Proteochemometric Modeling for Drug Repositioning

Schaduangrat, Nalini; Anuwongcharoen, Nuttapat; Phanus‐umporn, Chuleeporn; Sriwanichpoom, Nagaya; Wikberg, Jarl E. S.; Nantasenamat, Chanin

doi:10.1016/b978-0-12-816125-8.00010-9

Cited by 10 publications

(8 citation statements)

References 120 publications

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“…an HIV-1 protease inhibitor) has been demonstrated to exert promising anti-cancer activities against a wide range of cancer types [178]. Aside from network pharmacology proteochemometric modeling is systems-based approach that has also been demonstrated to facilitate drug repositioning [179,180].…”

Section: Systems-based Approachesmentioning

confidence: 99%

Towards reproducible computational drug discovery

et al. 2020

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The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

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Section: Systems-based Approachesmentioning

confidence: 99%

Towards reproducible computational drug discovery

et al. 2020

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“…In silico target prediction is necessary to search for efficient approaches toward novel drug discovery. However, this process for drug development is costly, as it requires precise screening and examination from both preclinical and clinical studies . In this respect, in silico methods for drug discovery have been widely used due to their economic benefits.…”

Section: Resultsmentioning

confidence: 99%

Luteolin, a Potent Human Monoamine Oxidase-A Inhibitor and Dopamine D₄ and Vasopressin V_1A Receptor Antagonist

Park

Paudel

Wagle

et al. 2020

J. Agric. Food Chem.

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Luteolin, a flavonoid widely distributed in the plant kingdom, contains two benzene rings and hydroxyl groups, and this structural specificity contributes to its diverse biological activities. However, no previous studies have simultaneously investigated the therapeutic potency of luteolin isolated from a plant as an antipsychotic and antidepressant. Here, luteolin exhibited selective inhibition of hMAO-A (IC 50 = 8.57 ± 0.47 μM) over hMAO-B (IC 50 > 100 μM). In silico proteochemometric modeling predicted promising targets of luteolin, and verification via cell-based G protein-coupled receptor functional assays showed that luteolin is a selective antagonist of the vasopressin receptor V 1A R (IC 50 = 19.49 ± 6.32 μM) and the dopamine D 4 receptor (IC 50 = 39.59 ± 1.46 μM). Molecular docking showed the tight binding of luteolin with a low binding score and the high stability of the luteolin−receptor complex, corroborating its functional effect. Thus, hMAO-A, hD 4 R, and hV 1A R are prime targets of luteolin and potential alternatives for the management of neurodegenerative diseases.

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“…According to van Westen et al, unlike QSAR, PCM is a modeling that is based on the similarity of a group of ligands and a group of targets, modeling the space of interaction between the ligand-target as it takes into account the chemical descriptors of the compounds added to the descriptor of the protein or target [ 66 ]. Another advantage of PCM according to Schaduangrat et al is that this method does not require knowledge of the three-dimensional structure of the protein, requiring only the sequence of amino acids [ 12 ]. The term ‘proteochemometric’ is most often used in studies that analyze the impact of protein and molecule descriptors on prediction performance [ 67 ].…”

Section: Methodsmentioning

confidence: 99%

“…According to Freyhult et al, the need for prior knowledge of the 3D structure is a bottleneck and therefore it is important to develop new methods that do not require the 3D model of the protein [11]. As a result, according to the authors, a new bioinformatic approach was created called proteochemometrics (PCM), whose strong point is the presentation of results obtained directly from data of real interactions and that does not require knowledge of the 3D protein structure model [11,12].…”

Section: Introductionmentioning

confidence: 99%

Chagas Disease: Perspectives on the Past and Present and Challenges in Drug Discovery

et al. 2020

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Chagas disease still has no effective treatment option for all of its phases despite being discovered more than 100 years ago. The development of commercial drugs has been stagnating since the 1960s, a fact that sheds light on the question of how drug discovery research has progressed and taken advantage of technological advances. Could it be that technological advances have not yet been sufficient to resolve this issue or is there a lack of protocol, validation and standardization of the data generated by different research teams? This work presents an overview of commercial drugs and those that have been evaluated in studies and clinical trials so far. A brief review is made of recent target-based and phenotypic studies based on the search for molecules with anti-Trypanosoma cruzi action. It also discusses how proteochemometric (PCM) modeling and microcrystal electron diffraction (MicroED) can help in the case of the lack of a 3D protein structure; more specifically, Trypanosoma cruzi carbonic anhydrase.

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Proteochemometric Modeling for Drug Repositioning

Cited by 10 publications

References 120 publications

Towards reproducible computational drug discovery

Towards reproducible computational drug discovery

Luteolin, a Potent Human Monoamine Oxidase-A Inhibitor and Dopamine D₄ and Vasopressin V_1A Receptor Antagonist

Chagas Disease: Perspectives on the Past and Present and Challenges in Drug Discovery

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Proteochemometric Modeling for Drug Repositioning

Cited by 10 publications

References 120 publications

Towards reproducible computational drug discovery

Towards reproducible computational drug discovery

Luteolin, a Potent Human Monoamine Oxidase-A Inhibitor and Dopamine D4 and Vasopressin V1A Receptor Antagonist

Chagas Disease: Perspectives on the Past and Present and Challenges in Drug Discovery

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Luteolin, a Potent Human Monoamine Oxidase-A Inhibitor and Dopamine D₄ and Vasopressin V_1A Receptor Antagonist