arXiv Machine Learning

Out-of-Distribution Detection in Molecular Complexes via Diffusion Models for Irregular Graphs

arXiv:2512. 18454v3 Announce Type: replace Abstract: Predictive machine learning models generally excel on in-distribution data, but their performance degrades on out-of-distribution (OOD) inputs.

arXiv Machine Learning

Jun 16

Learning Topological Representations for Molecular Dynamics

arXiv:2606. 14737v1 Announce Type: cross Abstract: Molecular dynamics (MD) simulations generate trajectories in a high-dimensional configuration space whose analysis critically depends on molecular descriptors, typically handcrafted observables or learned kinetic embeddings.

By Dominik Geng, Florian Graf, Martin Uray, Roland Kwitt

rag

arXiv Machine Learning

Sep 23

Transport-Coupled Bayesian Flows for Molecular Graph Generation

Transport-Coupled Bayesian Flows for Molecular Graph Generation (TopBF) addresses a key mismatch in existing diffusion models for molecular graph generation by eliminating the need for hard discretization during sampling. The framework generates graphs directly in continuous parameter distributions, learns graph topology via a Quasi-Wasserstein optimal‑transport coupling with geodesic costs, and enables property‑conditioned generation without retraining. Experiments on QM9 and ZINC250k show that TopBF achieves higher structural fidelity and more efficient generation compared to prior methods.

By Yida Xiong, Jiameng Chen, Kun Li, Hongzhi Zhang, Xiantao Cai, Lei Lei, Wenbin Hu

rag diffusion

arXiv AI

Jul 24

Beyond SBDD: Geometric Deep Learning in Polypharmacology and Multi-target Drug Design

arXiv:2607. 20550v1 Announce Type: cross Abstract: The traditional "one drug, one target" paradigm of structure-based drug design (SBDD) frequently proves inadequate for treating multifactorial diseases such as cancer and neurodegenerative disorders, owing to compensatory signaling pathways and the emergence of drug resistance.

By Tianming Han, Zhijie Pan, Wenchi Ge, Qi Zhao

rag diffusion reinforcement-learning multimodal benchmarks

arXiv AI

Jun 2

Structure-Guided Adaptive Propagation for Protein-Protein Interaction Site Prediction

arXiv:2606. 01781v1 Announce Type: new Abstract: Accurate prediction of protein-protein interaction sites (PPIS) is essential for understanding cellular processes, disease mechanisms, and therapeutic target discovery.

By Enqiang Zhu, Yizi Liu, Yilong Luo, Yao Chen, Yu Zhang, Baoshan Ma

diffusion benchmarks

arXiv Machine Learning

Aug 11

RAVEN: Frozen Random Graph Reservoirs with Physics-Informed Interaction Fingerprints for Protein-Ligand Binding Affinity Prediction

arXiv:2608. 09099v1 Announce Type: new Abstract: Quantitative estimation of protein-ligand binding affinity from three-dimensional complex structures is a fundamental task in structure-based computational chemistry and molecular modeling.

By Qingyang Zou, Jiaye Huang, Hangbo Xie, Jiayue Yin, Youyi Song, Jinfeng Liu

arXiv Machine Learning

Jul 8

Multimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES Embeddings

arXiv:2607. 05736v1 Announce Type: new Abstract: Molecular property prediction often relies on isolated data modalities, where continuous 3D graph neural networks (GNNs) struggle to efficiently capture long-range topological dependencies and exact macroscopic heuristics.

By Qiwei Han, Chi Zhou, Ruobing Wang, Zheng Ma

rag diffusion efficiency multimodal benchmarks safety

arXiv Machine Learning

Jun 15

Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction

arXiv:2606. 14217v1 Announce Type: new Abstract: Accurate prediction of protein-ligand binding affinity is essential for structure-based drug discovery.

By Peng-Fei Sun, Chuan-Xian Ren, Hong Yan

benchmarks safety

arXiv Machine Learning

Jun 9

Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules

arXiv:2606. 08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.

By Riccardo De Santi, Bruce Lee, Cristian Perez Jensen, Kimon Protopapas, Sophia Tang, Cheng-Hao Liu, Pranam Chatterjee, Yisong Yue, Andreas Krause

diffusion

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 AI

Jun 12

HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens

arXiv:2512. 15133v3 Announce Type: replace-cross Abstract: Proteins inherently possess a consistent sequence-structure duality.

By Yi Zhou, Haohao Qu, Yunqing Liu, Shanru Lin, Le Song, Wenqi Fan

llms diffusion fine-tuning efficiency multimodal benchmarks

arXiv Machine Learning

Jul 1

ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table

arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.

By Jacob W. Toney, Samir Darouich, Yiran Wang, Aaron G. Garrison, Johannes K\"astner, Heather J. Kulik

llms benchmarks

arXiv Machine Learning

Aug 6

Geometry-Informed Parameter-Efficient Fine-Tuning of Pre-trained Molecular GNNs for Blood-Brain Barrier Permeability Prediction

arXiv:2608. 04257v1 Announce Type: new Abstract: Blood-brain barrier permeability (BBBP) prediction is a critical screening task in central nervous system drug discovery, where candidate molecules must be assessed for whether they can cross, or should be prevented from crossing, the blood-brain barrier.

By Marco Vieto Vega, Long D. Nguyen, Binh P. Nguyen

fine-tuning

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