72 citations · 262 across the 18 of their papers we have counts for
17 papers
Flows, straight but not so fast: Exploring the design space of Rectified Flows in Protein Design
Junhua Chen, Simon Mathis, Charles Harris +2
Generative modeling techniques such as Diffusion and Flow Matching have achieved significant successes in generating designable and diverse protein backbones. However, many current…
Evaluating representation learning on the protein structure universe
Arian R. Jamasb, Alex Morehead, Chaitanya K. Joshi +8
We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural Networks. We consider large-scale pre-tr…
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design
Rishabh Anand, Chaitanya K. Joshi, Alex Morehead +7
We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone design. We build upon SE(3) flow matching for protein backbone generation and establish protocols for dat…
Improving Antibody Design with Force-Guided Sampling in Diffusion Models
Paulina Kulytė, Francisco Vargas, Simon Valentin Mathis +3
Antibodies, crucial for immune defense, primarily rely on complementarity-determining regions (CDRs) to bind and neutralize antigens, such as viruses. The design of these CDRs dete…
DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised -transform
Alexander Denker, Francisco Vargas, Shreyas Padhy +7
Generative modelling paradigms based on denoising diffusion processes have emerged as a leading candidate for conditional sampling in inverse problems. In many real-world applicati…
Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation
Divya Nori, Simon V. Mathis, Amir Shanehsazzadeh
The success of therapeutic antibodies relies on their ability to selectively bind antigens. AI-based antibody design protocols have shown promise in generating epitope-specific des…
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design
Kieran Didi, Francisco Vargas, Simon V Mathis +4
Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision. Generative modelling paradigms based on denoising dif…
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi +7
Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graphs with atoms embedded as nodes in 3D Euc…
DiffHopp: A Graph Diffusion Model for Novel Drug Design via Scaffold Hopping
Jos Torge, Charles Harris, Simon V. Mathis +1
Scaffold hopping is a drug discovery strategy to generate new chemical entities by modifying the core structure, the \emph{scaffold}, of a known active compound. This approach pres…
Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?
Charles Harris, Kieran Didi, Arian R. Jamasb +4
Deep generative models for structure-based drug design (SBDD), where molecule generation is conditioned on a 3D protein pocket, have received considerable interest in recent years.…
Predicting protein variants with equivariant graph neural networks
Antonia Boca, Simon Mathis
Pre-trained models have been successful in many protein engineering tasks. Most notably, sequence-based models have achieved state-of-the-art performance on protein fitness predict…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
gRNAde: Geometric Deep Learning for 3D RNA inverse design
Chaitanya K. Joshi, Arian R. Jamasb, Ramon Viñas +5
Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D confo…
On the Expressive Power of Geometric Graph Neural Networks
Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis +2
The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test. However, standard GNNs and the WL framew…
Thought experiments in a quantum computer
Nuriya Nurgalieva, Simon Mathis, Lídia del Rio +1
We introduce a software package that allows users to design and run simulations of thought experiments in quantum theory. In particular, it covers cases where several reasoning age…
Gauge invariant quantum circuits for and Yang-Mills lattice gauge theories
Giulia Mazzola, Simon V. Mathis, Guglielmo Mazzola +1
Quantum computation represents an emerging framework to solve lattice gauge theories (LGT) with arbitrary gauge groups, a general and long-standing problem in computational physics…
Toward scalable simulations of Lattice Gauge Theories on quantum computers
Simon V. Mathis, Guglielmo Mazzola, Ivano Tavernelli
The simulation of real-time dynamics in lattice gauge theories is particularly hard for classical computing due to the exponential scaling of the required resources. On the other h…