The recent advances in the approach of artificial intelligence (AI) towards drug discovery

Khan, Mahroza Kanwal; Raza, Mohsin; Shahbaz, Muhammad; Hussain, Iftikhar; Khan, Muhammad Farooq; Xie, Zhongjian; Shah, Syed Shoaib Ahmad; Tareen, Ayesha Khan; Bashir, Zoobia; Khan, Karim

doi:10.3389/fchem.2024.1408740

Cited by 3 publications

References 116 publications

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The power of artificial intelligence for managing pandemics: A primer for public health professionals

McKee,

Rosenbacke,

Stuckler

2024

Health Planning & Management

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Artificial intelligence (AI) applications are complex and rapidly evolving, and thus often poorly understood, but have potentially profound implications for public health. We offer a primer for public health professionals that explains some of the key concepts involved and examines how these applications might be used in the response to a future pandemic. They include early outbreak detection, predictive modelling, healthcare management, risk communication, and health surveillance. Artificial intelligence applications, especially predictive algorithms, have the ability to anticipate outbreaks by integrating diverse datasets such as social media, meteorological data, and mobile phone movement data. Artificial intelligence‐powered tools can also optimise healthcare delivery by managing the allocation of resources and reducing healthcare workers' exposure to risks. In resource distribution, they can anticipate demand and optimise logistics, while AI‐driven robots can minimise physical contact in healthcare settings. Artificial intelligence also shows promise in supporting public health decision‐making by simulating the social and economic impacts of different policy interventions. These simulations help policymakers evaluate complex scenarios such as lockdowns and resource allocation. Additionally, it can enhance public health messaging, with AI‐generated health communications shown to be more effective than human‐generated messages in some cases. However, there are risks, such as privacy concerns, biases in models, and the potential for ‘false confirmations’, where AI reinforces incorrect decisions. Despite these challenges, we argue that AI will become increasingly important in public health crises, but only if integrated thoughtfully into existing systems and processes.

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The power of artificial intelligence for managing pandemics: A primer for public health professionals

McKee,

Rosenbacke,

Stuckler

2024

Health Planning & Management

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Transformer Graph Variational Autoencoder for Generative Molecular Design

Nguyen,

Karolak

2024

Preprint

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In the field of drug discovery, the generation of new molecules with desirable properties remains a critical challenge. Traditional methods often rely on SMILES (Simplified Molecular Input Line Entry System) representations for molecular input data, which can limit the diversity and novelty of generated molecules. To address this, we present the Transformer Graph Variational Autoencoder (TGVAE), an innovative AI model that employs molecular graphs as input data, thus captures the complex structural relationships within molecules more effectively than string models. To enhance molecular generation capabilities, TGVAE combines a Transformer, Graph Neural Network (GNN), and Variational Autoencoder (VAE). Additionally, we address common issues like over-smoothing in training GNNs and posterior collapse in VAE to ensure robust training and improve the generation of chemically valid and diverse molecular structures. Our results demonstrate that TGVAE outperforms existing approaches, generating a larger collection of diverse molecules and discovering structures that were previously unexplored. This advancement not only brings more possibilities for drug discovery but also sets a new level for the use of AI in molecular generation.

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Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

Serrano,

Luciano,

Anaya

et al. 2024

Pharmaceutics

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Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI’s applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI’s transformative impact on the pharmaceutical industry and its broader implications for healthcare.

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The recent advances in the approach of artificial intelligence (AI) towards drug discovery

Cited by 3 publications

References 116 publications

The power of artificial intelligence for managing pandemics: A primer for public health professionals

The power of artificial intelligence for managing pandemics: A primer for public health professionals

Transformer Graph Variational Autoencoder for Generative Molecular Design

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

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