Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects

Bai, Ganggang; Sun, Chuance; Guo, Zhizhou; Wang, Yangjing; Zeng, Xincheng; Su, Yuhong; Zhao, Qi; Ma, Buyong

doi:10.1016/j.semcancer.2023.06.005

Cited by 9 publications

(4 citation statements)

References 116 publications

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“…Protein diffusion is an exciting and new technology. Built on the progress in large language models (LLMs) for general artificial intelligence, these models have potential to revolutionize the drug development process and reducing the initial lab-based development workload in favor of in silico exploration (36, 37). State-of-the-art protein generation models like EvoDiff from Microsoft Research (22) (used in this study), RFdiffusion (38) from the Baker Lab at the University of Washington, and Chroma (39) from Generate:Biomedicines have shown real promise in AI-based drug design.…”

Section: Discussionmentioning

confidence: 99%

PD-1 Targeted Antibody Discovery Using AI Protein Diffusion

Ford

2024

Preprint

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The programmed cell death protein 1 (PD-1, CD279) is an important therapeutic target in many oncological diseases. This checkpoint protein inhibits T lymphocytes from attacking other cells in the body and thus inhibiting it improves the clearance of tumor cells by the immune system. While there are already multiple FDA-approved anti-PD-1 antibodies, including nivolumab (Opdivo from Bristol-Myers Squibb) and pembrolizumab (Keytruda from Merck), there are ongoing efforts to discover new and improved checkpoint inhibitor therapeutics. In this study, we present multiple anti-PD-1 antibody fragments that were derived computationally using protein diffusion and evaluated through our scalable, in silico pipeline. Here we present nine synthetic Fv structures that are suitable for further empirical testing of their anti-PD-1 activity due to desirable predicted binding performance.

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Section: Discussionmentioning

confidence: 99%

PD-1 Targeted Antibody Discovery Using AI Protein Diffusion

Ford

2024

Preprint

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“… 13 , 18 In recent years, rapid progress has been made in developing machine learning strategies to firstly predict and secondly optimize biotherapeutic properties, aiming to reduce experimental screening requirements. 155–157 To train accurate machine learning models, however, large and high-quality experimental datasets must initially be generated. Therefore, even as new machine learning strategies more regularly become integrated into the bsAb discovery process over the coming years, HTP bsAb production and screening will remain a crucial capability.…”

Section: Discussionmentioning

confidence: 99%

Facilitating high throughput bispecific antibody production and potential applications within biopharmaceutical discovery workflows

Fawcett,

Tickle,

Coles

2024

mAbs

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A major driver for the recent investment surge in bispecific antibody (bsAb) platforms and products is the multitude of distinct mechanisms of action that bsAbs offer compared to a combination of two monoclonal antibodies. Four bsAb products were granted first regulatory approvals in the US or EU during 2023 and the biopharmaceutical industry pipeline is brimming with bsAb candidates across a broad range of therapeutic applications. In previously reported bsAb discovery campaigns, following a hypothesis-based choice of two specific target proteins, selections and screening activities have often been performed in mono-specific formats. The conversion to bispecific modalities has usually been positioned toward the end of the discovery process and has involved small numbers of lead molecules, largely due to challenges in expressing, purifying, and analyzing large numbers of bsAbs. In this review, we discuss emerging strategies to facilitate the production of expanded bsAb panels, focusing particularly upon combinatorial methods to generate bsAb matrices. Such technologies will enable screening in. bispecific formats at earlier stages of discovery campaigns, not only widening the accessible protein space to maximize chances of success, but also advancing empirical bi-target validation activities to assess initial target selection hypotheses.

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“…The possibilities for designing therapeutic mAbs have considerably increased as a result of recent developments in artificial intelligence (AI), machine learning (ML), and deep learning ( 61 ). The main focus of ML is the creation of prediction models, wherein data are presented as a collection of attributes.…”

Section: Ab Discoverymentioning

confidence: 99%

Recent Advances in the Development of Monoclonal Antibodies and Next-Generation Antibodies

Singh,

Chandley,

Rohatgi

2023

ImmunoHorizons

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mAbs are highly indispensable tools for diagnostic, prophylactic, and therapeutic applications. The first technique, hybridoma technology, was based on fusion of B lymphocytes with myeloma cells, which resulted in generation of single mAbs against a specific Ag. Along with hybridoma technology, several novel and alternative methods have been developed to improve mAb generation, ranging from electrofusion to the discovery of completely novel technologies such as B cell immortalization; phage, yeast, bacterial, ribosome, and mammalian display systems; DNA/RNA encoded Abs; single B cell technology; transgenic animals; and artificial intelligence/machine learning. This commentary outlines the evolution, methodology, advantages, and limitations of various mAb production techniques. Furthermore, with the advent of next-generation Ab technologies such as single-chain variable fragments, nanobodies, bispecific Abs, Fc-engineered Abs, Ab biosimilars, Ab mimetics, and Ab-drug conjugates, the healthcare and pharmaceutical sectors have become resourceful to develop highly specific mAb treatments against various diseases such as cancer and autoimmune and infectious diseases.

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Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects

Cited by 9 publications

References 116 publications

PD-1 Targeted Antibody Discovery Using AI Protein Diffusion

PD-1 Targeted Antibody Discovery Using AI Protein Diffusion

Facilitating high throughput bispecific antibody production and potential applications within biopharmaceutical discovery workflows

Recent Advances in the Development of Monoclonal Antibodies and Next-Generation Antibodies

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