AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development
Chiranjib Chakraborty, Manojit Bhattacharya, Soumen Pal, Srijan Chatterjee, Arpita Das, Sang‐Soo Lee
Abstract
• AI-enabled LLMs have prospered and are used in science, medicine, and different realms of society. • Different LLMs are used in the multiple drug discovery and development stages. • LLMs used are de novo drug discovery, drug target identification and validation, ADME/ADMET, etc. • These LLMs help in faster and more cost-efficient drug discovery. Due to the recent revolution of artificial intelligence (AI), AI-enabled large language models (LLMs) have flourished and started to be applied in various sectors of science and medicine. Drug discovery and development are time-consuming, complex processes that require high investment. The conventional method of drug discovery is costly and has a high failure rate. AI-enabled LLMs are used in various steps of drug discovery to solve the challenges of time and cost. The article aims to provide a comprehensive understanding of AI-enabled LLMs and their use in various steps of drug discovery to ease the challenges. The review provides an overview of the LLM and their current state-of-the-art application in structure-based drug molecule design and de novo drug design. The different applications of AI-enabled LLMs have been illustrated, such as drug target identification, validation, interaction, and ADME/ADMET. Several domain-specific models of LLMs are developed in this direction and applied in drug discovery and development to speed up the process. We discussed all these domain-specific models of LLMs and their applications in this field. Finally, we illustrated the challenges and future perspectives on the applications of AI-enabled LLMs to drug discovery and development.