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Wei-Ching Liang

dblp:354/9435 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
diffusion model
0.712023
AbDiffuser: full-atom generation of in-vitro functioning antibodies · NeurIPS 2023
Machine learning › Generative modeling › diffusion model › geometric diffusion model
equivariant diffusion model
0.712023
AbDiffuser: full-atom generation of in-vitro functioning antibodies · NeurIPS 2023
Machine learning › Generative modeling › protein design
protein structure generation
0.712023
AbDiffuser: full-atom generation of in-vitro functioning antibodies · NeurIPS 2023
Bioinformatics and computational biology › protein design
antibody design
0.712023
AbDiffuser: full-atom generation of in-vitro functioning antibodies · NeurIPS 2023
Bioinformatics and computational biology
protein design
0.712023
AbDiffuser: full-atom generation of in-vitro functioning antibodies · NeurIPS 2023
Bioinformatics and computational biology
protein structure prediction
0.212023
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
YearPublicationVenuePosition
2023 AbDiffuser: full-atom generation of in-vitro functioning antibodies
abstract
We 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
NeurIPS3