Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database

Herrera‐Acevedo, Chonny; Perdomo‐Madrigal, Camilo; Herrera-Acevedo, Kenyi; Coy-Barrera, Ericsson; Scotti, Luciana; Scotti, Luciana

doi:10.1007/s11030-021-10245-z

Cited by 16 publications

(13 citation statements)

References 43 publications

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“…structures and preventing inactive molecules from being predicted as active. [42] In addition, reviewing the obtained results in MolPre-dictX predictions for these 12 molecules, some biological activities were confirmed with bioactivities previously reported in literature. For example, Rodrigues et al, in 2018 [61] reported a pIC 50 value of 4.9 for kaurenoic acid (structure 4) for Leishmania amazonensis promastigotes, [61] in MolpredictX's classification model for this parasitic form (whose cut-off point is 4.79), this molecule was classified as active.…”

Section: Virtual Screening Of Query Molecules Through the Acetylcholi...supporting

confidence: 83%

“…A pIC 50 value above 6.0 was defined as active, based on the reported pIC 50 value of galantamine, which was used as a control since it is one of the primary drugs used in the treatment of Alzheimer's disease. [42,59,60] According to the MolPredictX prediction, only one of the 12 structures, donepezil (structure 5), would possess a potent inhibitory activity against AChE (Table 3).…”

Section: Virtual Screening Of Query Molecules Through the Acetylcholi...mentioning

confidence: 99%

“…We selected twelve, structurally diverse molecules to be screened through our AChE QSAR models based on their availability in the Bioorganic laboratory of the Nueva Granada Military University (Figure 3). A pIC 50 value above 6.0 was defined as active, based on the reported pIC 50 value of galantamine, which was used as a control since it is one of the primary drugs used in the treatment of Alzheimer's disease [42,59,60] . According to the MolPredictX prediction, only one of the 12 structures, donepezil (structure 5 ), would possess a potent inhibitory activity against AChE (Table 3).…”

Section: Molpredictx Utilitymentioning

confidence: 99%

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MolPredictX: Online Biological Activity Predictions by Machine Learning Models

Scotti

Herrera‐Acevedo

Menezes

et al. 2022

Molecular Informatics

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Here we report the development of MolPredictX, an innovate and freely accessible web interface for biological activity predictions of query molecules. MolPredictX utilizes in‐house QSAR models to provide 27 qualitative predictions (active or inactive), and quantitative probabilities for bioactivity against parasitic (Trypanosoma and Leishmania), viral (Dengue, Sars‐CoV and Hepatitis C), pathogenic yeast (Candida albicans), bacterial (Salmonella enterica and Escherichia coli), and Alzheimer disease enzymes. In this article, we introduce the methodology and usability of this webtool, highlighting its potential role in the development of new drugs against a variety of diseases. MolPredictX is undergoing continuous development and is freely available at https://www.molpredictx.ufpb.br/.

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Section: Virtual Screening Of Query Molecules Through the Acetylcholi...supporting

confidence: 83%

Section: Virtual Screening Of Query Molecules Through the Acetylcholi...mentioning

confidence: 99%

Section: Molpredictx Utilitymentioning

confidence: 99%

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MolPredictX: Online Biological Activity Predictions by Machine Learning Models

Scotti

Herrera‐Acevedo

Menezes

et al. 2022

Molecular Informatics

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“…In the most recent VS campaign, the researchers were seeking acetylcholinesterase (AChE) inhibitors, which is an approach for the treatment of Alzheimer’s disease. They employed a combined approach in which machine learning classification models and molecular docking calculations were used to identify two promising AChE inhibitors [ 146 ]. Other applications of SistematX include chemotaxonomic studies using self-organizing map algorithms [ 147 ] and the profile of over 2000 metabolites from the Asteraceae family while screening for inhibitors of Leishmania major dihydroorotate dehydrogenase [ 148 ].…”

Section: Latin American Natural Product Databasesmentioning

confidence: 99%

Progress and Impact of Latin American Natural Product Databases

Gómez-García

Medina-Franco

2022

Biomolecules

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Natural products (NPs) are a rich source of structurally novel molecules, and the chemical space they encompass is far from being fully explored. Over history, NPs have represented a significant source of bioactive molecules and have served as a source of inspiration for developing many drugs on the market. On the other hand, computer-aided drug design (CADD) has contributed to drug discovery research, mitigating costs and time. In this sense, compound databases represent a fundamental element of CADD. This work reviews the progress toward developing compound databases of natural origin, and it surveys computational methods, emphasizing chemoinformatic approaches to profile natural product databases. Furthermore, it reviews the present state of the art in developing Latin American NP databases and their practical applications to the drug discovery area.

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“…In the most recent VS campaign, the researchers were seeking for acetylcholinesterase (AChE) inhibitors, which is an approach for the treatment of Alzheimer's disease. A combined approach in which machine learning classification models and molecular docking calculations were used to identify two promising AChE inhibitors [145]. Other applications of SistematX include chemotaxonomic studies using self-organizing map algorithms [146] and the profile of over 2000 metabolites from the Asteraceae family while screening for inhibitors of Leishmania major dihydroorotate dehydrogenase [147].…”

Section: Sistematxmentioning

confidence: 99%

Progress and Impact of Latin American Natural Product Databases

Gómez-García¹,

Medina-Franco²

2022

Preprint

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Natural products (NPs) are a rich source of structurally novel molecules, and the chemical space they encompass is far from being fully explored. Over history, NPs have represented a significant source of bioactive molecules and have served as a source of inspiration for developing many drugs on the market. On the other hand, computer-aided drug design (CADD) has contributed to drug discovery research, mitigating costs and time. In this sense, compound databases represent a fundamental element for the CADD. This work reviews the progress toward developing compound databases of natural origin, particularly databases developed in Latin America, and their practical applications in the drug discovery area. We also survey the computational methods, emphasizing chemoinformatic approaches to profile natural product databases.

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Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database

Cited by 16 publications

References 43 publications

MolPredictX: Online Biological Activity Predictions by Machine Learning Models

MolPredictX: Online Biological Activity Predictions by Machine Learning Models

Progress and Impact of Latin American Natural Product Databases

Progress and Impact of Latin American Natural Product Databases

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