Drug Classification using Machine Learning and Interpretability

Gala, Dhrumil Vinil; Gandhi, Vaibhav Bharat; Gandhi, Vedant; Sawant, Vinaya

doi:10.1109/stcr51658.2021.9588972

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Tornado plots are frequently used for XAI methods like SHAP and LIME. Several contributions on XAI applied to drug discovery used these kind of plots 106,107 …”

Section: Visualization Strategies Of Xai Methods and Drug Discoverymentioning

confidence: 99%

Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery

Ponzoni

Prosper²,

Campillo

2023

WIREs Comput Mol Sci

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Artificial intelligence (AI) is having a growing impact in many areas related to drug discovery. However, it is still critical for their adoption by the medicinal chemistry community to achieve models that, in addition to achieving high performance in their predictions, can be trusty explained to the end users in terms of their knowledge and background. Therefore, the investigation and development of explainable artificial intelligence (XAI) methods have become a key topic to address this challenge. For this reason, a comprehensive literature review about explanation methodologies for AI based models, focused in the field of drug discovery, is provided. In particular, an intuitive overview about each family of XAI approaches, such as those based on feature attribution, graph topologies, or counterfactual reasoning, oriented to a wide audience without a strong background in the AI discipline is introduced. As the main contribution, we propose a new taxonomy of the current XAI methods, which take into account specific issues related with the typical representations and computational problems study in the design of molecules. Additionally, we also present the main visualization strategies designed for supporting XAI approaches in the chemical domain. We conclude with key ideas about each method category, thoroughly providing insightful analysis about the guidelines and potential benefits of their adoption in medical chemistry.This article is categorized under: Data Science > Artificial Intelligence/Machine Learning

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“…Tornado plots are frequently used for XAI methods like SHAP and LIME. Several contributions on XAI applied to drug discovery used these kind of plots 106,107 …”

Section: Visualization Strategies Of Xai Methods and Drug Discoverymentioning

confidence: 99%

Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery

Ponzoni

Prosper²,

Campillo

2023

WIREs Comput Mol Sci

View full text Add to dashboard Cite

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“…It was found that recent papers can be classified into three types of databases, namely clinical [13], [18], [27], [33], registry [10], [12], [20], [24], [25], [29], [34], and knowledge [14], [15], [16], [17], [19], [21], [22], [23], [26], [28], [30], [31], [32], [35]. Additionally, based on review papers of existing XAI models [48], [49], the XAI algorithms used were divided into the following four categories: surrogate The main advantage of an explanatory technique such as SHAP is that it has solid roots in game theory, which ensures that the explanation of a prediction instance is fairly distributed across the features.…”

Section: ) Xai Methodsmentioning

confidence: 99%

“…Rapidly advancing AI technologies can facilitate data-driven estimation when screening multiple variables and capture non-linear relationships to accurately predict clinical outcomes. In recent years, many studies have been conducted to identify an optimal model for accurately predicting ADRs and taking effective preventive measures [17], [18], [25], [26], [27], [28].…”

Section: B Aspects Of Xai Use In Pharmacovigilance 1) Using Xai To Pr...mentioning

confidence: 99%

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Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

et al. 2023

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Explainable AI (XAI) is a methodology that complements the black box of artificial intelligence, and its necessity has recently been highlighted in various fields. The purpose of this research is to identify studies in the field of pharmacovigilance using XAI. Though there have been many previous attempts to select papers, with a total of 781 papers being confirmed, only 25 of them manually met the selection criteria. This study presents an intuitive review of the potential of XAI technologies in the field of pharmacovigilance. In the included studies, clinical data, registry data, and knowledge data were used to investigate drug treatment, side effects, and interaction studies based on tree models, neural network models, and graph models. Finally, key challenges for several research issues for the use of XAI in pharmacovigilance were identified. Although artificial intelligence (AI) is actively used in drug surveillance and patient safety, gathering adverse drug reaction information, extracting drug-drug interactions, and predicting effects, XAI is not normally utilized. Therefore, the potential challenges involved in its use alongside future prospects should be continuously discussed.

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