85 citations · 192 across the 14 of their papers we have counts for
15 papers
AquaGen: Scaling generative models to molecular dynamics precision on thousands of atoms
Emmanuel Bengio, Sanjeev Raja, Yui Tik Pang +5
We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution…
MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models
Kacper Kapuśniak, Cristian Gabellini, Michael Bronstein +2
Molecular Dynamics (MD) is a powerful computational microscope for probing protein functions. However, the need for fine-grained integration and the long timescales of biomolecular…
Boltz-ABFE: Free Energy Perturbation without Crystal Structures
Stephan Thaler, Zhiyi Wu, William G. Glass +3
Free energy perturbation (FEP) is considered the gold-standard simulation method for estimating small molecule binding affinity, a quantity of vital importance to drug discovery. T…
Torsional-GFN: a conditional conformation generator for small molecules
Alexandra Volokhova, Léna Néhale Ezzine, Piotr Gaiński +5
Generating stable molecular conformations is crucial in several drug discovery applications, such as estimating the binding affinity of a molecule to a target. Recently, generative…
Virtual Cells: Predict, Explain, Discover
Emmanuel Noutahi, Jason Hartford, Prudencio Tossou +12
Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simu…
Implicit Delta Learning of High Fidelity Neural Network Potentials
Stephan Thaler, Cristian Gabellini, Nikhil Shenoy +1
Neural network potentials (NNPs) offer a fast and accurate alternative to ab-initio methods for molecular dynamics (MD) simulations but are hindered by the high cost of training da…
OpenQDC: Open Quantum Data Commons
Cristian Gabellini, Nikhil Shenoy, Stephan Thaler +5
Machine Learning Interatomic Potentials (MLIPs) are a highly promising alternative to force-fields for molecular dynamics (MD) simulations, offering precise and rapid energy and fo…
Role of Structural and Conformational Diversity for Machine Learning Potentials
Nikhil Shenoy, Prudencio Tossou, Emmanuel Noutahi +3
In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically conformational and structural diversity,…
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini, Shenyang Huang, Joao Alex Cunha +32
Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and…
Gotta be SAFE: A New Framework for Molecular Design
Emmanuel Noutahi, Cristian Gabellini, Michael Craig +2
Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of molecular substructures.…
3D Infomax improves GNNs for Molecular Property Prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso +4
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts. Including 3D molecular structure as input to learned mod…
Rethinking Graph Transformers with Spectral Attention
Devin Kreuzer, Dominique Beaini, William L. Hamilton +2
In recent years, the Transformer architecture has proven to be very successful in sequence processing, but its application to other data structures, such as graphs, has remained li…
Geodesics in fibered latent spaces: A geometric approach to learning correspondences between conditions
Tariq Daouda, Reda Chhaibi, Prudencio Tossou +1
This work introduces a geometric framework and a novel network architecture for creating correspondences between samples of different conditions. Under this formalism, the latent s…
Adaptive Deep Kernel Learning
Prudencio Tossou, Basile Dura, Francois Laviolette +2
Deep kernel learning provides an elegant and principled framework for combining the structural properties of deep learning algorithms with the flexibility of kernel methods. By mea…
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
Emmanuel Noutahi, Dominique Beaini, Julien Horwood +2
Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks. Unfortunately, GNNs often fail to capture the relative impo…