AccScience Publishing / AIH / Online First / DOI: 10.36922/aih.4423
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PERSPECTIVE ARTICLE

Revolutionizing new drug discovery: Harnessing AI and machine learning to overcome traditional challenges and accelerate targeted therapies

Satinder Singh1* ,  Vyas Shingatgeri2 ,  Pratima Srivastava1
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1 Drug Metabolism and Pharmacokinetics, Aragen Life Sciences Limited, Hyderabad, Telangana, India
2 Dean, School of Biosciences, Apeejay Stya University, Gurugram, Haryana, India
AIH, 4423 https://doi.org/10.36922/aih.4423
Submitted: 2 August 2024 | Accepted: 8 October 2024 | Published: 6 November 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Designing highly targeted, selective drugs with desirable absorption, distribution, metabolism, excretion, and pharmacokinetic (PK) profiles; single-digit nanomolar efficacy; and a wider therapeutic index are challenging. In the traditional drug discovery process, researchers screen thousands of chemical compounds during pre-clinical development, progressing through hit identification, lead optimization, and candidate selection to shortlist – potential clinical candidates with favorable PK profiles, high tolerability, and manageable toxicity. The selected candidate must demonstrate sufficient efficacy in treating the target disease in humans. Despite these efforts, the success rate of the pre-clinical candidate to sail through Phase I, Phase II, and Phase III in clinical trials remains exceedingly low. Supported by powerful data-driven tools, artificial intelligence (AI) has transformed this traditional drug discovery process by enabling the analysis of large quantities of omics, phenotypic, and expression data to identify the biological mechanisms of pathological conditions and in turn identify druggable proteins or genes. The generative AI-powered toolbox creates novel compounds from scratch, assists scientists in optimizing druggability attributes, and bridges the differences between animal and human physiology and anatomy to predict potential toxicity in humans with high accuracy. This review discusses the bottlenecks in the traditional drug discovery approach, the impact of AI and machine learning (ML) in drug discovery, and potential challenges associated with AI/ML adoption.

Keywords
Novel chemical entity
Absorption
distribution
metabolism and excretion
Pharmacokinetics
Artificial intelligence
Machine learning
Deep learning
Generative AI
Drug discovery and development
Funding
None.
Conflict of interest
Satinder Singh and Pratima Srivastava are employees in DMPK, Aragen Life Sciences Limited, Hyderabad, India. The other author declares no conflict of interest. The authors declare that they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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