eprintid: 10183018 rev_number: 8 eprint_status: archive userid: 699 dir: disk0/10/18/30/18 datestamp: 2023-12-05 08:38:44 lastmod: 2023-12-05 08:38:44 status_changed: 2023-12-05 08:38:44 type: article metadata_visibility: show sword_depositor: 699 creators_name: Li, Yutong creators_name: Cardoso-Silva, Jonathan creators_name: Kelly, John M creators_name: Delves, Michael J creators_name: Furnham, Nicholas creators_name: Papageorgiou, Lazaros G creators_name: Tsoka, Sophia title: Optimisation-based modelling for explainable lead discovery in malaria ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F43 keywords: Quantitative Structure–Activity Relationship (QSAR); Mathematical optimisation; Piecewise linear regression; Drug discovery; Malaria; Machine learning note: Copyright © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). abstract: Background: The search for new antimalarial treatments is urgent due to growing resistance to existing therapies. The Open Source Malaria (OSM) project offers a promising starting point, having extensively screened various compounds for their effectiveness. Further analysis of the chemical space surrounding these compounds could provide the means for innovative drugs. // Methods: We report an optimisation-based method for quantitative structure–activity relationship (QSAR) modelling that provides explainable modelling of ligand activity through a mathematical programming formulation. The methodology is based on piecewise regression principles and offers optimal detection of breakpoint features, efficient allocation of samples into distinct sub-groups based on breakpoint feature values, and insightful regression coefficients. Analysis of OSM antimalarial compounds yields interpretable results through rules generated by the model that reflect the contribution of individual fingerprint fragments in ligand activity prediction. Using knowledge of fragment prioritisation and screening of commercially available compound libraries, potential lead compounds for antimalarials are identified and evaluated experimentally via a Plasmodium falciparum asexual growth inhibition assay (PfGIA) and a human cell cytotoxicity assay. // Conclusions: Three compounds are identified as potential leads for antimalarials using the methodology described above. This work illustrates how explainable predictive models based on mathematical optimisation can pave the way towards more efficient fragment-based lead discovery as applied in malaria. date: 2024-01 date_type: published publisher: Elsevier BV official_url: https://doi.org/10.1016/j.artmed.2023.102700 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2114261 doi: 10.1016/j.artmed.2023.102700 lyricists_name: Papageorgiou, Lazaros lyricists_id: LPAPA33 actors_name: Flynn, Bernadette actors_id: BFFLY94 actors_role: owner full_text_status: public publication: Artificial Intelligence in Medicine volume: 147 article_number: 102700 issn: 0933-3657 citation: Li, Yutong; Cardoso-Silva, Jonathan; Kelly, John M; Delves, Michael J; Furnham, Nicholas; Papageorgiou, Lazaros G; Tsoka, Sophia; (2024) Optimisation-based modelling for explainable lead discovery in malaria. Artificial Intelligence in Medicine , 147 , Article 102700. 10.1016/j.artmed.2023.102700 <https://doi.org/10.1016/j.artmed.2023.102700>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10183018/1/1-s2.0-S0933365723002142-main.pdf