Protein-protein docking is the process of predicting the 3D structure of a protein complex formed by the interaction of two or more proteins. It is a challenging problem in computational biology, as it requires modelling the molecular interactions, conformational changes, and dynamics of the proteins involved. Protein-protein docking has many applications in drug discovery, biotechnology, […]
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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
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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
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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
QSAR and ML: A Powerful Combination for Drug Discovery Read More »
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
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Metabolomics is the study of small molecules (metabolites) that are involved in the biochemical processes of living organisms. Metabolites are the end products of gene expression and can reflect the physiological state of a cell or a tissue under different conditions. Metabolomics can provide insights into the molecular mechanisms of health and disease, as well
Small molecule drugs are compounds that can modulate the activity of biological targets, such as proteins, enzymes, or receptors, and have the potential to treat various diseases. These are the most common type of therapeutics, accounting for about 90% of the approved drugs on the market. However, discovering and developing new small molecule drugs is
How Machine Learning Can Accelerate Small Molecule Drug Discovery Read More »
The field of bioinformatics has revolutionized our understanding of biology by harnessing the power of computational methods to analyze and interpret vast amounts of biological data. In recent years, machine learning (ML) has emerged as a transformative tool in bioinformatics, enabling researchers to make groundbreaking discoveries and address complex challenges in areas such as genomics,
Power of Biological Data: The Role of Machine Learning in Bioinformatics Read More »
The route of drug development is analogous to traversing a difficult maze with high risks. Traditional procedures are both time-consuming and expensive, with frequently low success rates. According to a recent research, the average cost of producing a new medication is $2.6 billion, with fewer than 14% making it through the arduous approval procedure. Scientists
Computer Aided Drug Design (CADD) and Machine Learning: Future of Drug Discovery Read More »
