In
the drug discovery paradigm, the evaluation of absorption, distribution,
metabolism, and excretion (ADME) and toxicity properties of new chemical
entities is one of the most critical issues, which is a time-consuming
process, immensely expensive, and poses formidable challenges in pharmaceutical
R&D. In recent years, emerging technologies like artificial intelligence
(AI), big data, and cloud technologies have garnered great attention
to predict the ADME and toxicity of molecules. Currently, the blend
of quantum computation and machine learning has attracted considerable
attention in almost every field ranging from chemistry to biomedicine
and several engineering disciplines as well. Quantum computers have
the potential to bring advances in high-throughput experimental techniques
and in screening billions of molecules by reducing development costs
and time associated with the drug discovery process. Motivated by
the efficiency of quantum kernel methods, we proposed a quantum machine
learning (QML) framework consisting of a classical support vector
classifier algorithm with a kernel-based quantum classifier. To demonstrate
the feasibility of the proposed QML framework, the simplified molecular
input line entry system (SMILES) notation-based string kernel, combined
with a quantum support vector classifier, is used for the evaluation
of chemical/drug ADME-Tox properties. The proposed quantum machine
learning framework is validated and assessed via large-scale simulations.
Based on our results from numerical simulations, the quantum model
achieved the best performance as compared to classical counterparts
in terms of the area under the curve of the receiver operating characteristic
curve (AUC ROC; 0.80–0.95) for predicting outcomes on ADME-Tox
data sets for small molecules, with a different number of features.
The deployment of the proposed framework in the pharmaceutical industry
would be extremely valuable in making the best decisions possible.