arXiv Machine Learning

Sesame: Structure-Aware Molecular Generation via Spatial Density-Map Conditioning

arXiv:2606. 23856v1 Announce Type: new Abstract: Generative molecular models for drug design are a promising direction with much active research.

arXiv Machine Learning

2d ago

READ: A Retrieval-Alignment Diffusion Framework for Structure-based Drug Design

arXiv:2506. 14488v2 Announce Type: replace-cross Abstract: Structure-based drug design (SBDD) models are central to modern pharmaceutical research, enabling the rational exploration of protein-ligand interactions at atomic resolution.

By Dong Xu, Zhangfan Yang, Junchuang Cai, Sisi Yuan, Zexuan Zhu, Jianqiang Li, Junkai Ji

diffusion benchmarks safety

arXiv AI

Jul 21

Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints

arXiv:2607. 18144v1 Announce Type: cross Abstract: Structure-based drug design (SBDD) leverages the 3D structure of protein targets, often complemented by other spatial constraints, to generate candidate binding molecules.

By Thomas MacDougall, Maksim Kuznetsov, Roman Schutski, Rim Shayakhmetov, Maxim Malkov, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov

llms diffusion 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

Hugging Face Trending Papers

Jul 14

Generating Developable 3D Molecules via Pocket-Conditioned Diffusion and Property-Aware Optimization

Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug design. Many such models follow the structure-based drug design (SBDD) paradigm, generating molecules to fit a target binding pocket.

diffusion benchmarks

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

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

Jul 15

Generating Developable 3D Molecules via Pocket-Conditioned Diffusion and Property-Aware Optimization

arXiv:2607. 12349v1 Announce Type: new Abstract: Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug design.

By Ruoxi Gao, Jiangweizhi Peng, Ziqi Chen, Frazier N. Baker, David C. Kombo, John L. Kane Jr., Andrew A. Scholte, Yi Li, Matthew J. LaMarche, Luigi I. Iconaru, Hans-Peter Biemann, Mingyi Hong, Xia Ning

diffusion benchmarks

arXiv AI

Jun 2

Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling

arXiv:2606. 01628v1 Announce Type: cross Abstract: Biomolecules such as proteins and small-molecule ligands play a central role in biological systems, arising from the tight interplay between sequence and three-dimensional structure.

By Keyue Qiu, Xintong Wang, Zhilong Zhang, Hao Zhou, Wei-Ying Ma

multimodal

arXiv Machine Learning

Jun 2

Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models

arXiv:2606. 01461v1 Announce Type: new Abstract: Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes.

By Brenda Nogueira, Gisela A. Gonzalez-Montiel, Nitesh V. Chawla, Nuno Moniz

llms agents diffusion

arXiv Machine Learning

Jul 15

Sample Efficient Generative Optimization for Molecular Design

arXiv:2607. 12488v1 Announce Type: new Abstract: Molecular optimization in drug discovery, materials design, and catalysis requires searching vast chemical spaces under tight evaluation budgets, since high-fidelity oracles and experimental measurements are costly.

By Sarina Kopf, Cristina Nevado, Philippe Schwaller

benchmarks

arXiv Machine Learning

Jul 16

HEDGEHOG: Hierarchical Evaluation of Drug Generators Through Rigorous Filtration

arXiv:2607. 13155v1 Announce Type: new Abstract: Generative molecular models can support early drug discovery by proposing new candidate compounds de novo.

By Daria A. Ryabchenko (Ligand Pro, Moscow, Russia, Skolkovo Institute of Science and Technology, Artificial Intelligence Center, Moscow, Russia), Pavel Gurevich (Ligand Pro, Moscow, Russia, Skolkovo Institute of Science and Technology, Artificial Intelligence Center, Moscow, Russia), Shamil Kadyrov (Ligand Pro, Moscow, Russia), Daria Frolova (Ligand Pro, Moscow, Russia, Skolkovo Institute of Science and Technology, Artificial Intelligence Center, Moscow, Russia), Kseniia Fedisheva (Ligand Pro, Moscow, Russia), Sergei A. Nikolenko (Ligand Pro, Moscow, Russia), Alexander Shapeev (Ligand Pro, Moscow, Russia, Skolkovo Institute of Science and Technology, Artificial Intelligence Center, Moscow, Russia), Marina A. Pak (Ligand Pro, Moscow, Russia)

benchmarks

arXiv Machine Learning

Jul 27

TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex

arXiv:2607. 22143v1 Announce Type: new Abstract: Molecular glue degraders have emerged as a promising strategy for targeted protein degradation by inducing ternary complex formation between an E3 ubiquitin ligase and a target protein.

By Yuliang Yan, Shuo Yan, Haochun Tang, Yiqin Sun, Enyan Dai

diffusion

Related stories