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All Journal JIMKI: Jurnal Ilmiah Mahasiswa Kedokteran Indonesia
Nurfajrianty Jamaluddin
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Health Professions Medicine & Pharmacology Public Health

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AI-Powered De Novo Antibiotics Discovery: Is It The Answer to Overcome Antimicrobial Resistance? A Systematic Review of Preclinical Evidence Across In Vitro and In Vivo Studies Nabilah Nurul Iftitah; Nurfajrianty Jamaluddin; Alia Zhafira Agus
JIMKI: Jurnal Ilmiah Mahasiswa Kedokteran Indonesia Book of Abstrack RCIMS 2025
Publisher : BAPIN-ISMKI (Badan Analisis Pengembangan Ilmiah Nasional - Ikatan Senat Mahasiswa Kedokteran Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53366/jimki.vi.933

Abstract

Introduction. Antimicrobial resistance (AMR) remains a critical global issue. By 2050, it is projected to cause around 10 million deaths if current trends persist. Traditional antimicrobial discovery struggles to keep up with rapidly evolving resistance  due to its lengthy process, high cost, and high failure rate. Developing a single drug can take over a decade of research and cost millions of dollars. These challenges demand more efficient approaches, with artificial intelligence (AI) offering a promising path to accelerate and improve antibiotic development. Methods. GoogleScholar, PubMed, ScienceDirect, and Scopus were systematically searched following the PRISMA 2020, yielding 13 eligible studies. All included in vitro validation, and four extended to in vivo investigations. Risk of bias was evaluated using the QUIN (in vitro) and the SYRCLE (in vivo) tools. Result and Discussions. Across studies, AI supported multiple stages of antibiotic discovery, including target identification, lead compound optimization, also enhancement of pre-clinical testing. In target identification, two studies revealed novel antibacterial targets distinct from classical pathway. During lead optimization, applied in most studies, AI-generated compounds demonstrated strong antimicrobial activity and low MIC values against broad-spectrum and multi-drug resistant bacteria. Four in vivo studies further showed that these de novo antibiotics exhibited superior antimicrobial efficacy to current standard therapies. Finally, in preclinical testing, AI models accurately predicted cytotoxicity and hemolysis, later confirmed experimentally. Conclusions. AI has markedly improved efficiency and accuracy in antibiotic development. While continued model refinement, validation, and ethical oversight remain crucial, AI-intgerated pharmaceutical research indicates growing maturity and transformative potential.
AI-Powered De Novo Antibiotics Discovery: Is It The Answer to Overcome Antimicrobial Resistance? A Systematic Review of Preclinical Evidence Across In Vitro and In Vivo Studies Nabilah Nurul Iftitah; Nurfajrianty Jamaluddin; Alia Zhafira Agus
JIMKI: Jurnal Ilmiah Mahasiswa Kedokteran Indonesia Vol 12 No 2 (2025): JIMKI: Jurnal Ilmiah Mahasiswa Kedokteran Indonesia Vol. 12.2 (2025)
Publisher : BAPIN-ISMKI (Badan Analisis Pengembangan Ilmiah Nasional - Ikatan Senat Mahasiswa Kedokteran Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53366/jimki.vi.1046

Abstract

Introduction: Antimicrobial resistance (AMR) remains a critical global issue. By 2050, it is projected to cause around 10 million deaths if current trends persist. Traditional antimicrobial discovery struggles to keep up with rapidly evolving resistance  due to its lengthy process, high cost, and high failure rate. Developing a single drug can take over a decade of research and cost millions of dollars. These challenges demand more efficient approaches, with artificial intelligence (AI) offering a promising path to accelerate and improve antibiotic development. Methods: GoogleScholar, PubMed, ScienceDirect, and Scopus were systematically searched following the PRISMA 2020, yielding 13 eligible studies. All included in vitro validation, and four extended to in vivo investigations. Risk of bias was evaluated using the QUIN (in vitro) and the SYRCLE (in vivo) tools. Discussion: Across studies, AI supported multiple stages of antibiotic discovery, including target identification, lead compound optimization, also enhancement of pre-clinical testing. In target identification, two studies revealed novel antibacterial targets distinct from classical pathways. During lead optimization, applied in most studies, AI-generated compounds demonstrated strong antimicrobial activity and low MIC values against broad-spectrum and multi-drug resistant bacteria. Four in vivo studies further showed that these de novo antibiotics exhibited superior antimicrobial efficacy to current standard therapies. Finally, in preclinical testing, AI models accurately predicted cytotoxicity and hemolysis, later confirmed experimentally. Conclusion: AI has markedly improved efficiency and accuracy in antibiotic development. While continued model refinement, validation, and ethical oversight remain crucial, AI-integrated pharmaceutical research indicates growing maturity and transformative potential.
Co-Authors Alia Zhafira Agus Nabilah Nurul Iftitah