Application of Explainable Artificial Intelligence in Drug Discovery and Drug Design

Le résumé fourni par la source

Drug discovery is the process of introducing a novel drug molecule into medical practice. Drug discovery is a very costly and time-consuming process, and that is why initiatives that contribute to facilitating and accelerating the drug discovery process are of major interest. Artificial intelligence is the investigation of complicated medical data utilizing powerful algorithms and software to replicate human cognition and investigate the relationships between preventive or curative interventions and health outcomes. In recent years, several artificial intelligence (AI) approaches have been effectively used for computer-assisted drug discovery like deep learning (DL), machine learning (ML), and neural networks (NNs). Explainable artificial intelligence (XAI) makes an effort to help researchers comprehend how the model came to a certain conclusion and provide reasons for why the model’s response is reasonable. To make the decision-making process transparent, 214 XAI also offers thorough explanations in addition to the mathematical models. In this chapter, we have outlined the most important artificial intelligence approaches that aid in drug discovery. We have discussed the uses, prospects, and limitations of XAI.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.