AI in drug discovery and its clinical relevance

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.  Other InformationPublished in:HeliyonLicense: https://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://doi.org/10.1016/j.heliyon.2023.e17575 </p

Artificial Intelligence and Biosensors in Healthcare and its Clinical Relevance: A Review

Data generated from sources such as wearable sensors, medical imaging, personal health records, pathology records, and public health organizations have resulted in a  massive information increase in the medical sciences over the last decade. Advances in computational hardware, such as cloud computing, Graphical Processing Units (GPUs), and Tensor  Processing Units (TPUs), provide the means to utilize these data.  Consequently, many Artificial Intelligence (AI)-based methods have been developed to infer from large healthcare data. Here,  we present an overview of recent progress in artificial intelligence  and biosensors in medical and life sciences. We discuss the role  of machine learning in medical imaging, precision medicine,  and biosensors for the Internet of Things (IoT). We review the  most recent advancements in wearable biosensing technologies  that use AI to assist in monitoring bodily electro-physiological  and electro-chemical signals and disease diagnosis, demonstrating  the trend towards personalized medicine with highly effective, inexpensive, and precise point-of-care treatment. Furthermore,  an overview of the advances in computing technologies, such as  accelerated artificial intelligence, edge computing, and federated  learning for medical data, are also documented. Finally, we investigate challenges in data-driven AI approaches, the potential  issues that biosensors and IoT-based healthcare generate, and the distribution shifts that occur among different data modalities,  concluding with an overview of future prospects </p