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Decoding toxicological signatures through quantum computational paradigm
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AI
DOI：10.1007/s11082-023-06079-8.png)
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Abstract
En 中文
In this day and age, there has been a discernible focus on and significant usage of Quantum machine learning (QML) models with the intention of predicting the toxicity of inconsequential compounds. The application of computational toxicity prediction provides considerable advantages in the early phases of pharmaceutical research. These benefits include the identification and removal of compounds that are likely to display poor effectiveness when tested in clinical trials. This trend has been easier to observe with the introduction of extensive toxicity databases. As a result of the fact that this field is still in its infancy, it is essential to acquire a more all-encompassing grasp of the range of QAI approaches and the contexts in which they might be used. Trials to harmonize principles from quantum mechanics, Quantum Machine learning algorithms with classical ML techniques, which leads towards enhancing the interpretability has been performed. In an attempt to achieve robust accuracy associated with the QML model, reach out of enriching insights from naive Bayesian classification and recursive partitioning, through the merge of sophisticated computational techniques with complex biological phenomenon has been done, the present study not only moves towards enhancing the repertoire of tools available for early stage enhanced drug discovery & optimization but also moves towards revolutionary possibilities of quantum-infused methods in tackling persistent issues in the field of bioinformatics and toxicology.
Keywords:
Quantum AI
Toxicology
Computational analysis
Pharmacokinetics
Bioinformatics
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