Recent advances and application of generative adversarial networks in drug discovery, development, and targeting

Tripathi, Satvik; Augustin, Alisha Isabelle; Dunlop, Adam; Sukumaran, Rithvik; Dheer, Suhani; Zavalny, Alex; Haslam, Owen; Austin, Thomas; Donchez, Jacob; Tripathi, Pushpendra Kumar; Kim, Edward

doi:10.1016/j.ailsci.2022.100045

Cited by 21 publications

(6 citation statements)

References 99 publications

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“…The recent summary of GANs by Tripathi et al is worth reading and analyzing ( Tripathi et al, 2022 ). In this review, the authors looked at research on drug development that uses several GAN methods to assess molecular de novo design.…”

Section: Generative Ai Modelsmentioning

confidence: 99%

Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities

Gangwal,

Ansari,

Ahmad

et al. 2024

Front. Pharmacol.

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There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.

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Section: Generative Ai Modelsmentioning

confidence: 99%

Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities

Gangwal,

Ansari,

Ahmad

et al. 2024

Front. Pharmacol.

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“…It should be noted that GANs can augment small datasets of known active compounds, enabling better generalization and improving the performance of predictive models by generating additional data samples [88]. Also, GANs enhance the efficiency and accuracy of the ML models used in drug discovery [96].…”

Section: (V) Text Miningmentioning

confidence: 99%

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Visan,

Negut

2024

Life

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Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI’s role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.

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“…Protecting data privacy, obtaining informed permission, and maintaining openness throughout model creation and decision‐making are all key aspects that need to be carefully controlled (Peris et al., 2023; Tripathi et al., 2022). In addition, in order to minimize the perpetuation of disadvantages and inequalities in healthcare, it is vital to ensure fair representation, address biases, and promote inclusion in model training and implementation.…”

Section: Ethics and Considerationsmentioning

confidence: 99%

Large language models reshaping molecular biology and drug development

Tripathi,

Gabriel,

Tripathi

et al. 2024

Chem Biol Drug Des

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The utilization of large language models (LLMs) has become a significant advancement in the domains of medicine and clinical informatics, providing a revolutionary potential for scientific breakthroughs and customized therapies. LLM models are trained on large datasets and exhibit the capacity to comprehend and analyze intricate biological data, encompassing genomic sequences, protein structures, and clinical health records. With the utilization of their comprehension of the language of biology, they possess the ability to reveal concealed patterns and insights that may evade human researchers. LLMs have been shown to positively impact various aspects of molecular biology, including the following: genomic analysis, drug development, precision medicine, biomarker development, experimental design, collaborative research, and accessibility to specialized expertise. However, it is imperative to acknowledge and tackle the obstacles and ethical implications involved. The careful consideration of data bias and generalization, data privacy and security, explainability and interpretability, and ethical concerns around responsible application is vital. The successful resolution of these obstacles will enable us to fully utilize the capabilities of LLMs, leading to substantial progress in the fields of molecular biology and pharmaceutical research. This progression also has the ability to bolster influential impacts for both the individual and the broader community.

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Recent advances and application of generative adversarial networks in drug discovery, development, and targeting

Cited by 21 publications

References 99 publications

Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities

Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Large language models reshaping molecular biology and drug development

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