Machine Learning

Machine learning (ML) has emerged as a transformative force in computational biology, revolutionizing the way researchers analyze biological data. This powerful tool has facilitated predictive modeling, annotation of genomic sequences, and the identification of crucial biological insights. In this blog post, we will explore the various applications of ML in computational and systems biology, highlighting […]

The landscape of drug discovery has long been marked by challenges such as high costs, inefficiencies, and a high rate of candidate failures. However, recent years have witnessed a remarkable shift as artificial intelligence (AI) and machine learning technologies have begun to revolutionize the drug development process. Pioneering figures like Alex Zhavoronkov, CEO of Insilico

Quantitative structure-activity relationship (QSAR) is a method that uses mathematical models to predict the biological activity or physicochemical property of a molecule based on its structure. QSAR has been widely used in drug discovery, environmental toxicology, and chemical risk assessment for decades. However, traditional QSAR models often suffer from limitations such as low accuracy, poor

In the ever-evolving landscape of healthcare, the fusion of machine learning and drug discovery has emerged as a transformative force. One of the key frontiers in this collaboration is the analysis of drug-target interactions, a realm where machine learning is reshaping the way we identify and understand potential therapeutic opportunities. Deciphering Drug-Target Interactions: A Vital