Artificial intelligence and machine learning for smart bioprocesses

Bioresource Technology, Год журнала: 2023, Номер 375, С. 128826 - 128826

Опубликована: Март 5, 2023

Язык: Английский

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Computer-aided nanodrug discovery: recent progress and future prospects

Chemical Society Reviews, Год журнала: 2024, Номер 53(18), С. 9059 - 9132

Опубликована: Янв. 1, 2024

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation the 1990s. Substantial efforts been made to develop nanodrugs for overcoming limitations of conventional drugs, such as low targeting efficacy, high dosage toxicity, potential drug resistance. Despite significant progress that has nanodrug discovery, precise design or screening with desired biomedical functions prior experimentation remains a challenge. This is particularly case regard personalised precision nanodrugs, require simultaneous optimisation structures, compositions, surface functionalities nanodrugs. The development powerful computer clusters algorithms it possible overcome this challenge through

Язык: Английский

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Nonsense-Mediated mRNA Decay: Mechanistic Insights and Physiological Significance

Molecular Biotechnology, Год журнала: 2023, Номер 66(11), С. 3077 - 3091

Опубликована: Ноя. 6, 2023

Язык: Английский

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Tunnel engineering of gas-converting enzymes for inhibitor retardation and substrate acceleration

Bioresource Technology, Год журнала: 2023, Номер 394, С. 130248 - 130248

Опубликована: Дек. 27, 2023

Carbon monoxide dehydrogenase (CODH), formate (FDH), hydrogenase (H2ase), and nitrogenase (N2ase) are crucial enzymatic catalysts that facilitate the conversion of industrially significant gases such as CO, CO

Язык: Английский

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Combinatorial protein engineering and transporter engineering for efficient synthesis of L-Carnosine in Escherichia coli

Bioresource Technology, Год журнала: 2023, Номер 387, С. 129628 - 129628

Опубликована: Авг. 7, 2023

Язык: Английский

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The impact of thermophysical properties on eflornithine drug solute in acetone and ethyl acetate solvent interactions at varying concentrations and temperatures

BMC Chemistry, Год журнала: 2024, Номер 18(1)

Опубликована: Авг. 16, 2024

The study was conducted on the impact of thermophysical properties eflornithine drug solute–solvent interactions in aqueous ethyl acetate and acetone at different concentrations temperatures. aim this is to enhance understanding eflornithine's behavior solvents, which crucial for its effective use pharmaceutical applications. density, molar volume, viscometric, conductometric characteristics solutions (0.025, 0.05, 0.075, 0.1, 0.125 mol/kg) 25% (v/v) were measured within a temperature range 298.15 K–318.15 K. Based determined density parameters, following parameters assessed: viscosity (η), equivalent conductance, limiting apparent volume (V0φ), transfer (V0φtr), (Vφ). Masson empirical relationship viscosity-to-Jones-Dole (JD) equation used evaluate partial (Vφ), experimental slope (SV), viscosity, data. Temperature concentration determine each parameter. For set dilutions, studies both solvents. gathered data analyzed order ion–solvent interactions. Walden product Λomηo's positive coefficient values indicate that functions as structural modifier acetyl systems. structure-making breaking polar solvents identified.

Язык: Английский

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Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(22), С. 12233 - 12233

Опубликована: Ноя. 14, 2024

The complexities inherent in drug development are multi-faceted and often hamper accuracy, speed efficiency, thereby limiting success. This review explores how recent developments machine learning (ML) significantly impacting target-based discovery, particularly small-molecule approaches. Simplified Molecular Input Line Entry System (SMILES), which translates a chemical compound's three-dimensional structure into string of symbols, is now widely used design, mining, repurposing. Utilizing ML natural language processing techniques, SMILES has revolutionized lead identification, high-throughput screening virtual screening. models enhance the accuracy predicting binding affinity selectivity, reducing need for extensive experimental Additionally, deep learning, with its strengths analyzing spatial sequential data through convolutional neural networks (CNNs) recurrent (RNNs), shows promise screening, target de novo design. Fragment-based approaches also benefit from algorithms techniques like generative adversarial (GANs), predict fragment properties affinities, aiding hit selection design optimization. Structure-based relies on high-resolution protein structures, leverages accurate predictions interactions. While challenges such as interpretability quality remain, ML's transformative impact accelerates increasing efficiency innovation. Its potential to deliver new improved treatments various diseases significant.

Язык: Английский

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Integrated machine learning methods with oversampling technique for regional suitability prediction of waste-to-energy incineration projects

Waste Management, Год журнала: 2023, Номер 174, С. 251 - 262

Опубликована: Дек. 8, 2023

Язык: Английский

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Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design

Molecular Biotechnology, Год журнала: 2024, Номер 67(3), С. 862 - 884

Опубликована: Март 18, 2024

Язык: Английский

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Machine learning for <i>in silico</i> protein research

Acta Physica Sinica, Год журнала: 2024, Номер 73(6), С. 069301 - 069301

Опубликована: Янв. 1, 2024

<i>In silico</i> protein calculation has been an important research subject for a long time, while its recent combination with machine learning promotes the development greatly in related areas. This review focuses on four major fields of <i>in that combines learning, which are molecular dynamics, structure prediction, property prediction and molecule design. Molecular dynamics depend parameters force field, is necessary obtaining accurate results. Machine can help researchers to obtain more field parameters. In simulation, also perform free energy relatively low cost. Structure generally used predict given sequence. high complexity data volume, exactly what good at. By scientists have gained great achievements three-dimensional proteins. On other hand, predicting properties based known information study protein. More challenging, however, Though marching made breakthroughs drug-like small design years, there still plenty room exploration. summarizing above andlooks forward application research.

Язык: Английский

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