Diversifying cheminformatics

Zdrazil, Barbara; Guha, Rajarshi

doi:10.1186/s13321-022-00597-5

Cited by 8 publications

(10 citation statements)

References 8 publications

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“…Research article DrugEx v2: de novo design of drug molecules by Paretobased multi-objective reinforcement learning in polypharmacology [24] cito:extends, cito:citesAsAuthority, cito:usesMethodIn 100% Database PSnpBind: a database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow [25] cito:citesAsDataSource, cito:usesDataFrom, cito:usesMethodIn 23% Editorial Diversifying cheminformatics [26] cito:citesAsAuthority, cito:citesAsEvidence, cito:citesForInformation, cito:citesAsRecommendedReading, cito:citesAsSourceDocument, cito:containsAssertionFrom 100% Data Note DECIMER-hand-drawn molecule images dataset [27] cito:agreesWith, cito:cites, cito:citesAsAuthority, cito:extends, cito:usesDataFrom, cito:usesMethodIn 41% (*) tabular summary of articles of which methods or data is reused. Like citation counts, this data depends totally on explicit citation data.…”

Section: Article Intentions %Citomentioning

confidence: 99%

Two years of explicit CiTO annotations

Willighagen

2023

J Cheminform

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Citations are an essential aspect of research communication and have become the basis of many evaluation metrics in the academic world. Some see citation counts as a mark of scientific impact or even quality, but in reality the reasons for citing other work are manifold which makes the interpretation more complicated than a single citation count can reflect. Two years ago, the Journal of Cheminformatics proposed the CiTO Pilot for the adoption of a practice of annotating citations with their citation intentions. Basically, when you cite a journal article or dataset (or any other source), you also explain why specifically you cite that source. Particularly, the agreement and disagreement and reuse of methods and data are of interest. This article explores what happened after the launch of the pilot. We summarize how authors in the Journal of Cheminformatics used the pilot, shows citation annotations are distributed with Wikidata, visualized with Scholia, discusses adoption outside BMC, and finally present some thoughts on what needs to happen next.

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Section: Article Intentions %Citomentioning

confidence: 99%

Two years of explicit CiTO annotations

Willighagen

2023

J Cheminform

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“…Journals and funders demanding the deposition of research data and the necessary establishment of suitable research data infrastructures will inevitably alleviate the data shortage problem in the future [15,16]. The German government, for example, has recently decided to implement and long-term-fund a national research data infrastructure (Nationale Forschungsdateninfrastruktur, NFDI) [17] with 30 consortia in all areas of science, collaboratively developing open research data management (RDM) e-infrastructures, coordinated by an umbrella process and a joint directorate.…”

Section: Introductionmentioning

confidence: 99%

Open data and algorithms for open science in AI-driven molecular informatics

Brinkhaus

Rajan

Schaub

et al. 2023

Preprint

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Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data, as well as open-source software, have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.

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“…Recently we described [ 1 ] an initiative to put a spotlight on diversity within the cheminformatics community. As part of that we initiated a series of interviews, and this article is the second in that series.…”

Section: Introduction By the Editors-in-chiefmentioning

confidence: 99%

Paths to cheminformatics: Q&A with Phyo Phyo Kyaw Zin

Zin¹

2023

J Cheminform

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Diversifying cheminformatics

Cited by 8 publications

References 8 publications

Two years of explicit CiTO annotations

Two years of explicit CiTO annotations

Open data and algorithms for open science in AI-driven molecular informatics

Paths to cheminformatics: Q&A with Phyo Phyo Kyaw Zin

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