Explainable artificial intelligence-assisted virtual screening and bioinformatics approaches for effective bioactivity prediction of phenolic cyclooxygenase-2 (COX-2) inhibitors using PubChem molecular fingerprints

Rudrapal, Mithun; Kirboga, Kevser Kübra; Abdalla, Mohnad; Maji, Siddhartha

doi:10.1007/s11030-023-10782-9

Cited by 5 publications

(2 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…16 Another recent study highlights the utility of AI in natural product-based drug research. 17 In this study, explainable artificial intelligence, in which the user retains some intellectual oversight, was used to predict the bioactivity of COX-2 inhibitors, which are nonsteroidal anti-inflammatory drug inhibitors, and scan a database of natural products (CHEMBL) to find potential drug candidates. Molecular fingerprint analysis of the scanned compounds was performed, and bioinformatics was used to analyze structural properties of effective phenolic COX-2 inhibitors.…”

Section: Ai In Biomedical Researchmentioning

confidence: 99%

Exploring the Promise and Challenges of Artificial Intelligence in Biomedical Research and Clinical Practice

Altara,

Basson,

Biondi-Zoccai

et al. 2024

Journal of Cardiovascular Pharmacology

View full text Add to dashboard Cite

Artificial intelligence (AI) is poised to revolutionize how science, and biomedical research in particular, are done. With AI, problem solving and complex tasks using massive data sets can be performed at a much higher rate and dimensionality level compared to humans. With the ability to handle huge data sets and self-learn, AI is already being exploited in drug design, drug repurposing, toxicology, and material identification. AI could also be used in both basic and clinical research in study design, defining outcomes, analyzing data, interpreting findings, and even identifying the most appropriate areas of investigation and funding sources. State-of-the-art AI-based large language models (LLM), such as ChatGPT and Perplexity, are positioned to change forever how science is communicated and how scientists interact with one another and their profession, including post-publication appraisal and critique. Like all revolutions, upheaval will follow and not all outcomes can be predicted, necessitating guardrails at the onset, especially to minimize the untoward impact of the many drawbacks of LLMs, which include lack of confidentiality, risk of hallucinations, and propagation of mainstream albeit potentially mistaken opinions and perspectives. In this review, we highlight areas of biomedical research that are already being reshaped by AI and how AI is likely to impact it further in the near future. We discuss the potential benefits of AI in biomedical research and address possible risks, some surrounding the creative process, that warrant further reflection.

show abstract

Section: Ai In Biomedical Researchmentioning

confidence: 99%

Exploring the Promise and Challenges of Artificial Intelligence in Biomedical Research and Clinical Practice

Altara,

Basson,

Biondi-Zoccai

et al. 2024

Journal of Cardiovascular Pharmacology

View full text Add to dashboard Cite

show abstract

“…Virtual AI applications aid healthcare professionals in diagnosing diseases more accurately and efficiently. AI algorithms can analyze medical imaging data, such as X-rays, While the specific focus on AI-assisted material discovery in drug delivery is not extensively covered in the literature, the literature highlights the potential of AI in advancing pharmaceutical product development, including formulation design and drug discovery [2,3]. These insights suggest that AI can be a valuable tool in accelerating the discovery and optimization of materials for drug delivery.…”

Section: Ai In Discovering New Drugsmentioning

confidence: 99%

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

Visan,

Negut

2024

Life

View full text Add to dashboard Cite

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.

show abstract

Mechanism of Lian Hua Qing Wen capsules regulates the inflammatory response caused by M1 macrophage based on cellular experiments and computer simulations

Zong,

Cao,

Zhao

et al. 2024

Acta Tropica

View full text Add to dashboard Cite

Explainable artificial intelligence-assisted virtual screening and bioinformatics approaches for effective bioactivity prediction of phenolic cyclooxygenase-2 (COX-2) inhibitors using PubChem molecular fingerprints

Cited by 5 publications

References 87 publications

Exploring the Promise and Challenges of Artificial Intelligence in Biomedical Research and Clinical Practice

Exploring the Promise and Challenges of Artificial Intelligence in Biomedical Research and Clinical Practice

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

Mechanism of Lian Hua Qing Wen capsules regulates the inflammatory response caused by M1 macrophage based on cellular experiments and computer simulations

Contact Info

Product

Resources

About