arXiv AI

Navigating Route Latent Space for Synthesizable Molecular Design

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 Machine Learning

Sep 2

Elite-Weighted Supervised Fine-tuning for Goal-Directed Molecular Optimization

arXiv:2609.00189v1 Announce Type: new Abstract: Goal-directed optimization is essential for steering molecular generators to propose candidates with desired properties. However, it is often implement...

By Shiyun Wa, Yifei Wang, Anna G. Green, Simone Sciabola, Ye Wang

diffusion reinforcement-learning fine-tuning benchmarks safety

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

arXiv Machine Learning

Aug 4

Refine Drugs, Don't Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discovery

arXiv:2509. 26405v2 Announce Type: replace Abstract: We introduce InVirtuoGen, a discrete flow generative model for fragmented SMILES for de novo and fragment-constrained generation, and target-property/lead optimization of small molecules.

By Benno Kaech, Luis Wyss, Karsten Borgwardt, Gianvito Grasso

fine-tuning 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

Sep 1

Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation

arXiv:2608.31009v1 Announce Type: new Abstract: Structure-based drug design (SBDD) requires ligands that satisfy both 3D target affinity and 1D chemical validity. Existing controllable generation met...

By Tianyu Gao, Zhikai Su, Jiashu Li, Wenjun Gao, Zichuan Ying, Zhe Zhao, Fei Zhang, Ye Wei

agents diffusion fine-tuning multimodal

arXiv AI

Sep 16

LLM-Guided Dynamic Action Spaces for Synthesizable Molecular Optimization

arXiv:2604.07669v3 Announce Type: replace-cross Abstract: Synthesizable molecular optimization seeks to improve target properties while ensuring that molecular modifications follow feasible synthetic...

By Tao Li, Kaiyuan Hou, Tuan Vinh, Fanglei Xue, Monika Raj, Zhichun Guo, Carl Yang

llms

arXiv AI

Jun 2

Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling

arXiv:2606. 01220v1 Announce Type: cross Abstract: Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in structure-based drug design (SBDD).

By Guang Lin, Shikui Tu, Lei Xu

diffusion reinforcement-learning fine-tuning benchmarks data-engineering

arXiv Machine Learning

Oct 7

Evolutionary One-Step Generators: Fast and Diverse Sampling for Discrete Design

arXiv:2610.08367v1 Announce Type: new Abstract: Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates at low computational cost. High validity a...

By Marcus Vukojevic, Erik Nielsen, Veronica Lachi, Andrea Passerini, Giovanni Iacca

benchmarks

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

Jul 7

On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization

arXiv:2607. 02834v1 Announce Type: new Abstract: Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures.

By Trevor Chen, Ariel Dai, Jason Yang, Riccardo De Santi, Daniel Khalil, Wenda Chu, Nate Gruver, Pranav Murugan, Alexander F. G. Goldberg, Maruan Al-Shedivat, Yisong Yue

diffusion fine-tuning

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