Artificial intelligence and machine learning for protein toxicity prediction using proteomics data

Vishnoi, Shubham; Matre, Himani; Garg, Prabha; Pandey, Shubham Kumar

doi:10.1111/cbdd.13701

Cited by 29 publications

(9 citation statements)

References 92 publications

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“…AI algorithms are capable of predicting patient responses and refining trial procedures using patient data, illness features, and treatment results. This streamlines patient enrollment, study design, and personalized treatment [ 162 , 163 , 164 ]. AI has the potential to significantly enhance research, diagnostics, and therapeutics in the fields of exosomes, CAR T-cell therapy, and CRISPR/Cas9 [ 164 , 165 , 166 ].…”

Section: Ai For Drug Deliverymentioning

confidence: 99%

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora,

Gholap,

Jetha

et al. 2023

Pharmaceutics

242

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Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

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Section: Ai For Drug Deliverymentioning

confidence: 99%

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora,

Gholap,

Jetha

et al. 2023

Pharmaceutics

242

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“…These models can mine through relationships between antimicrobial activity and biochemical features, which help predict AMPs in large-scale environments [ 136 ]. Machine learning methods can potentially lead to reduction of toxicity seen in AMPs by modifying the physicochemical features and chemical modifications responsible for toxicity [ 137 ].…”

Section: Limitations and Strategies For Clinical Applicationsmentioning

confidence: 99%

Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology

et al. 2023

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Antimicrobial peptides (AMPs) have been investigated for their potential use as an alternative to antibiotics due to the increased demand for new antimicrobial agents. AMPs, widely found in nature and obtained from microorganisms, have a broad range of antimicrobial protection, allowing them to be applied in the treatment of infections caused by various pathogenic microorganisms. Since these peptides are primarily cationic, they prefer anionic bacterial membranes due to electrostatic interactions. However, the applications of AMPs are currently limited owing to their hemolytic activity, poor bioavailability, degradation from proteolytic enzymes, and high-cost production. To overcome these limitations, nanotechnology has been used to improve AMP bioavailability, permeation across barriers, and/or protection against degradation. In addition, machine learning has been investigated due to its time-saving and cost-effective algorithms to predict AMPs. There are numerous databases available to train machine learning models. In this review, we focus on nanotechnology approaches for AMP delivery and advances in AMP design via machine learning. The AMP sources, classification, structures, antimicrobial mechanisms, their role in diseases, peptide engineering technologies, currently available databases, and machine learning techniques used to predict AMPs with minimal toxicity are discussed in detail.

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“…Random forests, genetic algorithms, and artificial neural networks are some of the machine-learning techniques used in the toxicity analysis of new proteins (Vishnoi et al, 2020).…”

Section: Pharmacy and Bioinformaticsmentioning

confidence: 99%

One Health and a Computational Biology approach

Rodrigues

Oliveira

Franco

et al. 2022

RSD

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The current concept of One Health is based on the union of three inseparable pillars: human, animal, and environmental health, principles that must be paramount in any project or action in a society. The holistic view becomes fundamental to ensure levels of excellence in the health area as a whole, in addition to numerous diseases and pathologies being prevented and combated through the integrated action of professionals in these three areas. Nevertheless, One Health emerges as a worldwide concept and several projects are being based on this common good practice integrated with the most prominent technologies today, such as computational biology. In this way, national measures and laws are also being amended in pursuit of the principle being based on all places and situations that need to use environmental or animal resources for any circumstance. The primary objective of this brief literature review is to exemplify the concept of the One Health approach based on articles that applied the concept practically, with emphasis on prophylactic measures, applications in bioinformatics, and results presented with this well-known foundation.

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Artificial intelligence and machine learning for protein toxicity prediction using proteomics data

Cited by 29 publications

References 92 publications

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology

One Health and a Computational Biology approach

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