arXiv AI

JEDEL: Zero-Shot DNA-Encoded Library Design for Early-Stage Drug Discovery

arXiv:2606. 23745v1 Announce Type: cross Abstract: We present JEDEL, a framework for generating synthesis-ready DNA-encoded libraries (DELs) directly from three-dimensional pharmacophore representations of active ligands.

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 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

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

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 22

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models

arXiv:2607. 19237v1 Announce Type: new Abstract: Designing small molecule ligands that bind with high affinity to specific protein pockets is a fundamental goal in drug discovery, as small molecules constitute a major fraction of approved therapeutics.

By Yiming Qin, Kai Yi, Miruna Cretu, Sjors H. W. Scheres, Pietro Li\`o, Pascal Frossard

safety

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 AI

Jul 15

Gene Expression-Informed Jointly Controlled Generative Modeling for Precision Molecular Design

arXiv:2607. 11978v1 Announce Type: cross Abstract: Precision molecular design aims to discover personalized drug candidates through joint control of multiple conditions, such as biological relevance and molecular design strategies.

By Hang Yuan, Chen Li, Wenjun Ma, Tadahiko Murata, Yuncheng Jiang

benchmarks

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

Jun 11

ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation

arXiv:2606. 11243v1 Announce Type: new Abstract: De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology.

By Chuanzhen Wang, Meade Cleti, Pete Jano

diffusion

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

Jun 24

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.

By Konstantin Yatsenko, Arvind Thiagarajan

diffusion fine-tuning

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

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