ArticleGenomics, proteomics & bioinformatics2023
AB-Gen: Antibody Library Design with Generative Pre-trained Transformer and Deep Reinforcement Learning.
Article in Genomics, proteomics & bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 21 citations in OpenAlex.
- Functional alignment of protein language models via reinforcement learning.Nature communications · 2026Article
- Evidence-aware comparison of sequence-centric machine learning for antibody discovery and optimization.Briefings in bioinformatics · 2026Article
- Heuristic multi-site optimization for protein sequence design using Masked Protein Language Models.PLoS computational biology · 2026Article
- Steering generative models for protein design: Aligning and conditioning strategies.Current opinion in structural biology · 2026Review
- AI-Driven BCR Modeling for Precision Immunology.International journal of molecular sciences · 2026Review
- Technological advancements in antibody-based therapeutics for treatment of diseases.Journal of biomedical science · 2025Review
- Generative Deep Learning Design of Single-Domain Antibodies Against Venezuelan Equine Encephalitis Virus.Antibodies (Basel, Switzerland) · 2025Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin.Computational and structural biotechnology journal · 2025Article
- A comprehensive overview of recent advances in generative models for antibodies.Computational and structural biotechnology journal · 2024Review
- Deep learning-based design and experimental validation of a medicine-like human antibody library.Briefings in bioinformatics · 2024Article
- Development and experimental validation of computational methods for human antibody affinity enhancement.Briefings in bioinformatics · 2024Article
- HELM-GPT: de novo macrocyclic peptide design using generative pre-trained transformer.Bioinformatics (Oxford, England) · 2024Article
- Revolutionizing Antibody Discovery: An Innovative AI Model for Generating Robust Libraries.Genomics, proteomics & bioinformatics · 2023Article
- Improving antibody optimization ability of generative adversarial network through large language model.Computational and structural biotechnology journal · 2023Article
- Transformer technology in molecular science.Wiley interdisciplinary reviews. Computational molecular scienceArticle
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibody leads must fulfill multiple desirable properties to be clinical candidates. Primarily due to the low throughput in the experimental procedure, the need for such multi-property optimization causes the bottleneck in preclinical antibody discovery and development, because addressing one issue usually causes another. We developed a reinforcement learning (RL) method, named AB-Gen, for antibody library design using a generative pre-trained transformer (GPT) as the policy network of the RL agent. We showed that this model can learn the antibody space of heavy chain complementarity determining region 3 (CDRH3) and generate sequences with similar property distributions. Besides, when using human epidermal growth factor receptor-2 (HER2) as the target, the agent model of AB-Gen was able to generate novel CDRH3 sequences that fulfill multi-property constraints. Totally, 509 generated sequences were able to pass all property filters, and three highly conserved residues were identified. The importance of these residues was further demonstrated by molecular dynamics simulations, consolidating that the agent model was capable of grasping important information in this complex optimization task. Overall, the AB-Gen method is able to design novel antibody sequences with an improved success rate than the traditional propose-then-filter approach. It has the potential to be used in practical antibody design, thus empowering the antibody discovery and development process. The source code of AB-Gen is freely available at Zenodo (https://doi.org/10.5281/zenodo.7657016) and BioCode (https://ngdc.cncb.ac.cn/biocode/tools/BT007341).
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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.