Exploring Machine Learning Applications and Future Prospects in Drug Discovery

This study aims to provide a wide-ranging investigation of the ML-based drug discovery approach by discussing ML methods, results, and outcomes at the drug development process. The methodology covers the steps of data acquisition and preprocessing, feature engineering and selection, model selection and optimization, virtual screening of drugs, prediction of response to drugs, toxicity prediction, integration of multi-omics data, and validation and deployment of an ML model. The study shows superiority of ML-driven methods in terms of speeding up the candidate selection process, prediction of drug target relationships, toxicity prognosis, and integration of multiple molecular datasets for the discovery of new therapeutic targets and biomarkers. While the models might perform seriously in metrics and such, without the model interpretability, generalization, and ethics, there comes necessity to research more and to work together with various experts. A talk is given where a clear link between accurate delivering results, ethical management and regulations as well as a fully transparent reporting as can be done is given for responsible and fair ML technology development in drug discovery is made. If we cast our eye on the future, constant improvements and capital increases in ML based methods are set to achieve a fundamental breakthrough in drug development and individualized medicine and thus ultimately turn patients lives upside down for the better also global scale.