AI in Drug Discovery and Development: Review on Current Trends and Tools.

Abstract AI in drug development defines as the application of artificial intelligence (AI) technologies like deep learning (DL), machine learning (ML), and natural language processing (NLP) to enhance the process drug development by discovering, designing, testing, and launching new active drug to market. AI accelerates drug development by analysing vast biological, chemical, and clinical datasets to identify potential drug candidates. AI increases newly synthesized drug safety and efficacy and reduce its toxicity. This review aims to cover an overview of current tools, technologies, and trends in AI for drug discovery and development and introduce the power of AI in a new era for drug discovery and development, accelerated research functioning, by powering new tools. Simplifying new drug discovery by explaining AI powered tools in each stage of drug development. As the field continues to evolve, ongoing collaboration between scientists, technologists, and regulatory bodies will be essential to harness these advances for the benefit of global health.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-25
DOI
https://doi.org/10.5281/zenodo.15711109
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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article

AI in Drug Discovery and Development: Review on Current Trends and Tools.

Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
article

AI in Drug Discovery and Development: Review on Current Trends and Tools.

article en

Abstract

Abstract AI in drug development defines as the application of artificial intelligence (AI) technologies like deep learning (DL), machine learning (ML), and natural language processing (NLP) to enhance the process drug development by discovering, designing, testing, and launching new active drug to market. AI accelerates drug development by analysing vast biological, chemical, and clinical datasets to identify potential drug candidates. AI increases newly synthesized drug safety and efficacy and reduce its toxicity. This review aims to cover an overview of current tools, technologies, and trends in AI for drug discovery and development and introduce the power of AI in a new era for drug discovery and development, accelerated research functioning, by powering new tools. Simplifying new drug discovery by explaining AI powered tools in each stage of drug development. As the field continues to evolve, ongoing collaboration between scientists, technologists, and regulatory bodies will be essential to harness these advances for the benefit of global health.

Zenodo (CERN European Organization for Nuclear Research)
Shri Vile Parle Kelavani Mandal (IN)
Openalex Percentile: Top 51%
Computational Drug Discovery Methods
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AI in Drug Discovery and Development: Review on Current Trends and Tools. · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS