Publications

Controllable Generation of Drug-like Molecules with Multi-modal Variational Flow
Fang Sun
Zhihao Zhan
Hongyu Guo
Ming Zhang
Jian Tang
Yizhou Sun
Designing drug molecules that bind effectively to target proteins while maintaining desired pharmacological properties remains a fundamental… (see more) challenge in drug discovery. Current approaches struggle to simultaneously control molecular topology and 3D geometry, often requiring expensive retraining for new design objectives. We propose a multi-modal variational flow framework that addresses these limitations by integrating a 2D topology encoder with a 3D geometry generator. Our architecture encodes molecular graphs into a learned latent distribution via junction tree representations, then employs normalizing flows to autoregressively generate atoms in 3D space conditioned on the protein binding site. This design enables zero-shot controllability: by manipulating the latent prior distribution, we can generate molecules with specific substructures or optimized properties without model retraining. Experiments on the CrossDocked benchmark show that our model achieves 31.1% high-affinity rate, substantially outperforming existing methods, while maintaining superior drug-likeness and structural diversity. Our framework opens new possibilities for on-demand molecular design, allowing medicinal chemists to rapidly explore chemical space with precise control over both structural motifs and physicochemical properties.
2025-10-21
logconference.io/LOG/2025/Conference (poster)
openreview.net
openreview.net
A Derandomization Framework for Structure Discovery: Applications in Neural Networks and Beyond
Nikos Tsikouras
Yorgos Pantis
Ioannis Mitliagkas
Christos Tzamos
2025-10-21
ArXiv (preprint)
doi.org
arxiv.org
Improving the Physics of Video Generation with VJEPA-2 Reward Signal
Jianhao Yuan
Xiaofeng Zhang
Felix Friedrich
Nicolas Beltran-Velez
Melissa Hall
Reyhane Askari Hemmat
Xiaochuang Han
Nicolas Ballas
Michal Drozdzal
Adriana Romero
2025-10-21
ArXiv (preprint)
doi.org
arxiv.org
International AI Safety Report: First Key Update, Capabilities and Risk Implications
Prof. Yoshua Bengio
Stephen Clare
Carina Prunkl
Maksym Andriushchenko
BEN BUCKNALL
Philip Fox
Tiancheng Hu
Cameron Jones
Sam Manning
Nestor Maslej
Vasilios Mavroudis
Conor McGlynn
Malcolm Murray
Shalaleh Rismani
Charlotte Stix
Lucia Velasco
Nicole Wheeler
Daniel Privitera
Sören Mindermann
Daron Acemoglu … (see 36 more)
Thomas G. Dietterich
Fredrik Heintz
Geoffrey Hinton
Nick Jennings
Susan Leavy
Teresa Ludermir
Vidushi Marda
Helen Margetts
John McDermid
Jane Munga
Arvind Narayanan
Alondra Nelson
Clara Neppel
Sarvapali D. (Gopal) Ramchurn
Stuart Russell
Marietje Schaake
Bernhard Schölkopf
Alvaro Soto
Lee Tiedrich
Gael Varoquaux
Andrew Yao
Ya-Qin Zhang
Lambrini Das
Claire Dennis
Arianna Dini
Freya Hempleman
Samuel Kenny
Patrick King
Hannah Merchant
Jamie-Day Rawal
Rose Woolhouse
Jonathan Barry
Marc-Antoine Guérard
Claire Latendresse
Cassidy MacNeil
Benjamin Prud'homme
The field of AI is moving too quickly for a single yearly publication to keep pace. Significant changes can occur on a timescale of months, … (see more)sometimes weeks. This is why we are releasing Key Updates: shorter, focused reports that highlight the most important developments between full editions of the International AI Safety Report. With these updates, we aim to provide policymakers, researchers, and the public with up-to-date information to support wise decisions about AI governance. This first Key Update focuses on areas where especially significant changes have occurred since January 2025: advances in general-purpose AI systems' capabilities, and the implications for several critical risks. New training techniques have enabled AI systems to reason step-by-step and operate autonomously for longer periods, allowing them to tackle more kinds of work. However, these same advances create new challenges across biological risks, cyber security, and oversight of AI systems themselves. The International AI Safety Report is intended to help readers assess, anticipate, and manage risks from general-purpose AI systems. These Key Updates ensure that critical developments receive timely attention as the field rapidly evolves.
2025-10-21
SuperIntelligence - Robotics - Safety & Alignment (published)
doi.org
The Intricate Dance of Prompt Complexity, Quality, Diversity, and Consistency in T2I Models
Xiaofeng Zhang
Aaron Courville
Michal Drozdzal
Adriana Romero
2025-10-21
ArXiv (preprint)
doi.org
arxiv.org
A Hardware‐in‐Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration
Jian Tang
Wen Yu
JunFei Qiao
2025-10-20
(published)
doi.org
Neural FIM: Bridging Statistical Manifolds and Generative Modeling through Fisher Geometry
Yanlei Zhang
Guillaume Huguet
Edward De Brouwer
Danqi Liao
Oluwadamilola Fasina
Alexander Tong
Ricky T. Q. Chen
Guy Wolf
Maximilian Nickel
Ian Adelstein
Smita Krishnaswamy
While data diffusion-based embeddings are widely used in unsupervised learning to reveal the intrinsic geometry of data, they are fundamenta… (see more)lly constrained by their discrete nature and inability to generalize beyond training points. This limitation ob
2025-10-20
TechRxiv (accepted)
doi.org
Rapid De Novo Antibody Design with GeoFlow-V3
BioGeometry Team
Jian Tang
Recent years have witnessed striking advances in miniprotein design, yet de novo antibody discovery remains challenging, marked by low bindi… (see more)ng rates and the need for extensive, labor-intensive experimental screening of millions of candidates. This technical report introduces GeoFlow-V3, a unified atomic generative model for structure prediction and protein design. GeoFlow-V3 delivers improved accuracy on antibody-antigen complex structure prediction relative to our previous version, and its performance is further enhanced when experimental constraints or prior knowledge are provided, enabling precise control over both folding and design. The model also demonstrates reliable ability to discriminate binders from non-binders based on its confidence scores. Leveraging this capability, we build a GeoFlow-V3 in silico pipeline to design no more than 50 nanobodies per therapeutically relevant target de novo, completing a single round of wet-lab characterization in under three weeks. GeoFlow-V3 identifies at least one binder for 8 tested epitopes and achieves an average hit rate of 15.5%, representing a two-orders-of-magnitude improvement over prior computational pipelines. These results position GeoFlow-V3 as an appealing platform for rapid, AI-driven therapeutic antibody discovery, significantly reducing experimental screening demands and offering a powerful avenue to tackle previously undruggable targets. A demo of GeoFlow-V3 can be accessed via prot.design for non-commercial use.
2025-10-20
bioRxiv (preprint)
doi.org
Representing Positional Information in Generative World Models for Object Manipulation
Stefano Ferraro
Pietro Mazzaglia
Tim Verbelen
Bart Dhoedt
Sai Rajeswar
Object manipulation capabilities are essential skills that set apart embodied agents engaging with the world, especially in the realm of rob… (see more)otics. The ability to predict outcomes of interactions with objects is paramount in this setting. While model-based control methods have started to be employed for tackling manipulation tasks, they have faced challenges in accurately manipulating objects. As we analyze the causes of this limitation, we identify the cause of underperformance in the way current world models represent crucial positional information, especially about the target's goal specification for object positioning tasks. We introduce a general approach that empowers world model-based agents to effectively solve object-positioning tasks. We propose two declinations of this approach for generative world models: position-conditioned (PCP) and latent-conditioned (LCP) policy learning. In particular, LCP employs object-centric latent representations that explicitly capture object positional information for goal specification. This naturally leads to the emergence of multimodal capabilities, enabling the specification of goals through spatial coordinates or a visual goal. Our methods are rigorously evaluated across several manipulation environments, showing favorable performance compared to current model-based control approaches.
2025-10-20
Frontiers in Artificial Intelligence and Applications (published)
doi.org
arxiv.org
Improved Localized Machine Unlearning Through the Lens of Memorization
Reihaneh Torkzadehmahani
Reza Nasirigerdeh
Georgios Kaissis
Daniel Rueckert
Gintare Karolina Dziugaite
Eleni Triantafillou
Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after… (see more) it has already been trained. This is important for key applications, including making the model more accurate by removing outdated, mislabeled, or poisoned data. In this work, we study localized unlearning, where the unlearning algorithm operates on a (small) identified subset of parameters. Drawing inspiration from the memorization literature, we propose an improved localization strategy that yields strong results when paired with existing unlearning algorithms. We also propose a new unlearning algorithm, Deletion by Example Localization (DEL), that resets the parameters deemed-to-be most critical according to our localization strategy, and then finetunes them. Our extensive experiments on different datasets, forget sets and metrics reveal that DEL sets a new state-of-the-art for unlearning metrics, against both localized and full-parameter methods, while modifying a small subset of parameters, and outperforms the state-of-the-art localized unlearning in terms of test accuracy too.
2025-10-19
TMLR (accepted)
doi.org
openreview.net
Toward the Decarbonization of Maritime Supply Chains: A Ship Emissions Prediction Framework
Abdelhak El Aissi
Ismail Bourzak
Loubna Benabbou
Abdelaziz Berrado
Maritime transport is a vital component of international trade, yet the industry contributes substantially to greenhouse gas (GHG) emissions… (see more), with carbon dioxide
2025-10-19
2025 International Conference on Intelligent Systems: Theories and Applications (SITA) (published)
doi.org
High-Dimensional Privacy-Utility Dynamics of Noisy Stochastic Gradient Descent on Least Squares
Shurong Lin
Eric D. Kolaczyk
Adam Smith
Elliot Paquette
2025-10-18
ArXiv (preprint)
doi.org
arxiv.org