2.3k citations · 2.5k across the 24 of their papers we have counts for
25 papers
Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise
Kumar Shubham, Pavan Karjol, Kiran M K +1
The performance of machine learning models often relies on large labeled datasets; however, data collected from diverse sources can contain label noise. Recent work has shown that,…
Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration
Alan Kai Hassen, Andrius Bernatavicius, Antonius P. A. Janssen +3
Applications of machine learning in chemistry are often limited by the scarcity and expense of labeled data, restricting traditional supervised methods. In this work, we introduce…
FLOWR.root: A flow matching based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction
Julian Cremer, Tuan Le, Mohammad M. Ghahremanpour +3
We present FLOWR.root, an SE(3)-equivariant flow-matching model for pocket-aware 3D ligand generation with joint potency and binding affinity prediction and confidence estimation.…
Exact Solutions to the Quantum Schrödinger Bridge Problem
Mykola Bordyuh, Djork-Arné Clevert, Marco Bertolini
The Quantum Schrödinger Bridge Problem (QSBP) describes the evolution of a stochastic process between two arbitrary probability distributions, where the dynamics are governed by th…
Fast and scalable retrosynthetic planning with a transformer neural network and speculative beam search
Mikhail Andronov, Natalia Andronova, Michael Wand +2
AI-based computer-aided synthesis planning (CASP) systems are in demand as components of AI-driven drug discovery workflows. However, the high latency of such CASP systems limits t…
FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation
Julian Cremer, Ross Irwin, Alessandro Tibo +3
We introduce FLOWR, a novel structure-based framework for the generation and optimization of three-dimensional ligands. FLOWR integrates continuous and categorical flow matching wi…
Diffusion Generative Modeling on Lie Group Representations
Marco Bertolini, Tuan Le, Djork-Arné Clevert
We introduce a novel class of score-based diffusion processes that operate directly in the representation space of Lie groups. Leveraging the framework of Generalized Score Matchin…
KGARevion: An AI Agent for Knowledge-Intensive Biomedical QA
Xiaorui Su, Yibo Wang, Shanghua Gao +4
Biomedical reasoning integrates structured, codified knowledge with tacit, experience-driven insights. Depending on the context, quantity, and nature of available evidence, researc…
Accelerating the inference of string generation-based chemical reaction models for industrial applications
Mikhail Andronov, Natalia Andronova, Michael Wand +2
Template-free SMILES-to-SMILES translation models for reaction prediction and single-step retrosynthesis are of interest for industrial applications in computer-aided synthesis pla…
PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling
Julian Cremer, Tuan Le, Frank Noé +2
The generation of ligands that both are tailored to a given protein pocket and exhibit a range of desired chemical properties is a major challenge in structure-based drug design. H…
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
Tuan Le, Julian Cremer, Frank Noé +2
Deep generative diffusion models are a promising avenue for 3D de novo molecular design in materials science and drug discovery. However, their utility is still limited by suboptim…
Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis Planning
Paula Torren-Peraire, Alan Kai Hassen, Samuel Genheden +4
Retrosynthesis consists of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found with the go…
From slides (through tiles) to pixels: an explainability framework for weakly supervised models in pre-clinical pathology
Marco Bertolini, Van-Khoa Le, Jake Pencharz +4
In pre-clinical pathology, there is a paradox between the abundance of raw data (whole slide images from many organs of many individual animals) and the lack of pixel-level slide a…
Equivariant Graph Attention Networks for Molecular Property Prediction
Tuan Le, Frank Noé, Djork-Arné Clevert
Learning and reasoning about 3D molecular structures with varying size is an emerging and important challenge in machine learning and especially in drug discovery. Equivariant Grap…
Explaining, Evaluating and Enhancing Neural Networks' Learned Representations
Marco Bertolini, Djork-Arné Clevert, Floriane Montanari
Most efforts in interpretability in deep learning have focused on (1) extracting explanations of a specific downstream task in relation to the input features and (2) imposing const…
Unsupervised Learning of Group Invariant and Equivariant Representations
Robin Winter, Marco Bertolini, Tuan Le +2
Equivariant neural networks, whose hidden features transform according to representations of a group G acting on the data, exhibit training efficiency and an improved generalisatio…
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
Ryan Henderson, Djork-Arné Clevert, Floriane Montanari
Rationalizing which parts of a molecule drive the predictions of a molecular graph convolutional neural network (GCNN) can be difficult. To help, we propose two simple regularizati…
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
Robin Winter, Frank Noé, Djork-Arné Clevert
Recently, there has been great success in applying deep neural networks on graph structured data. Most work, however, focuses on either node- or graph-level supervised learning, su…
Parameterized Hypercomplex Graph Neural Networks for Graph Classification
Tuan Le, Marco Bertolini, Frank Noé +1
Despite recent advances in representation learning in hypercomplex (HC) space, this subject is still vastly unexplored in the context of graphs. Motivated by the complex and quater…
Auto-Encoding Molecular Conformations
Robin Winter, Frank Noé, Djork-Arné Clevert
In this work we introduce an Autoencoder for molecular conformations. Our proposed model converts the discrete spatial arrangements of atoms in a given molecular graph (conformatio…
Gini in a Bottleneck: Sparse Molecular Representations for Graph Convolutional Neural Networks
Ryan Henderson, Djork-Arné Clevert, Floriane Montanari
Due to the nature of deep learning approaches, it is inherently difficult to understand which aspects of a molecular graph drive the predictions of the network. As a mitigation str…
IVE-GAN: Invariant Encoding Generative Adversarial Networks
Robin Winter, Djork-Arné Clevert
Generative adversarial networks (GANs) are a powerful framework for generative tasks. However, they are difficult to train and tend to miss modes of the true data generation proces…
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter
We introduce the "exponential linear unit" (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectified linear units (ReLUs)…
Rectified Factor Networks
Djork-Arné Clevert, Andreas Mayr, Thomas Unterthiner +1
We propose rectified factor networks (RFNs) to efficiently construct very sparse, non-linear, high-dimensional representations of the input. RFN models identify rare and small even…