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Yuanqing Zhou

dblp:384/4164 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—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.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%
Artificial intelligence
1 paper
Generative modeling · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › protein design
antibody affinity maturation
0.812024
GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation · ICML 2024
Bioinformatics and computational biology › protein design
antibody design
0.812024
GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation · ICML 2024
Bioinformatics and computational biology
protein design
0.812024
GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation · ICML 2024
Bioinformatics and computational biology
protein engineering
0.812024
GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation · ICML 2024

Methods — techniques the papers use, named apart from their topics

position refinement · 1.5mutation effect prediction · 1.5generative geometry initialization · 1.5
YearPublicationVenuePosition
2024 GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation
abstract
Increasing works for antibody design are emerging to generate sequences and structures in Complementarity Determining Regions (CDRs), but problems still exist. We focus on two of them: (i) authenticity of the generated structure and (ii) rationality of the affinity maturation, and propose GeoAB as a solution. In specific, GeoAB-Designergenerates CDR structures with realistic internal geometries, composed of a generative geometry initializer (Geo-Initializer) and a position refiner (Geo-Refiner); GeoAB-Optimizer achieves affinity maturation by accurately predicting both the mutation effects and structures of mutant antibodies with the same network architecture as Geo-Refiner. Experiments show that GeoAB achieves state-of-the-art performance in CDR co-design and mutation effect predictions, and fulfills the discussed tasks effectively.
Lirong Wu, Yufei Huang 0002, Yunfan Liu 0002, Odin Zhang, Yuanqing Zhou, Stan Z. Li
ICML6