Machine learning enhances prediction of plants as potential sources of antimalarials

Frontiers in Plant Science

Plant Science

Daniele Silvestro¹  Ian Ondo²  Oscar A. Pérez-Escobar²  David Russell²  Adam Richard-Bollans²  Cássia Bitencourt²  Eve Lucas²  David Goyder²  Conal Aitken³  Alexandre Antonelli⁴  Melanie-Jayne R. Howes⁵  Samuel Pironon⁶  James E. Richardson⁷  Colin W. Wright⁸ 

[1] Gothenburg Global Biodiversity Centre, Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden;Department of Biology, University of Fribourg, Fribourg, Switzerland;Swiss Institute of Bioinformatics, Fribourg, Switzerland;Royal Botanic Gardens, Kew, Richmond, United Kingdom;Royal Botanic Gardens, Kew, Richmond, United Kingdom;EaStCHEM, School of Chemistry, University of St Andrews, St Andrews, United Kingdom;Royal Botanic Gardens, Kew, Richmond, United Kingdom;Gothenburg Global Biodiversity Centre, Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden;Department of Biology, University of Oxford, Oxford, United Kingdom;Royal Botanic Gardens, Kew, Richmond, United Kingdom;Institute of Pharmaceutical Science, King’s College London, Franklin-Wilkins Building, London, United Kingdom;Royal Botanic Gardens, Kew, Richmond, United Kingdom;UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), Cambridge, United Kingdom;School of Biological, Earth and Environmental Sciences, University College Cork, Cork, Ireland;Tropical Diversity Section, Royal Botanic Garden, Edinburgh, United Kingdom;Departamento de Biología, Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia;Environmental Research Institute, University College Cork, Cork, Ireland;School of Pharmacy and Medical Sciences, University of Bradford, Bradford, United Kingdom;

关键词: malaria;    traditional and indigenous knowledge;    machine learning;    botany;    ethnobotany;    sampling bias;    antiplasmodial activity;    ethnopharmacology;   

DOI  :  10.3389/fpls.2023.1173328

 received in 2023-02-24, accepted in 2023-04-20,  发布年份 2023

来源: Frontiers

【 摘 要 】

Plants are a rich source of bioactive compounds and a number of plant-derived antiplasmodial compounds have been developed into pharmaceutical drugs for the prevention and treatment of malaria, a major public health challenge. However, identifying plants with antiplasmodial potential can be time-consuming and costly. One approach for selecting plants to investigate is based on ethnobotanical knowledge which, though having provided some major successes, is restricted to a relatively small group of plant species. Machine learning, incorporating ethnobotanical and plant trait data, provides a promising approach to improve the identification of antiplasmodial plants and accelerate the search for new plant-derived antiplasmodial compounds. In this paper we present a novel dataset on antiplasmodial activity for three flowering plant families – Apocynaceae, Loganiaceae and Rubiaceae (together comprising c. 21,100 species) – and demonstrate the ability of machine learning algorithms to predict the antiplasmodial potential of plant species. We evaluate the predictive capability of a variety of algorithms – Support Vector Machines, Logistic Regression, Gradient Boosted Trees and Bayesian Neural Networks – and compare these to two ethnobotanical selection approaches – based on usage as an antimalarial and general usage as a medicine. We evaluate the approaches using the given data and when the given samples are reweighted to correct for sampling biases. In both evaluation settings each of the machine learning models have a higher precision than the ethnobotanical approaches. In the bias-corrected scenario, the Support Vector classifier performs best – attaining a mean precision of 0.67 compared to the best performing ethnobotanical approach with a mean precision of 0.46. We also use the bias correction method and the Support Vector classifier to estimate the potential of plants to provide novel antiplasmodial compounds. We estimate that 7677 species in Apocynaceae, Loganiaceae and Rubiaceae warrant further investigation and that at least 1300 active antiplasmodial species are highly unlikely to be investigated by conventional approaches. While traditional and Indigenous knowledge remains vital to our understanding of people-plant relationships and an invaluable source of information, these results indicate a vast and relatively untapped source in the search for new plant-derived antiplasmodial compounds.

【 授权许可】

Unknown   
Copyright © 2023 Richard-Bollans, Aitken, Antonelli, Bitencourt, Goyder, Lucas, Ondo, Pérez-Escobar, Pironon, Richardson, Russell, Silvestro, Wright and Howes