Insight into TLR4 receptor inhibitory activity <i>via</i> QSAR for the treatment of <i>Mycoplasma pneumonia</i> disease

Zhu, Zemin; Rahman, Ziaur; Aamir, Muhammad; Shah, Syed Qaiser; Hamid, Sattar; Bilawal, Akhunzada; Li, Sihong; Ishfaq, Muhammad

doi:10.1039/d2ra06178c

Cited by 38 publications

(13 citation statements)

References 59 publications

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“…It learns from past data and makes new predictions without performing experiments or computations. 13–15 Machine learning has the ability to predict almost every property for which enough data are available. 16–18…”

Section: Introductionmentioning

confidence: 99%

“…It learns from past data and makes new predictions without performing experiments or computations. [13][14][15] Machine learning has the ability to predict almost every property for which enough data are available. [16][17][18] Sanchez-Lengeling et al reported a machine learning approach based on the Bayesian method to predict Hansen solubility parameters.…”

Section: Introductionmentioning

confidence: 99%

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Easy and fast prediction of green solvents for small molecule donor-based organic solar cells through machine learning

Wang

Mahmood

Sandali

2023

Phys. Chem. Chem. Phys.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Easy and fast prediction of green solvents for small molecule donor-based organic solar cells through machine learning

Wang

Mahmood

Sandali

2023

Phys. Chem. Chem. Phys.

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“…The conventional methods are not sufficient to perform the task needed. Advanced algorithms need to be added in order to meet the desired targets 29–31 …”

Section: Introductionmentioning

confidence: 99%

“…Advanced algorithms need to be added in order to meet the desired targets. [29][30][31] ML is a potent tool that has the potential to revolutionize material sciences. 32 Recent advancements in AI increased computational power, and increased data volume will allow us to systematize material design and discovery.…”

Section: Introductionmentioning

confidence: 99%

Designing of benzofuran‐based monomers for photodetectors through similarity analysis and library enumeration

Saqib

Mubashir

Tahir

et al. 2023

J of Physical Organic Chem

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Organic molecules have been extensively utilized in various applications including materials science, chemical, and biomedical fields. Traditionally, the design of organic molecules is achieved through experimental approaches, guided by conceptual insights, intuition, and experience. Although these experimental approaches have been successfully utilized to unveil various high‐performance materials, these methods show serious limitations due to vast design space and the ever‐increasing demand for organic molecules (new materials). Artificial intelligence with computer science is used by modern researchers to design materials with better performance and for predicting the properties of new materials. Herein, benzofuran‐based building blocks are used as a standard molecule to search for new building blocks. Similarity analysis is performed to screen/search potential candidates for photodetectors based on the chemical structural similarity. Extended‐connectivity fingerprints (ECFPs) are used for the similarity analysis. The virtual libraries of unique monomers are enumerated. The breaking retro‐synthetically interesting chemical substructures (BRICS) method is also used to design building blocks by automatically decomposing and combining monomers in enumerated libraries. Moreover, this work offers a potential way to identify new monomers for photodetectors cost‐effectively and rapidly.

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“…ML algorithms come in a vast variety. Selecting an appropriate algorithm, which is typically done using a trial-and-error method, is essential to achieving a highly effective model [32,33]. The best algorithms can be selected with its assistance.…”

Section: Introductionmentioning

confidence: 99%

Predicting the multiple parameters of organic acceptors through machine learning using RDkit descriptors: An easy and fast pipeline

Katubi,

Saqib,

Mubashir

et al. 2023

Int J of Quantum Chemistry

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Machine learning (ML) analysis has gained huge importance among researchers for predicting multiple parameters and designing efficient donor and acceptor materials without experimentation. Data are collected from literature and subsequently used for predicting impactful properties of organic solar cells such as power conversion efficiency (PCE) and energy levels (HOMO/LUMO). Importantly, out of various tested models, hist gradient boosting (HGB) and the light gradient boosting (LGBM) regression models revealed better predictive capabilities. To achieve the prediction effectively, the selected (best) ML regression models are further tuned. For the prediction of PCE (test set), the LGBM shows the coefficient of determination (R2) value of 0.787, which is higher than HGB (R2 = 0.680). For the prediction of HOMO (test set), the LGBM shows R2 value of 0.566, which is higher than HGB (R2 = 0.563). However, for the prediction of LUMO (test set), the LGBM shows R2 value of 0.605, which is lower than HGB (R2 = 0.606). Among the three predicted properties, prediction ability is higher for PCE. These models help to predict the efficient acceptors in a short time and less computational cost.

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Insight into TLR4 receptor inhibitory activity via QSAR for the treatment of Mycoplasma pneumonia disease

Abstract: Mycoplasma pneumoniae (MP) is one of the most common pathogenic organisms causing upper and lower respiratory tract infections, lung injury, and even death in young children.

Cited by 38 publications

References 59 publications

Easy and fast prediction of green solvents for small molecule donor-based organic solar cells through machine learning

Easy and fast prediction of green solvents for small molecule donor-based organic solar cells through machine learning

Designing of benzofuran‐based monomers for photodetectors through similarity analysis and library enumeration

Predicting the multiple parameters of organic acceptors through machine learning using RDkit descriptors: An easy and fast pipeline

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