AIntibody: an experimentally validated in silico antibody discovery design challenge

IF 33.1 1区 生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
M. Frank Erasmus, Laura Spector, Fortunato Ferrara, Roberto DiNiro, Thomas J. Pohl, Katheryn Perea-Schmittle, Wei Wang, Peter M. Tessier, Crystal Richardson, Laure Turner, Sumit Kumar, Daniel Bedinger, Pietro Sormanni, Monica L. Fernández-Quintero, Andrew B. Ward, Johannes R. Loeffler, Olivia M. Swanson, Charlotte M. Deane, Matthew I. J. Raybould, Andreas Evers, Carolin Sellmann, Sharrol Bachas, Jeff Ruffolo, Horacio G. Nastri, Karthik Ramesh, Jesper Sørensen, Rebecca Croasdale-Wood, Oliver Hijano, Camila Leal-Lopes, Melody Shahsavarian, Yu Qiu, Paolo Marcatili, Erik Vernet, Rahmad Akbar, Simon Friedensohn, Rick Wagner, Vinodh babu Kurella, Shipra Malhotra, Satyendra Kumar, Patrick Kidger, Juan C. Almagro, Eric Furfine, Marty Stanton, Christilyn P. Graff, Santiago David Villalba, Florian Tomszak, Andre A. R. Teixeira, Elizabeth Hopkins, Molly Dovner, Sara D’Angelo, Andrew R. M. Bradbury
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Abstract

Science is frequently subject to the Gartner hype cycle1: emergent technologies spark intense initial enthusiasm with the recruitment of dedicated scientists. As limitations are recognized, disillusionment often sets in; some scientists turn away, disappointed in the inability of the new technology to deliver on initial promise, while others persevere and further develop the technology. Although the value (or not) of a new technology usually becomes clear with time, appropriate benchmarks can be invaluable in highlighting strengths and areas for improvement, substantially speeding up technology maturation. A particular challenge in computational engineering and artificial intelligence (AI)/machine learning (ML) is that benchmarks and best practices are uncommon, so it is particularly hard for non-experts to assess the impact and performance of these methods. Although multiple papers have highlighted best practices and evaluation guidelines2,3,4, the true test for such methods is ultimately prospective performance, which requires experimental testing.

Abstract Image

AIntibody:经实验验证的硅学抗体发现设计挑战
科学经常受到 Gartner 炒作周期1 的影响:新兴技术最初会引发强烈的热情,并招募专职科学家。一些科学家因新技术无法兑现最初的承诺而失望离开,而另一些科学家则坚持不懈,进一步开发该技术。虽然一项新技术的价值(或不价值)通常会随着时间的推移而逐渐显现,但适当的基准对于突出优势和有待改进的领域非常宝贵,可大大加快技术成熟的速度。计算工程和人工智能(AI)/机器学习(ML)领域面临的一个特殊挑战是,基准和最佳实践并不常见,因此非专业人士很难评估这些方法的影响和性能。虽然有多篇论文强调了最佳实践和评估指南2,3,4,但对这些方法的真正检验最终还是要看预期性能,这就需要进行实验测试。
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来源期刊
Nature biotechnology
Nature biotechnology 工程技术-生物工程与应用微生物
CiteScore
63.00
自引率
1.70%
发文量
382
审稿时长
3 months
期刊介绍: Nature Biotechnology is a monthly journal that focuses on the science and business of biotechnology. It covers a wide range of topics including technology/methodology advancements in the biological, biomedical, agricultural, and environmental sciences. The journal also explores the commercial, political, ethical, legal, and societal aspects of this research. The journal serves researchers by providing peer-reviewed research papers in the field of biotechnology. It also serves the business community by delivering news about research developments. This approach ensures that both the scientific and business communities are well-informed and able to stay up-to-date on the latest advancements and opportunities in the field. Some key areas of interest in which the journal actively seeks research papers include molecular engineering of nucleic acids and proteins, molecular therapy, large-scale biology, computational biology, regenerative medicine, imaging technology, analytical biotechnology, applied immunology, food and agricultural biotechnology, and environmental biotechnology. In summary, Nature Biotechnology is a comprehensive journal that covers both the scientific and business aspects of biotechnology. It strives to provide researchers with valuable research papers and news while also delivering important scientific advancements to the business community.
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