ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
RESP2: An Uncertainty Aware Multi-Target Multi-Property Optimization AI Pipeline for Antibody Discovery.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Beyond affinity: AI-supported developability assessment and multi-objective optimization in antibody development.Antibody therapeutics · 2026Review
- A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability.Nature biotechnology · 2026Article
- NbBayesLM: bayesian prediction of nanobody thermostability using protein language model.Frontiers in bioinformatics · 2026Article
- RESP2: An Uncertainty Aware Multi-Target Multi-Property Optimization AI Pipeline for Antibody Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
Discovery of therapeutic antibodies against infectious disease pathogens presents distinct challenges. Ideal candidates must possess not only the properties required for any therapeutic antibody (e.g., specificity, low immunogenicity) but also high affinity to many mutants of the target antigen. Here, we present RESP2, an enhanced version of the Rapid Engineering System for Proteins (RESP) pipeline, designed for the discovery of antibodies against one or multiple antigens with simultaneously optimized developability properties. First, we evaluated this pipeline in silico using the Absolut! database of antibodies docked to a variety of target antigens. RESP2 consistently identifies sequences that bind more tightly to groups of target antigens than any sequence present in the training set, with success rates ≥ 85%. As a comparison, popular generative artificial intelligence (AI) techniques achieve success rates <= 1.5%. Next, we used the receptor binding domain (RBD) of the COVID-19 spike protein as a case study, and discovered a highly human antibody with mid to high-affinity binding to at least eight different variants of the RBD. These results illustrate the advantages of RESP2 pipeline for antibody discovery against evolving targets. A Python package that enables users to utilize the RESP pipeline on their own targets is available at https://github.com/Wang-lab-UCSD/RESP2.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.