How machine learning algorithm is used in drug discovery?
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Machine learning algorithms are used in drug discovery to enhance decision-making and reduce costs and research times. These algorithms are applied in various stages of the drug discovery process, including data analysis, predictive modeling, and virtual screening. By utilizing machine learning techniques, researchers can analyze large volumes of biological and medical data to identify potential drug candidates with specific chemical properties. Machine learning models can be used to predict the activity of a drug, develop new drugs, and automate repetitive data processing and analysis tasks . In particular, machine learning has been used in the discovery of inhibitors for specific kinases, which play vital roles in various human diseases . Machine learning methods have also been employed in virtual screening approaches to identify new structures with potential high affinity for specific receptors, such as the 5-HT6 receptor .
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| Papers (5) | Insight |
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| The paper describes the use of machine learning algorithms in drug discovery, specifically in the search for new ligands of the 5-HT6 receptor. The algorithms are used in a sequential virtual screening approach, combining ligand-based and structure-based drug discovery methods. | |
| Machine learning algorithms are used in drug discovery for target identification, data analysis in clinical trials, and predicting drug molecular structures. | |
05 May 2023 | Machine learning algorithms are used in drug discovery to develop predictive models that reduce expenses and research times by analyzing molecular data and addressing biological problems. |
| Machine learning algorithms are used in drug discovery to predict the activity of drugs, develop new drugs, and analyze large volumes of data to decrease costs and research times. | |
| Machine learning algorithms are used in drug discovery to automate repetitive data processing and analysis processes, reducing complexity and cost. |
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