Exploring the Chemical Space of CYP17A1 Inhibitors Using Cheminformatics and Machine Learning

Yu, Tianshi; Huang, Tianyang; Yu, Leiye; Nantasenamat, Chanin; Anuwongcharoen, Nuttapat; Piacham, Theeraphon; Ren, Ruobing; Chiang, Ying‐Chih

doi:10.3390/molecules28041679

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…For each compound, we generated multiple sets of fingerprint descriptors using the PaDEL-Descriptor software 27 and RDKit ( https://www.rdkit.org ). Molecular fingerprints are widely employed in the field of cheminformatics because they effectively capture the structural characteristics of chemical compounds 28 – 30 . In this study, we considered nine different categories of molecular fingerprints, which include AP2D, AP2DC, KR, KRC, MACCS, Pubchem, FP4, FP4C, and RDK5 31 – 36 .…”

Section: Methodsmentioning

confidence: 99%

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

Schaduangrat,

Homdee,

Shoombuatong

2023

Sci Rep

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The role of estrogen receptors (ERs) in breast cancer is of great importance in both clinical practice and scientific exploration. However, around 15–30% of those affected do not see benefits from the usual treatments owing to the innate resistance mechanisms, while 30–40% will gain resistance through treatments. In order to address this problem and facilitate community-wide efforts, machine learning (ML)-based approaches are considered one of the most cost-effective and large-scale identification methods. Herein, we propose a new SMILES-based stacked approach, termed StackER, for the accelerated and efficient identification of ERα and ERβ inhibitors. In StackER, we first established an up-to-date dataset consisting of 1,996 and 1,207 compounds for ERα and ERβ, respectively. Using the up-to-date dataset, StackER explored a wide range of different SMILES-based feature descriptors and ML algorithms in order to generate probabilistic features (PFs). Finally, the selected PFs derived from the two-step feature selection strategy were used for the development of an efficient stacked model. Both cross-validation and independent tests showed that StackER surpassed several conventional ML classifiers and the existing method in precisely predicting ERα and ERβ inhibitors. Remarkably, StackER achieved MCC values of 0.829–0.847 and 0.712–0.786 in terms of the cross-validation and independent tests, respectively, which were 5.92–8.29 and 1.59–3.45% higher than the existing method. In addition, StackER was applied to determine useful features for being ERα and ERβ inhibitors and identify FDA-approved drugs as potential ERα inhibitors in efforts to facilitate drug repurposing. This innovative stacked method is anticipated to facilitate community-wide efforts in efficiently narrowing down ER inhibitor screening.

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Section: Methodsmentioning

confidence: 99%

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

Schaduangrat,

Homdee,

Shoombuatong

2023

Sci Rep

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“…Machine learning algorithms may be utilised to predict molecular properties of novel efflux pump inhibitors, as it is now essential to screen for therapeutic targets capable of restoring the effectiveness of known antimicrobials [174]. Furthermore, models were used in quantitative structure-activity relationship (QSAR) modelling of LpxC inhibitors to predict the inhibitory activity and identify the best model [175].…”

Section: Drug Discoverymentioning

confidence: 99%

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Rusic,

Kumric,

Seselja Perisin

et al. 2024

Microorganisms

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Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs’ kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

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“…Further preprocessing involved removing known binding affinity compounds, resulting in a final dataset of 2136 compounds, comprising 1406 "active" and 727 "inactive" compounds. To achieve a more uniform distribution, the IC 50 values were converted to pIC 50 [35].…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors

Pusparini

Krisnadhi

Firdayani³

2023

Molecules

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Breast cancer ranks as the second leading cause of death among women, but early screening and self-awareness can help prevent it. Hormone therapy drugs that target estrogen levels offer potential treatments. However, conventional drug discovery entails extensive, costly processes. This study presents a framework for analyzing the quantitative structure–activity relationship (QSAR) of estrogen receptor alpha inhibitors. Our approach utilizes supervised learning, integrating self-attention Transformer and molecular graph information, to predict estrogen receptor alpha inhibitors. We established five classification models for predicting these inhibitors in breast cancer. Among these models, our proposed MATH model achieved remarkable precision, recall, F1 score, and specificity, with values of 0.952, 0.972, 0.960, and 0.922, respectively, alongside an ROC AUC of 0.977. MATH exhibited robust performance, suggesting its potential to assist pharmaceutical and health researchers in identifying candidate compounds for estrogen alpha inhibitors and guiding drug discovery pathways.

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Exploring the Chemical Space of CYP17A1 Inhibitors Using Cheminformatics and Machine Learning

Cited by 7 publications

References 32 publications

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors

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