Artificial Intelligence in Drug Discovery and Development

Mak, Kit-Kay; Wong, Yi-Hang; Pichika, Mallikarjuna Rao

doi:10.1007/978-3-030-73317-9_92-1

Cited by 24 publications

(1 citation statement)

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“…Machine learning and deep learning algorithms can analyze huge amounts of data, identifying complex patterns and filtering candidate molecules based on desirable properties [ 8 ]. One of the most promising approaches is the computational design of new antibiotics [ 9 ], in which machine learning models analyze the molecular structures of existing antibiotics and their protein targets to identify potential modifications that can overcome bacterial resistance mechanisms [ 10 ]. In addition, AI can be used to identify new therapeutic targets by exploiting the growing genomic and transcriptional data on resistant bacteria.…”

Section: Introductionmentioning

confidence: 99%

Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future

Branda,

Scarpa

2024

Antibiotics

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Antibiotic resistance poses a significant threat to global public health due to complex interactions between bacterial genetic factors and external influences such as antibiotic misuse. Artificial intelligence (AI) offers innovative strategies to address this crisis. For example, AI can analyze genomic data to detect resistance markers early on, enabling early interventions. In addition, AI-powered decision support systems can optimize antibiotic use by recommending the most effective treatments based on patient data and local resistance patterns. AI can accelerate drug discovery by predicting the efficacy of new compounds and identifying potential antibacterial agents. Although progress has been made, challenges persist, including data quality, model interpretability, and real-world implementation. A multidisciplinary approach that integrates AI with other emerging technologies, such as synthetic biology and nanomedicine, could pave the way for effective prevention and mitigation of antimicrobial resistance, preserving the efficacy of antibiotics for future generations.

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Section: Introductionmentioning

confidence: 99%