jun 11

My Chemical Harness: Evolutionary Molecular Design over Synthetic Pathways with Large Language Model Agents

arXiv:2606. 11256v1 Announce Type: cross Abstract: Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes.

By C\'esar Ojeda, Darius A. Faroughy, Maryam Karimi, Payam Zarrintaj, Mir Mehdi Seyedebrahimi, Mart\'in Carballo-Pacheco

llms agents fine-tuning benchmarks

arXiv AI

Jun 16

Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis

arXiv:2508. 10967v3 Announce Type: replace-cross Abstract: Retrosynthesis prediction aims to infer the reactant molecules based on a given product molecule, which is a fundamental task in chemical synthesis.

By Xinyi Li, Sai Wang, Yutian Lin, Yu Wu

llms reinforcement-learning

arXiv Machine Learning

Aug 12

Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching

arXiv:2602. 13136v2 Announce Type: replace Abstract: Template-free retrosynthesis methods treat the task as black-box sequence generation, limiting learning efficiency, while semi-template approaches rely on rigid reaction libraries that constrain generalization.

By Chenguang Wang, Zihan Zhou, Lei Bai, Tianshu Yu

llms rag diffusion benchmarks safety

arXiv Machine Learning

Jul 15

Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models

arXiv:2607. 12771v1 Announce Type: new Abstract: Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations.

By Xingyu Dang, Haocheng Tang, Junmei Wang, Yanjun Li

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jul 2

SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles

arXiv:2607. 01105v1 Announce Type: new Abstract: We present SynLaD, a latent diffusion framework for small-molecule generation that unifies ligand-based drug design objectives (what to make) with synthetic accessibility (how to make it).

By Miruna Cretu, John Bradshaw, Patricia Suriana, Saeed Saremi, Omar Mahmood, Kirill Shmilovich, Kangway Chuang, Vishnu Sresht, Colin Grambow

llms diffusion

arXiv Machine Learning

Jul 22

Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation

arXiv:2607. 19044v1 Announce Type: new Abstract: Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design.

By Mingxuan Ouyang, Hao Lan, Wanyu Lin

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 10

Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors

arXiv:2602. 04119v2 Announce Type: replace Abstract: The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in practice.

By Hyeonah Kim, Minsu Kim, Celine Roget, Dionessa Biton, Louis Vaillancourt, Yves V. Brun, Yoshua Bengio, Alex Hernandez-Garcia

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