arXiv Machine Learning

MolMiner: Toward Controllable, 3D-Aware, Fragment-Based Molecular Design

arXiv:2411. 06608v3 Announce Type: replace Abstract: We introduce MolMiner, a fragment-based, geometry-aware, and order-agnostic autoregressive model for molecular design.

arXiv Machine Learning

Jun 8

Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport

arXiv:2606. 07239v1 Announce Type: new Abstract: The success of generative molecular design hinges on a model's steerability toward high-reward samples.

By Malte Franke, Stefan P. Schmid, Zarko Ivkovic, Kjell Jorner, Andreas Krause

diffusion benchmarks

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 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 24

SenCos-GEM: SENet-Calibrated and Law-of-Cosines-Constrained Geometry-Enhanced Molecular Representation for Property Prediction

arXiv:2607. 20551v1 Announce Type: cross Abstract: Effective molecular representation learning is crucial for accurate molecular property prediction.

By Tianming Han, Li Zhang, Qi Zhao

benchmarks

arXiv Machine Learning

Jul 13

Autoregressive latent diffusion for 3D molecule generation

arXiv:2607. 09277v1 Announce Type: new Abstract: Three-dimensional (3D) molecule generation has been dominated by diffusion models, which achieve strong generation quality but typically require the molecular size to be specified a priori.

By Federico Ottomano, Gaopeng Ren, Yingzhen Li, Kim E. Jelfs, Alex M. Ganose

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

Jun 8

Controllable Molecular Generative Foundation Models

arXiv:2605. 15354v2 Announce Type: replace Abstract: Despite the success of foundation models in language and vision, molecular graph generation still lacks a unified framework for heterogeneous design tasks with reliable controllability.

By Yihan Zhu, Yuhan Liu, Weijiang Li, Tengfei Luo, Meng Jiang

rag diffusion reinforcement-learning benchmarks

arXiv Machine Learning

Jul 7

FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms

arXiv:2508. 02641v2 Announce Type: replace-cross Abstract: Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics.

By Vahe Gharakhanyan, Yi Yang, Luis Barroso-Luque, Daniel S. Levine, Sushree Jagriti Sahoo, Brandon M. Wood, Kyle Michel, Muhammed Shuaibi, Gregory J. O. Beran, Viachaslau Bernat, Misko Dzamba, Xiang Fu, Meng Gao, Xingyu Liu, Benjamin K. Miller, Keian Noori, Lafe J. Purvis, Tingling Rao, Ammar Rizvi, Matt Uyttendaele, Andrew J. Ouderkirk, Chiara Daraio, C. Lawrence Zitnick, Arman Boromand, Noa Marom, Zachary W. Ulissi, Anuroop Sriram

fine-tuning efficiency

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

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 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 23

Boltzmann-Expected Molecular Design with Decoupled Annealing Flows

arXiv:2607. 19519v1 Announce Type: cross Abstract: Most 3D properties relevant to molecular design, including free energies and shape descriptors, are $\textit{expectations}$ over the Boltzmann distribution over 3D configurations of a molecular graph.

By Selma Moqvist, Richard Beckmann, Ross Irwin, Roc\'io Mercado, Simon Olsson

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