34 citations · 119 across the 17 of their papers we have counts for
17 papers
DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models
Yiming Qin, Kai Yi, Miruna Cretu +3
Designing small molecule ligands that bind with high affinity to specific protein pockets is a fundamental goal in drug discovery, as small molecules constitute a major fraction of…
Multi-Scale Protein Structure Modelling with Geometric Graph U-Nets
Chang Liu, Vivian Li, Linus Leong +3
Geometric Graph Neural Networks (GNNs) and Transformers have become state-of-the-art for learning from 3D protein structures. However, their reliance on message passing prevents th…
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…
SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modeling
Andrei Rekesh, Miruna Cretu, Dmytro Shevchuk +6
Synthesizability remains a critical bottleneck in generative molecular design. While recent advances have addressed synthesizability in 2D graphs, extending these constraints to 3D…
TABASCO: A Fast, Simplified Model for Molecular Generation with Improved Physical Quality
Carlos Vonessen, Charles Harris, Miruna Cretu +1
State-of-the-art models for 3D molecular generation are based on significant inductive biases, SE(3), permutation equivariance to respect symmetry and graph message-passing network…
Diffusion-Free Graph Generation with Next-Scale Prediction
Samuel Belkadi, Steve Hong, Marian Chen +3
Autoregressive models excel in efficiency and plug directly into the transformer ecosystem, delivering robust generalization, predictable scalability, and seamless workflows such a…
Towards Mechanistic Interpretability of Graph Transformers via Attention Graphs
Batu El, Deepro Choudhury, Pietro Liò +1
We introduce Attention Graphs, a new tool for mechanistic interpretability of Graph Neural Networks (GNNs) and Graph Transformers based on the mathematical equivalence between mess…
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…
SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints
Miruna Cretu, Charles Harris, Ilia Igashov +6
Generative models see increasing use in computer-aided drug design. However, while performing well at capturing distributions of molecular motifs, they often produce synthetically…
Understanding Biology in the Age of Artificial Intelligence
Elsa Lawrence, Adham El-Shazly, Srijit Seal +6
Modern life sciences research is increasingly relying on artificial intelligence approaches to model biological systems, primarily centered around the use of machine learning (ML)…
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.…
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…
Group Invariant Global Pooling
Kamil Bujel, Yonatan Gideoni, Chaitanya K. Joshi +1
Much work has been devoted to devising architectures that build group-equivariant representations, while invariance is often induced using simple global pooling mechanisms. Little…
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…