The Role of AI in Drug Discovery

Abstract

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

AI-assisted chemistry research: a comprehensive analysis of evolutionary paths and hotspots through knowledge graphs

“…At the same time, the scope of AI applications in early drug discovery, such as designing compounds and peptides from scratch and synthetic planning, has expanded considerably. 87,88 For example, Segler et al 89 employed recurrent neural networks as generative models to design novel drug molecules. By fine-tuning with small sets of active molecules, the model effectively targets specific biological threats, achieving 14% reproduction against Staphylococcus aureus and 28% against Plasmodium falciparum .…”