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Are new ideas harder to find? A note on incremental research and Journal of Cheminformatics’ Scientific Contribution Statement
Journal of Cheminformatics ( IF 8.6 ) Pub Date : 2024-01-15 , DOI: 10.1186/s13321-023-00798-6
Barbara Zdrazil , Rajarshi Guha , Karina Martinez-Mayorga , Nina Jeliazkova

In the field of cheminformatics, technological advancements in recent times include, e.g., the way chemical information is being represented for large scale screening and de novo drug design. Especially, chemical language models originating from natural language processing offer new opportunities for molecular design [1].

However, for science in general and compared to past decades, recent paucity of transformative ideas has been noticed [2]. While there are many explanations for observed technological stagnation, in pharmaceutical R&D, a productivity crisis was already noted ~20 years ago [3, 4]. An often stated scientific/technological reason for stagnation in pharmaceutical R&D, is the “low hanging fruit” problem. That is, the easier-to-tackle problems have been solved already and that what remains are the more complex and more challenging problems (diseases) [5]. Other possible explanations for declining research productivity might be a shift to more “defensive R&D”, which could be a direct consequence of R&D resources being diverted away from risk-taking by investors, managers, and entrepreneurs to instead fulfil regulatory requirements. Instead of fueling innovation, monetary resources are used to keep “old” products on the market [5].

At the same time, we do observe a trend that more and more papers are being published in scientific journals or on preprint servers [6]. In line with this observation, also more data, methods, and models are being made available in the public domain (through publications and/or platforms such as Zenodo [7], GitHub [8], and Hugging Face [9]). As an effect, researchers are often facing a situation of information-overload with the luxurious problem of filtering out the real innovative contributions, that aren’t just incremental improvements of existing ones.

From a publisher’s perspective, every research paper should be regarded as an attempt to contribute new ideas and/or refine old ones. In Cheminformatics, we have observed a few phases of new methodological developments/inventions with consequent iterations of incremental improvements. Examples include (but are not limited to) molecular representation [10], descriptors for QSPR modeling/ML [11, 12], molecular docking algorithms [13], or more recently the development and refinement of generative AI algorithms [14, 15].

The Editors of the Journal of Cheminformatics do not judge articles based purely on scientific novelty. Rather, we consider aspects such as utility and availability, and the contribution itself, along with notions of novelty.

The Scientific Contribution Statement is our attempt to give space to a declaration by authors regarding the contributions made in their research. The authors should use a maximum of three sentences to specifically highlight the scientific contributions that advance the field and what differentiates their contribution from prior work on this topic (https://jcheminf.biomedcentral.com/submission-guidelines/preparing-your-manuscript/research). It should be regarded by authors as an opportunity to highlight their scientific contribution(s) rather than as a burden or additional request by the Editors of J. Cheminform. Such declaration(s) about contributions and novelty have always been part of the scientific publication process—albeit in a more convoluted or scattered way, as there usually isn’t a specific section in a paper dedicated to such declarations. We therefore started to make this vital information more accessible by assigning it a fixed section (namely the Abstract of a paper).

We hope that this amendment will not only help us as Editors when assessing a paper submitted for consideration, but equally the members of our scientific community— reviewers and readers.

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  9. https://huggingface.co/

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Authors and Affiliations

  1. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK

    Barbara Zdrazil

  2. Vertex Pharmaceuticals, 50 Northern Ave, 02210, Boston, MA, USA

    Rajarshi Guha

  3. Institute of Chemistry, National Autonomous University of Mexico, Campus Merida, Merida-Tetiz Highway, Km. 4.5, Ucu, Yucatan, Mexico

    Karina Martinez-Mayorga

  4. Ideaconsult Ltd, 1000, Sofia, Bulgaria

    Nina Jeliazkova

Authors
  1. Barbara ZdrazilView author publications

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  2. Rajarshi GuhaView author publications

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  3. Karina Martinez-MayorgaView author publications

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  4. Nina JeliazkovaView author publications

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Contributions

BZ drafted the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Barbara Zdrazil.

Competing interests

The authors declare that they have no competing interests.

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Zdrazil, B., Guha, R., Martinez-Mayorga, K. et al. Are new ideas harder to find? A note on incremental research and Journal of Cheminformatics’ Scientific Contribution Statement. J Cheminform 16, 6 (2024). https://doi.org/10.1186/s13321-023-00798-6

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中文翻译：
新想法更难找到吗？关于增量研究和《化学信息学杂志》科学贡献声明的说明

在化学信息学领域，近年来的技术进步包括例如用于大规模筛选和从头药物设计的化学信息的表示方式。特别是，源自自然语言处理的化学语言模型为分子设计提供了新的机会[1]。

然而，对于一般科学来说，与过去几十年相比，人们注意到最近缺乏变革性的想法[2]。尽管对观察到的技术停滞有多种解释，但在医药研发领域，生产力危机在大约 20 年前就已被注意到 [3, 4]。医药研发停滞的一个经常被提及的科学/技术原因是“容易实现的目标”问题。也就是说，比较容易解决的问题已经解决了，剩下的就是更复杂、更具挑​​战性的问题（疾病）[5]。研究生产力下降的其他可能解释可能是转向更加“防御性研发”，这可能是研发资源从投资者、管理者和企业家承担风险转向满足监管要求的直接后果。货币资源不是用来推动创新，而是用来将“旧”产品留在市场上[5]。

与此同时，我们确实观察到越来越多的论文发表在科学期刊或预印本服务器上的趋势[6]。根据这一观察，更多的数据、方法和模型也在公共领域提供（通过出版物和/或平台，如 Zenodo [7]、GitHub [8] 和 Hugging Face [9]）。结果，研究人员经常面临信息过载的情况，并面临过滤掉真正的创新贡献的奢侈问题，而这些贡献不仅仅是对现有贡献的渐进式改进。

从出版商的角度来看，每一篇研究论文都应被视为贡献新想法和/或完善旧想法的尝试。在化学信息学中，我们观察到新方法开发/发明的几个阶段以及随后的增量改进迭代。示例包括（但不限于）分子表示 [10]、QSPR 建模/ML 描述符 [11, 12]、分子对接算法 [13]，或者最近生成 AI 算法的开发和完善 [14, 15] 。

《化学信息学杂志》的编辑不会纯粹根据科学新颖性来评判文章。相反，我们考虑实用性和可用性等方面，以及贡献本身，以及新颖性的概念。

科学贡献声明是我们试图为作者提供有关其研究贡献的声明的空间。作者应最多使用三句话来具体强调推动该领域发展的科学贡献，以及他们的贡献与该主题之前的工作有何不同 (https://jcheminf.biomedcentral.com/submission-guidelines/preparing-your-manuscript /研究）。作者应将其视为强调其科学贡献的机会，而不是 J. Cheminform 编辑的负担或额外要求。这种关于贡献和新颖性的声明一直是科学出版过程的一部分——尽管以一种更加复杂或分散的方式，因为论文中通常没有专门针对此类声明的特定部分。因此，我们开始通过为其分配一个固定部分（即论文的摘要）来使这些重要信息更容易访问。

我们希望这项修正案不仅能帮助我们作为编辑评估提交审议的论文，而且同样能帮助我们科学界的成员——审稿人和读者。

  1. Grisoni F (2023) 用于从头药物设计的化学语言模型：挑战和机遇。当前观点结构生物学 79：102527。https://doi.org/10.1016/j.sbi.2023.102527

    文章 CAS PubMed 谷歌学术

  2. Bhaskar M (2021) 人类前沿：小思维时代大创意的未来

  3. Pammolli F, Magazzini L, Riccaboni M (2011) 药品研发的生产力危机。《自然评论药物发现》10：428–438。https://doi.org/10.1038/nrd3405

    文章 CAS PubMed 谷歌学术

  4. Laermann-Nguyen U、Backfisch M (2021) 制药行业的创新危机？一项调查。SN 巴士经济 1:164。https://doi.org/10.1007/s43546-021-00163-5

    文章谷歌学术

  5. Cockburn IM (2006) 制药行业是否陷入生产力危机？创新政策经济学7：1-32。https://doi.org/10.1086/ipe.7.25056188

    文章谷歌学术

  6. Rawat S, Meena S (2014) 出版或灭亡：我们将走向何方？医学研究杂志 19:87–89

    PubMed PubMed 中心 Google 学术搜索

  7. https://zenodo.org/

  8. https://github.com/

  9. https://huggingface.co/

  10. Wigh DS、Goodman JM、Lapkin AA (2022) 机器学习时代分子表征综述。电线计算分子科学 12：e1603。https://doi.org/10.1002/wcms.1603

    文章谷歌学术

  11. Balaban AT (2009) 药物设计，分子描述符。见：Meyers RA（编辑）复杂性和系统科学百科全书。施普林格，纽约州，第 2196–2215 页

    章谷歌学术

  12. Sahoo S、Adhikari C、Kuanar M、Mishra BK (2016) 对分子描述符的生成及其在定量结构性质/活性关系中的应用的简短回顾。当前计算机辅助药物设计 12：181–205。https://doi.org/10.2174/1573409912666160525112114

    文章 CAS PubMed 谷歌学术

  13. Torres PHM、Sodero ACR、Jofily P、Silva-Jr FP (2019) 药物设计分子对接的关键主题。国际分子科学杂志 20：4574。https://doi.org/10.3390/ijms20184574

    文章 CAS PubMed PubMed Central Google Scholar

  14. Bandi A、Adapa PVSR、Kuchi YEVPK (2023) 生成式 AI 的力量：对需求、模型、输入输出格式、评估指标和挑战的回顾。未来互联网 15:260。https://doi.org/10.3390/fi15080260

    文章谷歌学术

  15. Shokrollahi Y、Yarmohammadtoosky S、Nikahd MM 等人 (2023) 医疗保健中生成式人工智能的全面综述

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作者和单位

  1. 欧洲分子生物学实验室，欧洲生物信息学研究所 (EMBL-EBI)，Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK

    芭芭拉·兹德拉齐尔

  2. Vertex 制药公司，50 Northern Ave, 02210, Boston, MA, USA

    拉贾什·古哈

  3. 墨西哥国立自治大学化学研究所，梅里达校区，梅里达-泰蒂兹高速公路，公里。4.5, Ucu, 尤卡坦半岛, 墨西哥

    卡琳娜·马丁内斯·马约尔加

  4. Ideaconsult Ltd, 1000, 索非亚, 保加利亚

    尼娜·杰利亚兹科娃

作者
  1. Barbara Zdrazil查看作者出版物

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  2. Rajarshi Guha查看作者出版物

    您也可以在PubMed Google Scholar中搜索该作者 

  3. 卡琳娜·马丁内斯-马约尔加查看作者出版物

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  4. Nina Jeliazkova查看作者出版物

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贡献

BZ 起草了手稿。所有作者阅读并认可的终稿。

通讯作者

通讯作者：芭芭拉·兹德拉齐尔。

利益争夺

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施普林格·自然对于已出版的地图和机构隶属关系中的管辖权主张保持中立。

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Zdrazil, B.、Guha, R.、Martinez-Mayorga, K.等人。新想法更难找到吗？关于增量研究和《化学信息学杂志》科学贡献声明的说明。《化学信息》杂志 16 , 6 (2024)。https://doi.org/10.1186/s13321-023-00798-6

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