Lstm-driven drug design using selfies for target-focused de novo generation of hiv-1 protease inhibitor candidates for aids treatment

Albrijawi, M. Taleb; Alhajj, Reda

Lstm-driven drug design using selfies for target-focused de novo generation of hiv-1 protease inhibitor candidates for aids treatment

dc.authorid	0000-0001-6657-9738
dc.contributor.author	Albrijawi, M. Taleb
dc.contributor.author	Alhajj, Reda
dc.date.accessioned	2024-07-02T06:59:47Z
dc.date.available	2024-07-02T06:59:47Z
dc.date.issued	2024
dc.department	İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstract	The battle against viral drug resistance highlights the need for innovative approaches to replace time-consuming and costly traditional methods. Deep generative models offer automation potential, especially in the fight against Human immunodeficiency virus (HIV), as they can synthesize diverse molecules effectively. In this paper, an application of an LSTM-based deep generative model named "LSTM-ProGen"is proposed to be tailored explicitly for the de novo design of drug candidate molecules that interact with a specific target protein (HIV-1 protease). LSTM-ProGen distinguishes itself by employing a longshort- term memory (LSTM) architecture, to generate novel molecules target specificity against the HIV-1 protease. Following a thorough training process involves fine-tuning LSTM-ProGen on a diverse range of compounds sourced from the ChEMBL database. The model was optimized to meet specific requirements, with multiple iterations to enhance its predictive capabilities and ensure it generates molecules that exhibit favorable target interactions. The training process encompasses an array of performance evaluation metrics, such as drug-likeness properties. Our evaluation includes extensive silico analysis using molecular docking and PCA-based visualization to explore the chemical space that the new molecules cover compared to those in the training set. These evaluations reveal that a subset of 12 de novo molecules generated by LSTM-ProGen exhibit a striking ability to interact with the target protein, rivaling or even surpassing the efficacy of native ligands. Extended versions with further refinement of LSTM-ProGen hold promise as versatile tools for designing efficacious and customized drug candidates tailored to specific targets, thus accelerating drug development and facilitating the discovery of new therapies for various diseases.
dc.identifier.citation	Albrijawi, M. T. ve Alhajj, R. (2024). Lstm-driven drug design using selfies for target-focused de novo generation of hiv-1 protease inhibitor candidates for aids treatment. PLoS ONE, 19(6). http://dx.doi.org/10.1371/journal.pone.0303597
dc.identifier.doi	10.1371/journal.pone.0303597
dc.identifier.issn	1932-6203
dc.identifier.issue	6
dc.identifier.pmid	38905197
dc.identifier.scopus	2-s2.0-85196856217
dc.identifier.scopusquality	Q1
dc.identifier.uri	http://dx.doi.org/10.1371/journal.pone.0303597
dc.identifier.uri	https://hdl.handle.net/20.500.12511/12684
dc.identifier.volume	19
dc.identifier.wos	001262702900030	en_US
dc.identifier.wosquality	Q1
dc.indekslendigikaynak	Web of Science
dc.indekslendigikaynak	Scopus
dc.indekslendigikaynak	PubMed
dc.institutionauthor	Albrijawi, M. Taleb
dc.institutionauthor	Alhajj, Reda
dc.language.iso	en
dc.relation.ispartof	PLoS ONE	en_US
dc.relation.publicationcategory	Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rights	Attribution 4.0 International	*
dc.rights	info:eu-repo/semantics/openAccess
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject	Acquired Immunodeficiency Syndrome
dc.subject	Drug Design
dc.subject	HIV Protease
dc.subject	HIV Protease Inhibitors
dc.subject	HIV-1
dc.subject	Humans
dc.subject	Molecular Docking Simulation
dc.title	Lstm-driven drug design using selfies for target-focused de novo generation of hiv-1 protease inhibitor candidates for aids treatment
dc.type	Article