6 papers
Flexible Flows for Biological Sequence Design
Yogesh Verma, Dani Korpela, Harri Lähdesmäki +1
Designing functional biological sequences requires navigating vast discrete spaces under strict evolutionary and biophysical constraints. Discrete Flow Matching (DFM) offers a gene…
Let Physics Guide Your Protein Flows: Topology-aware Unfolding and Generation
Yogesh Verma, Markus Heinonen, Vikas Garg
Protein structure prediction and folding are fundamental to understanding biology, with recent deep learning advances reshaping the field. Diffusion-based generative models have re…
Positional Encoding meets Persistent Homology on Graphs
Yogesh Verma, Amauri H. Souza, Vikas Garg
The local inductive bias of message-passing graph neural networks (GNNs) hampers their ability to exploit key structural information (e.g., connectivity and cycles). Positional enc…
E(3)-equivariant models cannot learn chirality: Field-based molecular generation
Alexandru Dumitrescu, Dani Korpela, Markus Heinonen +4
Obtaining the desired effect of drugs is highly dependent on their molecular geometries. Thus, the current prevailing paradigm focuses on 3D point-cloud atom representations, utili…
Robust Simulation-Based Inference under Missing Data via Neural Processes
Yogesh Verma, Ayush Bharti, Vikas Garg
Simulation-based inference (SBI) methods typically require fully observed data to infer parameters of models with intractable likelihood functions. However, datasets often contain…
Diffusion Twigs with Loop Guidance for Conditional Graph Generation
Giangiacomo Mercatali, Yogesh Verma, Andre Freitas +1
We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or…