Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models

Munteanu, Cristian R.; González-Dı́az, Humberto; García, Rafael; Loza, Mabel

doi:10.2174/1386207318666150803140950

Cited by 7 publications

(5 citation statements)

References 74 publications

(75 reference statements)

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“…There are a range of publications with prediction models for specific drug biological activity, drug toxicity, protein target interaction 33 . Some studies developed models based on Shannon entropy measures and encompassing a multitarget network to predict multitarget drugs.…”

Section: Discussionmentioning

confidence: 99%

Pred-binding: large-scale protein–ligand binding affinity prediction

Shar

Tao

Gao

et al. 2016

Journal of Enzyme Inhibition and Medicinal Chemistry

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Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently, experimental determination of compound-protein interactions remains challenging because of funding investment and difficulties of purifying proteins. In this study, we proposed two in silico models based on support vector machine (SVM) and random forest (RF), using 1589 molecular descriptors and 1080 protein descriptors in 9948 ligand-protein pairs to predict DTIs that were quantified by K i values. The cross-validation coefficient of determination of 0.6079 for SVM and 0.6267 for RF were obtained, respectively. In addition, the two-dimensional (2D) autocorrelation, topological charge indices and three-dimensional (3D)-MoRSE descriptors of compounds, the autocorrelation descriptors and the amphiphilic pseudo-amino acid composition of protein are found most important for K i predictions. These models provide a new opportunity for the prediction of ligand-receptor interactions that will facilitate the target discovery and toxicity evaluation in drug development.

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Section: Discussionmentioning

confidence: 99%

Pred-binding: large-scale protein–ligand binding affinity prediction

Shar

Tao

Gao

et al. 2016

Journal of Enzyme Inhibition and Medicinal Chemistry

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“…Because chemical structures are in a simplified molecular input line entry system (SMILES) format, they are similar to their own language 28 . Thus, SMILES strings can be trained using transformers for transformer models to learn different characteristics of chemical data, such as chemical properties and its structures 28 – 31 . Chemical data are often complex and high-dimensional, making it difficult to train a model from scratch using limited data 28 .…”

Section: Introductionmentioning

confidence: 99%

Predicting blood–brain barrier permeability of molecules with a large language model and machine learning

Huang,

Yang,

Liao

et al. 2024

Sci Rep

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Predicting the blood–brain barrier (BBB) permeability of small-molecule compounds using a novel artificial intelligence platform is necessary for drug discovery. Machine learning and a large language model on artificial intelligence (AI) tools improve the accuracy and shorten the time for new drug development. The primary goal of this research is to develop artificial intelligence (AI) computing models and novel deep learning architectures capable of predicting whether molecules can permeate the human blood–brain barrier (BBB). The in silico (computational) and in vitro (experimental) results were validated by the Natural Products Research Laboratories (NPRL) at China Medical University Hospital (CMUH). The transformer-based MegaMolBART was used as the simplified molecular input line entry system (SMILES) encoder with an XGBoost classifier as an in silico method to check if a molecule could cross through the BBB. We used Morgan or Circular fingerprints to apply the Morgan algorithm to a set of atomic invariants as a baseline encoder also with an XGBoost classifier to compare the results. BBB permeability was assessed in vitro using three-dimensional (3D) human BBB spheroids (human brain microvascular endothelial cells, brain vascular pericytes, and astrocytes). Using multiple BBB databases, the results of the final in silico transformer and XGBoost model achieved an area under the receiver operating characteristic curve of 0.88 on the held-out test dataset. Temozolomide (TMZ) and 21 randomly selected BBB permeable compounds (Pred scores = 1, indicating BBB-permeable) from the NPRL penetrated human BBB spheroid cells. No evidence suggests that ferulic acid or five BBB-impermeable compounds (Pred scores < 1.29423E−05, which designate compounds that pass through the human BBB) can pass through the spheroid cells of the BBB. Our validation of in vitro experiments indicated that the in silico prediction of small-molecule permeation in the BBB model is accurate. Transformer-based models like MegaMolBART, leveraging the SMILES representations of molecules, show great promise for applications in new drug discovery. These models have the potential to accelerate the development of novel targeted treatments for disorders of the central nervous system.

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“…In recent years, machine learning methods have been widely applied in the field of chemical informatics and bioinformatics 14–16. The Structure Activity Relationship (SAR)/Quantitative Structure Activity Relationship (QSAR) model has been established as one of the major computational modeling methodologies 17.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we collected a dataset 1 of 479 neuraminidase inhibitors referring to influenza A viruses (H1N1), and then built computational models to classify the neuraminidase inhibitors according to their bioactivity. Considered the multiple subtypes of H1N1, a dataset 2 of 208 neuraminidase inhibitors referring to A/PR/8/34 (H1N1)14 was extracted from dataset 1 to build other models. A/P/8/34 is a typical strain of H1N1,18 which is normally used in virus research in library.…”

Section: Introductionmentioning

confidence: 99%

Using Support Vector Machine (SVM) for Classification of Selectivity of H1N1 Neuraminidase Inhibitors

Kong

Zhang

et al. 2016

Molecular Informatics

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Inhibition of the neuraminidase is one of the most promising strategies for preventing influenza virus spreading. 479 neuraminidase inhibitors are collected for dataset 1 and 208 neuraminidase inhibitors for A/P/8/34 are collected for dataset 2. Using support vector machine (SVM), four computational models were built to predict whether a compound is an active or weakly active inhibitor of neuraminidase. Each compound is represented by MASSC fingerprints and ADRIANA.Code descriptors. The predication accuracies for the test sets of all the models are over 78 %. Model 2B, which is the best model, obtains a prediction accuracy and a Matthews Correlation Coefficient (MCC) of 89.71 % and 0.81 on test set, respectively. The molecular polarizability, molecular shape, molecular size and hydrogen bonding are related to the activities of neuraminidase inhibitors. The models can be obtained from the authors.

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Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models

Cited by 7 publications

References 74 publications

Pred-binding: large-scale protein–ligand binding affinity prediction

Pred-binding: large-scale protein–ligand binding affinity prediction

Predicting blood–brain barrier permeability of molecules with a large language model and machine learning

Using Support Vector Machine (SVM) for Classification of Selectivity of H1N1 Neuraminidase Inhibitors

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