Developing new drugs from marketed ones is a well-established and successful approach in drug discovery. We offer a unified view of this field, focusing on the new chemical aspects of the involved approaches: (a) chemical transformation of the origin...
Neurodegenerative diseases (NDs) pose serious healthcare challenges with limited therapeutic treatments and high social burdens. The integration of artificial intelligence (AI) into drug discovery has emerged as a promising approach to address these ...
Automatic eligibility criteria parsing in clinical trials is crucial for cohort recruitment leading to data validity and trial completion. Recent years have witnessed an explosion of powerful machine learning (ML) and natural language processing (NLP...
Deep generative models (GMs) have transformed the exploration of drug-like chemical space (CS) by generating novel molecules through complex, nontransparent processes, bypassing direct structural similarity. This review examines five key architecture...
This perspective paper explores the synergistic potential of blockchain and artificial intelligence (AI) in transforming healthcare. It begins with an overview of blockchain's role in healthcare data management, security, the pharmaceutical supply ch...
Digital therapeutics (DTx) is a recently conceived idea in health care that aims to cure ailments and modify patient behavior by employing a range of digital technologies. Notably, when traditional medication is not entirely efficacious, DTx offers a...
Cancer, a multifaceted and pernicious disease, continuously challenges medicine, requiring innovative treatments. Brain cancers pose unique and daunting challenges due to the intricacies of the central nervous system and the blood-brain barrier. In t...
In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our approach to complex data. This review explores the attention mechanism and its extended architectures, including graph attention networks (GATs), transform...
Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In th...