arXiv AI

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.

arXiv AI

Jul 7

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.

By Bogdan Zagribelnyy, Ivan Ilin, Nikita Bondarev, Anton Morgunov, Arkadii Lin, Maksim Kuznetsov, Rim Shayakhmetov, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov

llms 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 Machine Learning

Jun 16

Generative Molecular Design with Steerable and Granular Synthesizability Control

arXiv:2505. 08774v2 Announce Type: replace-cross Abstract: Designing molecules that are both property-optimal and readily synthesizable is a central challenge in drug discovery.

By Jeff Guo, V\'ictor Sabanza-Gil, Olha Semenenko, Oleksii Hrabovskyi, Mykola Protopopov, Anna Kapeliukha, Oleksandr Mosia, Sofiia Hatych, Diana Alieksieieva, Tom Nelis, Patrick Molliet, Helena Sol\'e-\`Avila, Valentas Olikauskas, Nina Aregger, Irina Morozova, Joseph Schmidt, Zlatko Jon\v{c}ev, Olga Tarkhanova, Petro Borysko, Jerome Waser, Bruno Correia, Jeremy Luterbacher, Philippe Schwaller

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

Aug 10

CAi Copilot: Reducing Operational Workload in Molecular Design through Intent-Driven Agentic Workflows

arXiv:2608. 06961v1 Announce Type: new Abstract: Early-stage molecular design is an iterative process, not just a task of generating molecules.

By Zhu Wang, Jiangyu Chen, Yingjun Shang, Yuhui Yao, Laiao Lu, Tianfan Fu, Na Zou

agents 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

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

Jul 2

Agentic generation of verifiable rules for deterministic, self-expanding reaction classification

arXiv:2607. 01061v1 Announce Type: new Abstract: Computer-assisted synthesis planning breaks target molecules into accessible precursors using large libraries of reaction rules that assign each transformation a deterministic, interpretable label.

By Daniel Armstrong, Maarten Dobbelaere, Valentas Olikauskas, Helena Avila, Octavian Susanu, J\'er\^ome Waser, Philippe Schwaller

llms agents

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 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 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 AI

Jun 8

Autonomous computational catalysis through an agentic research system

arXiv:2601. 13508v4 Announce Type: replace-cross Abstract: Autonomous agents are beginning to transform scientific research from tool-assisted workflows toward self-sustaining discovery processes.

By Honghao Chen, Jiangjie Qiu, Yi Shen Tew, Xiaonan Wang

agents

Related stories