Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics

Title:Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics

Volume: 30 Issue: 28

Author(s): Priyanka Arora, Manaswini Behera, Shubhini A. Saraf and Rahul Shukla*

Affiliation:

• Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER)-Raebareli, Near CRPF Base Camp, Bijnor-Sisendi Road, Sarojini Nagar, Lucknow (UP)-226002, India

Abstract: Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis development by training the machines in multiple ways. AI-based drug development from molecule identification to clinical approval tremendously reduces the cost of development and the time over conventional methods. The COVID-19 vaccine development and approval by regulatory agencies within 1-2 years is the finest example of drug development. Hence, AI is fast becoming a boon for scientific researchers to streamline their advanced discoveries. AI-based FDA-approved nanomedicines perform well as target selective, synergistic therapies, recolonize the theragnostic pharmaceutical stream, and significantly improve drug research outcomes. This comprehensive review delves into the fundamental aspects of AI along with its applications in the realm of pharmaceutical life sciences. It explores AI's role in crucial areas such as drug designing, drug discovery and development, traditional Chinese medicine, integration of multi-omics data, as well as investigations into drug repurposing and polypharmacology studies.

Cite this article as:

Arora Priyanka, Behera Manaswini, Saraf A. Shubhini and Shukla Rahul*, Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics, Current Pharmaceutical Design 2024; 30 (28) . https://dx.doi.org/10.2174/0113816128308066240529121148