JUCHMME: a Java Utility for Class Hidden Markov Models and Extensions for biological sequence analysis

Tamposis, Ioannis; Tsirigos, Konstantinos D.; Theodoropoulou, Margarita C.; Kontou, Panagiota I.; Tsaousis, Georgios N.; Sarantopoulou, Dimitra; Litou, Zoi I.; Bagos, Pantelis G.

doi:10.1093/bioinformatics/btz533

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…This ability is vital in discovering new proteins that serve as potential targets for novel drugs. Further, HMMs play a significant role in protein sequence analysis, a critical process in understanding the function of a protein and selecting it as a target in the early stages of drug development [ 43 , 44 ]. Through augmenting sequence analysis, HMMs reveal more accurate and profound insights, thereby enhancing the outcomes.…”

Section: Ai/ml Algorithms and Bio Big Data Utilized In Drug Discovery...mentioning

confidence: 99%

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Han,

Yoon,

Kim

et al. 2023

Pharmaceuticals

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Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug–target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI’s expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI’s growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

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Section: Ai/ml Algorithms and Bio Big Data Utilized In Drug Discovery...mentioning

confidence: 99%

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Han,

Yoon,

Kim

et al. 2023

Pharmaceuticals

View full text Add to dashboard Cite

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Hidden neural networks for transmembrane protein topology prediction

Tamposis

Sarantopoulou

Theodoropoulou

et al. 2021

Computational and Structural Biotechnology Journal

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Significant non-existence of sequences in genomes and proteomes

Koulouras

Frith

2021

Nucleic Acids Research

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Minimal absent words (MAWs) are minimal-length oligomers absent from a genome or proteome. Although some artificially synthesized MAWs have deleterious effects, there is still a lack of a strategy for the classification of non-occurring sequences as potentially malicious or benign. In this work, by using Markovian models with multiple-testing correction, we reveal significant absent oligomers, which are statistically expected to exist. This suggests that their absence is due to negative selection. We survey genomes and proteomes covering the diversity of life and find thousands of significant absent sequences. Common significant MAWs are often mono- or dinucleotide tracts, or palindromic. Significant viral MAWs are often restriction sites and may indicate unknown restriction motifs. Surprisingly, significant mammal genome MAWs are often present, but rare, in other mammals, suggesting that they are suppressed but not completely forbidden. Significant human MAWs are frequently present in prokaryotes, suggesting immune function, but rarely present in human viruses, indicating viral mimicry of the host. More than one-fourth of human proteins are one substitution away from containing a significant MAW, with the majority of replacements being predicted harmful. We provide a web-based, interactive database of significant MAWs across genomes and proteomes.

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JUCHMME: a Java Utility for Class Hidden Markov Models and Extensions for biological sequence analysis

Cited by 5 publications

References 28 publications

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Hidden neural networks for transmembrane protein topology prediction

Significant non-existence of sequences in genomes and proteomes

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