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@article{170834,
        author = {SHARDA CHHABRIA and VEDANT DONGRE and SUSHANT PANDEY and VINIT CHOUBEY and SHUBHAM SHARMA and SWARAJ DESHMUKH and SOHAM CHOUDHARI},
        title = {Review of Machine Learning Techniques For  Drug Discovery},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {730-732},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170834},
        abstract = {The field of drug discovery has witnessed transformative changes with the integration of Machine Learning (ML) techniques. Traditional methods of identifying potential therapeutic compounds are time-intensive and resource-heavy, often involving large-scale experimental testing. In contrast, ML offers a computational approach that 
accelerates and enhances the drug discovery process by predicting bioactivity, analyzing molecular interactions, and generating novel compounds. This paper reviews the evolution, applications, and challenges of ML in drug discovery, focusing on techniques such as Quantitative Structure-Activity Relationship (QSAR) models, virtual screening, and deep learning, as highlighted in recent studies.},
        keywords = {},
        month = {December},
        }