3D Molecular Generation

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
May 1, 2026Fang Wu, Weihao Xuan, Heli Qi +26Generative Modeling 3D Molecular Generation
Stanford University
Quotient-Space Diffusion Models
Apr 23, 2026Yixian Xu, Yusong Wang, Shengjie Luo +4Generative Modeling 3D Molecular Generation
State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China · State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · Zhongguancun Academy, Beijing, China +2
Structure-guided molecular design with contrastive 3D protein-ligand learning
Apr 21, 2026Carles Navarro, Philipp Tholke, Gianni de FabritiisContrastive Learning Virtual Screening
Acellera Labs, C/ Doctor Trueta 183, 08005 Barcelona, Spain · Computational Science Laboratory, Universitat Pompeu Fabra, PRBB, C/ Doctor Aiguader 88, 08003 Barcelona, Spain · Instituci´o Catalana de Recerca i Estudis Avan¸cats (ICREA), Passeig Llu´ıs Companys 23, 08010 Barcelona, Spain
Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation
Oct 6, 2025Johanna Sommer, John Rachwan, Nils Fleischmann +2Flow Matching Efficient Inference
Pruna AI · Technical University of Munich, Germany
MolMiner: Toward Controllable, 3D-Aware, Fragment-Based Molecular Design
Nov 10, 2024Raul Ortega-Ochoa, Tejs Vegge, Jes Frellsen3D Molecular Generation Autoregressive Generation
Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark · CAPeX Pioneer Center for Accelerating P2X Materials Discovery · Department of Applied Mathematics and Computer Science, Technical University of Denmark

May 1, 2026, cs.LGJ/K move · Enter open · S save

Proteo-R1: Reasoning Foundation Models for De Novo Protein Design

Fang Wu, Weihao Xuan, Heli Qi, Hanqun Cao+25

Stanford University

Generative Modeling 3D Molecular Generation Molecular Generation Protein Design Large Language Model-Guided Scientific Discovery Conditional Generative Modeling

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Deep learning in \emph{de novo} protein design has achieved atomic-level fidelity. However, existing models remain largely non-deliberative: they directly synthesize molecular geometries without explicitly reasoning about which residues or interactions are functionally essential. As a result, design decisions are entangled with continuous sampling dynamics, limiting interpretability, controllability, and systematic reuse of biochemical knowledge. We introduce \textbf{Proteo-R1}, a reasoning-guided protein design framework that explicitly decouples \emph{molecular understanding} from \emph{geometric generation}. Proteo-R1 adopts a dual-expert architecture in which a multimodal large language model (MLLM) serves as an \emph{understanding expert}, analyzing protein sequences, structures, and textual context to identify key functional residues that govern binding and specificity. These residue-level decisions are then passed as hard constraints to a separate diffusion-based \emph{generation expert}, which performs conditional co-design while respecting the fixed interaction anchors. This factorization mirrors how human experts approach molecular engineering: first, reasoning about critical interactions, then optimizing geometry subject to those constraints. By operationalizing reasoning as explicit residue-level commitments rather than latent textual guidance, Proteo-R1 achieves stable, interpretable, and modular integration of LLM reasoning with state-of-the-art geometric generative models. Code, data, and demos are available at https://smiles724.github.io/r1/.

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Latest papers 35

Proteo-R1: Reasoning Foundation Models for De Novo Protein Design

Quotient-Space Diffusion Models

Structure-guided molecular design with contrastive 3D protein-ligand learning

Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation

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