MAIP: a web service for predicting blood‐stage malaria inhibitors

Journal of Cheminformatics

J. Mark F. Gardner¹  Jason Ochoada²  Anang A. Shelat²  Martin R. Saunders³  Darren V. S. Green³  Ricardo Arcila⁴  Andrew R. Leach⁴  David Mendez⁴  Nicolas Bosc⁴  Eloy Felix⁴  Ola Engkvist⁵  Preeti Iyer⁵  Jeremy Burrows⁶  James Duffy⁶  Eric J. Martin⁷  Andreas Verras⁸ 

[1] AMG Consultants Ltd, Discovery Park House, Discovery Park, Ramsgate Road, CT13 9ND, Sandwich, Kent, UK;Department of Chemical Biology and Therapeutics, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, 38105, Memphis, Tennessee, USA;Department of Molecular Design, Data and Computational Sciences, GlaxoSmithKline, Gunnels Wood Road, SG1 2NY, Stevenage, Hertfordshire, UK;European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, CB10 1SD, Hinxton, Cambridge, United Kingdom;Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden;Medicines for Malaria Ventures Discovery, 1215, Geneva, Switzerland;Novartis Institute for Biomedical Research, 5300 Chiron Way, 94608- 2916, Emeryville, California, USA;Schrodinger Inc, 120 West 45th Street, 10036-4041, New York, NY, USA;

关键词: Malaria;    Antimalarial drug discovery;    QSAR;    Classification modelling;    Open‐source software;    Naïve Bayes;    Machine learning;    Data fusion;   

DOI  :  10.1186/s13321-021-00487-2

来源: Springer

【 摘 要 】

Malaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify novel molecules that may have antimalarial properties. The performance of machine learning methods generally improves with the number of data points available for training. One practical challenge in building large training sets is that the data are often proprietary and cannot be straightforwardly integrated. Here, this was addressed by sharing QSAR models, each built on a private data set. We describe the development of an open-source software platform for creating such models, a comprehensive evaluation of methods to create a single consensus model and a web platform called MAIP available at https://www.ebi.ac.uk/chembl/maip/. MAIP is freely available for the wider community to make large-scale predictions of potential malaria inhibiting compounds. This project also highlights some of the practical challenges in reproducing published computational methods and the opportunities that open-source software can offer to the community.

【 授权许可】

CC BY