Development of Machine Learning Platforms that Accelerate Drug Discovery and Molecular Screening for Cancers, Autoimmune Diseases, And Neurological Disorders

Abstract

The emergence of machine learning (ML) systems has gone a long way in advancing drug discovery, especially in the face of complex diseases including cancers, autoimmune diseases and neurological diseases. The combination of ML algorithms and genomic, proteomic, and clinical data has a tremendous potential to be used in predicting the therapeutic targets, in accelerating the drug screening process, and in improving the molecular interactions. This paper will discuss some of the ML methods, such as deep learning, reinforcement learning, and generative models, in drug discovery and molecular screening of these diseases. Moreover, the article raises the problems related to the implementation of ML to large-scale molecular data, the ethical issues of the implementation, and the necessity of the explainability of the ML models. Cases of MLs used in oncology, autoimmune disorders, and neurodegenerative diseases are described to demonstrate how the technologies have been used to aid in the early diagnosis of diseases, repurposing of drugs, and the creation of precision medicine. The combination of quantum computing, blockchain, and AI-based platforms are also discussed and their transformative power that should be used to ensure healthcare data security and enhance the efficiency of drug discovery pipelines. The article ends by stating that interdisciplinary work and data-sharing models are vital to the achievement of the full potential of ML in healthcare innovation.