Scientists reveal machine learning could accelerate discovery of antimalarial properties in plants
- The discovery of potential antimalarial properties in plants could be accelerated by machine learning finds new study
- Researchers reviewed 21,000 species from three plant families and estimate that over a third could have antimalarial properties and merit further investigation
- Experts believe at least 1,300 active anti-Plasmodium species may have been missed using conventional approaches
A new study published on 25 May 2023 in Frontiers by researchers at the Royal Botanic Gardens, Kew and partners have identified that the use of machine learning could speed up the search for plants with antimalarial properties.
Malaria is one of humankind’s deadliest diseases and remains a significant global public health challenge. Malaria is caused by the Plasmodium parasite, which is spread to humans through the bite of infected mosquitoes. In 2021, the World Health Organisation found that there were an estimated 247 million cases of malaria worldwide. Resistance to existing antimalarial drugs is an escalating challenge for eliminating malaria and, as a result, the WHO recommends that research into antimalarial medicines should be accelerated as part of an effort to reach global malaria targets.
Plants have provided or inspired the development of numerous pharmaceutical drugs as they are a rich source of bioactive compounds. For example, two major pharmaceutical drugs used in the treatment of malaria, quinine, and artemisinin, are derived from plants. However, considering that there are an estimated 343,000 vascular plant species, identifying plants containing anti-Plasmodium compounds can be time-consuming and costly.
As part of the new study, scientists aimed to assess whether machine learning models could be trained on plant trait data to predict the anti-Plasmodium activity of plants. To do this they studied three flowering plant families – Apocynaceae, Loganiaceae and Rubiaceae – together comprising 21,100 species. Researchers explored an array of methods to demonstrate the effectiveness of machine learning algorithms and compared their performance with other approaches often used to select plants to find sources of bioactive compounds.
The experts found that the techniques provide a promising approach to improve the ability to predict plants with anti-Plasmodium properties, which could greatly accelerate the search for new compounds of pharmaceutical interest. Researchers estimate that 7,677 species in the three families warrant further investigation and that at least 1,300 active anti-Plasmodium species may have been missed using conventional approaches.
Royal Botanic Gardens, Kew Research Fellow Adam Richard-Bollans said: ‘Our results highlight the vast unexplored potential of plants to produce novel medicines. There are an estimated 343,000 known vascular plant species that remain largely unexplored scientifically, and we hope that our machine learning approach can be employed to efficiently search these plants for new medicinal compounds. These results also reinforce the importance of protecting biodiversity and using natural resources sustainably in order to preserve this valuable resource’.
University of Fribourg and SIB Swiss Institute of Bioinformatics Group Leader Daniele Silvestro says: ‘Our study shows that machine learning offers the tools to combine scientific knowledge of plants and their traditional uses into a powerful predictive framework that can guide future testing and research. Biodiversity likely holds solutions to existing and future global health issues, and machine learning coupled with continued rigorous research in biology can help unlocking this potential’.
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NOTES TO EDITORS
About Kew Science
Kew Science is the driving force behind RBG Kew’s mission to understand and protect plants and fungi, for the well-being of people and the future of all life on Earth. Over 470 Kew scientists work with partners in more than 100 countries worldwide to halt biodiversity loss, uncover secrets of the natural world, and to conserve and restore the extraordinary diversity of plants and fungi. Kew’s Science Strategy 2021–2025 lays out five scientific priorities to aid these goals: research into the protection of biodiversity through Ecosystem Stewardship, understanding the variety and evolution of traits in plants and fungi through Trait Diversity and Function; digitising and sharing tools to analyse Kew’s scientific collections through Digital Revolution; using new technologies to speed up the naming and characterisation of plants through Accelerated Taxonomy; and cultivating new scientific and commercial partnerships in the UK and globally through Enhanced Partnerships. One of Kew’s greatest international collaborations is the Millennium Seed Bank Partnership, which has to date stored more than 2.4 billion seeds of over 40,000 wild species of plants across the globe. In 2020, Kew scientists estimated in the State of the World’s Plants and Fungi report that 2 in 5 plants globally are threatened with extinction.
About the SIB Swiss Institute of Bioinformatics
SIB is an internationally recognized non-profit organization, dedicated to biological and biomedical data science. Its data scientists are passionate about creating knowledge and solving complex questions in many fields, from biodiversity and evolution to medicine. They provide essential databases and software platforms as well as bioinformatics expertise and services to academic, clinical, and industry groups. SIB federates the Swiss bioinformatics community of some 900 scientists, encouraging collaboration and knowledge sharing. The institute contributes to keeping Switzerland at the forefront of innovation by fostering progress in biological research and enhancing health.
About the University of Fribourg
The University of Fribourg (Unifr) is an institution of higher education and research, an employer and serves as a venue for numerous events. As such, it is a place of innovation and an important driver of economic and cultural life in the region. Since it was founded in 1889, the University of Fribourg has drawn students and researchers from all over the world. It offers a full range of study programmes and disciplines and gives great importance to interdisciplinary research. With over 10,000 students for a total population of 40,000 inhabitants, it is not surprising that UniFr shapes local community life more than in any other Swiss town. In most cases, the language of instruction is French, German or both languages. Many courses are also taught in English, including all MSc programmes. The possibility of obtaining a bilingual university degree is unique in Europe. UniFr has over fifty study programmes in five different faculties. In addition, UniFr has numerous interdisciplinary institutes and competence centres, including a national centre of competence in research (NCCR) as well as several interdisciplinary research centres. UniFr offers an international environment in a medieval setting; it acts as a bridge between two linguistic cultures. Moreover, top-notch teaching quality is facilitated by very favourable professor-to-student ratio.