Demo Data from EDBT 2026 / reviewers in the wild / expert
Wei-Ching Liang
dblp:354/9435
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Generative modeling · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 6 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Generative modeling
diffusion model |
0.7 | 1 | 2023 | AbDiffuser: full-atom generation of in-vitro functioning antibodies · NeurIPS 2023 |
Machine learning › Generative modeling › diffusion model › geometric diffusion model
equivariant diffusion model |
0.7 | 1 | 2023 | AbDiffuser: full-atom generation of in-vitro functioning antibodies · NeurIPS 2023 |
Machine learning › Generative modeling › protein design
protein structure generation |
0.7 | 1 | 2023 | AbDiffuser: full-atom generation of in-vitro functioning antibodies · NeurIPS 2023 |
Bioinformatics and computational biology › protein design
antibody design |
0.7 | 1 | 2023 | AbDiffuser: full-atom generation of in-vitro functioning antibodies · NeurIPS 2023 |
Bioinformatics and computational biology
protein design |
0.7 | 1 | 2023 | AbDiffuser: full-atom generation of in-vitro functioning antibodies · NeurIPS 2023 |
Bioinformatics and computational biology
protein structure prediction |
0.2 | 1 | 2023 | AbDiffuser: full-atom generation of in-vitro functioning antibodies · NeurIPS 2023 |
Methods — techniques the papers use, named apart from their topics
physics-informed diffusion · 1.3equivariant neural network · 1.3domain knowledge constraints · 0.7domain knowledge constraint · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | AbDiffuser: full-atom generation of in-vitro functioning antibodiesabstractWe introduce AbDiffuser, an equivariant and physics-informed diffusion model for the joint generation of antibody 3D structures and sequences. AbDiffuser is built on top of a new representation of protein structure, relies on a novel architecture for aligned proteins, and utilizes strong diffusion priors to improve the denoising process. Our approach improves protein diffusion by taking advantage of domain knowledge and physics-based constraints; handles sequence-length changes; and reduces memory complexity by an order of magnitude, enabling backbone and side chain generation. We validate AbDiffuser in silico and in vitro. Numerical experiments showcase the ability of AbDiffuser to generate antibodies that closely track the sequence and structural properties of a reference set. Laboratory experiments confirm that all 16 HER2 antibodies discovered were expressed at high levels and that 57.1% of the selected designs were tight binders. Karolis Martinkus, Jan Ludwiczak, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hötzel, Arvind Rajpal, Yan Wu 0027, Kyunghyun Cho, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas |
NeurIPS | 3 |