Classifying Natural Products from Plants, Fungi or Bacteria using the COCONUT Database and Machine Learning

Capecchi, Alice; Reymond, Jean‐Louis

doi:10.26434/chemrxiv-2021-gxjgc-v2

Cited by 1 publication

(1 citation statement)

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“…The PubChem database is used by millions of users every month [35]. An example for the usage of the referenced databases is the creation of a classifier that determines whether a Natural Product (NP) originates from funghi, plants, or bacteria based on its chemical structure with data obtained from the COCONUT database [36]. The ZINC database has recently been used for the in silico determination of drug candidates that inhibit the main protease of SARS-CoV-2 [37].…”

Section: The Importance Of Openly Available Resources and Datamentioning

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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Section: The Importance Of Openly Available Resources and Datamentioning

confidence: 99%