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.
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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.
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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