← Back to all news
arXiv Machine Learning By Xinyu Wang, Jinbo Bi, Minghu Song

Budgeted Quotient-Residual Guidance for Frozen Pocket-Conditioned Molecular Diffusion

Read the original on arXiv Machine Learning →

Budgeted Quotient-Residual Guidance (QRG) is an inference-time correction that enables quotient-based lead‑optimization objectives—such as distances, contacts, and anchored substructures—to be actively used with frozen pocket‑conditioned molecular diffusion models without retraining. QRG lifts quotient covectors to metric‑horizontal ambient directions and delivers them through a trust budget set by the sampler’s step norm, providing a closed‑form sampler‑budget update and a product‑budget split for section and residual controls. Experiments on frozen TargetDiff backbones show that QRG improves ligand‑generation quality on fragment growing, scaffold hopping, and linker design while preserving novelty and diversity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

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
diffusionfine-tuning
More like this →
arXiv Machine Learning
Sep 18

VGAS: Variance-Reduced Guidance and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion

VGAS: Variance-Reduced Guidance and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion proposes a new inference-time framework that improves steering of frozen masked discrete diffusion models. By reducing the variance of guidance estimates, applying reward tilting to clean-token logits, and adapting the selection temperature at each step, VGAS addresses three default choices in existing pipelines. Experiments on regulatory DNA, protein, and small-molecule benchmarks show that VGAS achieves the best training-free reward performance and matches or surpasses reward-fine-tuned generators.

By Kwanyoung Kim
diffusionfine-tuningbenchmarkssafety
More like this →
arXiv AI
Jun 2

Probe Before You Edit: Probing-Guided Molecular Optimization for LLM Agents in Structure-Based Drug Design

arXiv:2606. 00555v1 Announce Type: new Abstract: Structure-based drug design increasingly employs LLM agents to iteratively refine ligands against a target pocket, yet a viable ligand must satisfy two often-conflicting objectives -- binding affinity and druggability -- which single optimization steps rarely improve together.

By Zaifei Yang, Weiyu Chen, Yaqing Wang, James Kwok
llmsagentsbenchmarks
More like this →