Abstract: Drug innovations have entered the battle of the host-pathogen evolutionary arms race by focusing on invading species. In the recent studies it has been observed that majority of the assorted drugs utilized in medications are being challenged from the pathogens. This is leading to the blooming of antimicrobial resistance or AMR. The emergence of drug-resistant microbes coupled with a lengthy and expensive pipeline for drug or antibiotic research, has sparked a critical interest in computational techniques that could hasten candidate discovery. Advances in AI have promoted its use in a variety of computer-aided drug design contexts, with an expanding utility in drug discovery. Along with the crucial prediction of antimicrobial activity, de novo molecular design, determination of drug-likeness features, and antimicrobial compound representation are also being fetched by employing AI. This review describes developments in the innovations of drugs, antibiotics and antimicrobial peptides, which have been made possible by AI. Further this investigation also examines the adoption of open scientific best practices in AI-driven drug innovations in the current urgency of the antimicrobial resistance challenges and makes the case for openness and reproducibility as a way to quicken preclinical research. It was observed in this study that numerous new corporate start-ups are eagerly approaching to AI based drug innovations. The conclusion of this investigation opined that artificial intelligence advancements in the drug innovation arena offer numerous prospects for future applications.

Keywords: Drugs, Innovation, discovery, Artificial Intelligence, Machine Learning, Antibiotics, Antimicrobial resistance, Pipeline.

| DOI: 10.17148/IARJSET.2022.91109