Researchers are exploring the use of quantum machine learning methods such as variational quantum circuits and kernel estimators. They are also working on standardizing data and merging duplicate parent compounds into single entries. For instance, researchers have proposed using Morgan fingerprints as a descriptor for SARS-CoV-2 data to generate binary numbers. However, the size of the Morgan fingerprints is 2048 bits, which exceeds the maximum capacity of quantum computing. To address this issue, a hybrid approach is being used to run a part of the code on a quantum computer and part on a classical computer, reducing the size of the data and lowering the dimension to 3 to 10 dimensions to consider the architecture of the qubit. The use of quantum machine learning algorithms such as SVM and MNN in drug discovery holds great promise. The proposed computer, called IBMs Rochester, which is a quantum computer with 33 qubits linked in a network, can run machine learning algorithms. For example, researchers used a neural network to predict the activity of compounds against the protein p38, a target for anti-inflammatory drugs. The neural network was trained on a dataset of over 200,000 compounds with known activities against the target and was able to predict the activity of new compounds with a high degree of accuracy. The researchers then used the algorithm to screen a database of over 6 million compounds and identified several new compounds with high predicted activities against p38alpha.