Drug Screening and Target Prediction Based on Machine Learning
DOI:
https://doi.org/10.53469/wjimt.2024.07(02).05Keywords:
Machine learning, Drug targeting, Predicts drug screening, Support vector machines (SVM)Abstract
With the development of artificial intelligence information technology in biomedicine, the traditional practice of drug development is gradually replaced by machine learning. In drug development, drug screening and target protein identification are the keys to modern drug development. However, the traditional comparative experiments are limited by the high throughput, low precision and high cost of biological experimental methods, and the screening and prediction of a large number of drug targets have a certain degree of blindness, which is difficult to be widely carried out in practical applications. In contrast, machine learning has powerful intelligent information processing technology capabilities such as data mining and mathematical statistics. By simulating and predicting the interaction between drugs and targets through machine learning, it can reduce research and development costs, shorten the time of new drug development, and reduce the blindness of new drug development, which is of great significance for the development of new drugs and the improvement of human medical level. In this paper, a powerful deep learning classification model (SVM) is proposed to predict drug-target interactions, achieve efficient prediction of multi-source and high-dimensional data, and make great progress in reducing research and development costs, shortening research and development cycles, and improving experimental accuracy.
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