Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework

Nascimben, Mauro; Rimondini, Lia

doi:10.3390/molecules28031342

Cited by 2 publications

(1 citation statement)

References 78 publications

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“…Due to its capacity to expedite the identification and development of new medications, artificial intelligence (AI) has grown in significance in the field of drug discovery. Numerous compounds must be tested in the lab using traditional drug screening techniques, which can be time-and resource-consuming [8]. By analysing huge databases of chemical compounds and making predictions about their properties and probable biological functions, AI can help in virtual screening.…”

Section: Drug Discovery and Identificationmentioning

confidence: 99%

Employing Artificial Intelligence Methods in Drug Development: A New Era in Medicine

Unogwu,

Ike,

Joktan

2023

MJAIH

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Artificial intelligence (AI) plays a vital role in the development of pharmaceuticals. The whole drug development process, from target identification and lead optimization to clinical trials and post-marketing monitoring, has the potential to be revolutionized by AI. Data analysis is a crucial area where AI thrives. To find trends, correlations, and prospective targets for therapeutic intervention, AI systems can analyse enormous volumes of data, including electronic health records, molecular databases, academic literature, and clinical trial data. This saves time and resources by enabling researchers to decide which compounds or pathways to investigate. Lead optimization is another area where AI is essential since algorithms can screen and forecast the efficacy of new drug candidates. To improve efficacy, safety, and pharmacokinetics, the most promising leads are prioritized, and their attributes are optimized. By examining past data to pinpoint patient groupings that are most likely to respond to a certain medication, AI can also improve clinical trial design and patient selection. This advances success rates aids in the development of personalized medication and can support post-marketing surveillance by continuously scanning real-world data for adverse events or unexpected drug-drug interactions after a medicine has been licensed. This permits the quicker detection of potential safety issues and contributes to maintaining the continuous safety of licensed medications. However, some obstacles must be overcome to completely utilize AI in medication development. These include the requirement for high-quality data, assuring AI model transparency and interpretability, taking into account ethical issues, and regulatory frameworks for using AI in drug development.

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Section: Drug Discovery and Identificationmentioning

confidence: 99%

Employing Artificial Intelligence Methods in Drug Development: A New Era in Medicine

Unogwu,

Ike,

Joktan

2023

MJAIH

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Applicability domains of neural networks for toxicity prediction

Pérez-Santín,

de-la-Fuente-Valentín,

García

et al. 2023

MATH

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<abstract> <p>In this paper, the term "applicability domain" refers to the range of chemical compounds for which the statistical quantitative structure-activity relationship (QSAR) model can accurately predict their toxicity. This is a crucial concept in the development and practical use of these models. First, a multidisciplinary review is provided regarding the theory and practice of applicability domains in the context of toxicity problems using the classical QSAR model. Then, the advantages and improved performance of neural networks (NNs), which are the most promising machine learning algorithms, are reviewed. Within the domain of medicinal chemistry, nine different methods using NNs for toxicity prediction were compared utilizing 29 alternative artificial intelligence (AI) techniques. Similarly, seven NN-based toxicity prediction methodologies were compared to six other AI techniques within the realm of food safety, 11 NN-based methodologies were compared to 16 different AI approaches in the environmental sciences category and four specific NN-based toxicity prediction methodologies were compared to nine alternative AI techniques in the field of industrial hygiene. Within the reviewed approaches, given known toxic compound descriptors and behaviors, we observed a difficulty in being able to extrapolate and predict the effects with untested chemical compounds. Different methods can be used for unsupervised clustering, such as distance-based approaches and consensus-based decision methods. Additionally, the importance of model validation has been highlighted within a regulatory context according to the Organization for Economic Co-operation and Development (OECD) principles, to predict the toxicity of potential new drugs in medicinal chemistry, to determine the limits of detection for harmful substances in food to predict the toxicity limits of chemicals in the environment, and to predict the exposure limits to harmful substances in the workplace. Despite its importance, a thorough application of toxicity models is still restricted in the field of medicinal chemistry and is virtually overlooked in other scientific domains. Consequently, only a small proportion of the toxicity studies conducted in medicinal chemistry consider the applicability domain in their mathematical models, thereby limiting their predictive power to untested drugs. Conversely, the applicability of these models is crucial; however, this has not been sufficiently assessed in toxicity prediction or in other related areas such as food science, environmental science, and industrial hygiene. Thus, this review sheds light on the prevalent use of Neural Networks in toxicity prediction, thereby serving as a valuable resource for researchers and practitioners across these multifaceted domains that could be extended to other fields in future research.</p> </abstract>

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Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework

Cited by 2 publications

References 78 publications

Employing Artificial Intelligence Methods in Drug Development: A New Era in Medicine

Employing Artificial Intelligence Methods in Drug Development: A New Era in Medicine

Applicability domains of neural networks for toxicity prediction

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