Review
Pharmacology & Pharmacy
Angela Cesaro, Mojtaba Bagheri, Marcelo Torres, Fangping Wan, Cesar de la Fuente-Nunez
Summary: As machine learning and artificial intelligence continue to expand in various sectors, including drug discovery, recent deep learning models have emerged as efficient tools to explore high-dimensional data and design compounds with desired properties. This article provides a review of key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. Various deep learning approaches, such as discriminative and generative models, are described and their integration with bioinformatics, molecular dynamics, and data augmentation is discussed as potential solutions to challenges in accurate antimicrobial prediction.
EXPERT OPINION ON DRUG DISCOVERY
(2023)
Review
Pharmacology & Pharmacy
Anh T. N. Nguyen, Diep T. N. Nguyen, Huan Yee Koh, Jason Toskov, William MacLean, Andrew Xu, Daokun Zhang, Geoffrey I. Webb, Lauren T. May, Michelle L. Halls
Summary: The application of artificial intelligence in drug discovery for G protein-coupled receptors (GPCRs) is expanding rapidly. It can assist in understanding the actions of GPCRs, discovering new ligand-GPCR interactions, and predicting clinical responses. This article provides an overview of artificial intelligence concepts and its applications in different stages of GPCR drug discovery. The benefits and limitations of artificial intelligence are discussed, along with the potential for further development in assisting GPCR drug discovery.
BRITISH JOURNAL OF PHARMACOLOGY
(2023)
Article
Materials Science, Composites
Fengqing Chen, Longjie Weng, Jinhe Wang, Pin Wu, Dianpu Ma, Fei Pan, Peng Ding
Summary: Extensive machine learning methods have brought about significant changes in various fields such as metals, catalysts, and polymers. However, the application of machine learning in the exploration of functional polymer-based composites, particularly in flame retardancy, is still in its early stages. In this study, an adaptive framework combining domain knowledge and machine learning was proposed to accelerate the optimization of high flame retardant composites. Different data resources, including experiments, handbooks, and published papers, were used for training, feedback, or prediction purposes. The framework demonstrated an effective approach for feature engineering and classification of flame-retardant polymer-based composites. Four machine learning methods were compared in the framework, and the combination of Lasso, Ridge, and ANN showed higher accuracy in predicting the limit oxygen index (LOI), assisting in the discovery of new experiments and effective prediction of different flame retardants. The optimized models from the adaptive framework could contribute to machine intelligence in the engineering of flame-retardant polymer-based composites. Moreover, the proposed adaptive framework has the potential to be extended to the machine intelligence design of other functional polymer-based composites.
COMPOSITES SCIENCE AND TECHNOLOGY
(2023)
Article
Construction & Building Technology
Mostafa Hassani Niaki, Matin Pashaian, Morteza Ghorbanzadeh Ahangari
Summary: This study investigates the usefulness of a deep learning-based method, DNN, in predicting the mechanical properties of basalt fiber and nanoclay reinforced polymer concrete. The study presents three independent DNN models and achieves satisfactory accuracy in predicting the properties of PC. The results demonstrate the effectiveness of the deep learning method in predicting PC properties.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Wenjie Shang, Minxiang Zeng, A. N. M. Tanvir, Ke Wang, Mortaza Saeidi-Javash, Alexander Dowling, Tengfei Luo, Yanliang Zhang
Summary: A hybrid data-driven strategy combining Bayesian optimization and Gaussian process regression is proposed to optimize the composition of AgSe-based thermoelectric materials. Through active collection of experimental data, a significant improvement in material performance is achieved within seven iterations.
ADVANCED MATERIALS
(2023)
Article
Chemistry, Multidisciplinary
Attila Kovacs, Philippe Nimmegeers, Ana Cunha, Joost Brancart, Seyed Soheil Mansouri, Rafiqul Gani, Pieter Billen
Summary: Chemical recycling of polymers is gaining momentum as a circular technology. However, the high energy demand and sensitivity of complex formulations hinder efficient recycling. To address this, a hybrid model-based framework is proposed, combining machine learning, structure-property models, and experimental data. This approach aims to substitute virgin feedstocks with complex recyclates and improve the efficiency of polymer recycling.
CURRENT OPINION IN GREEN AND SUSTAINABLE CHEMISTRY
(2023)
Article
Pharmacology & Pharmacy
Tingting Jin, Wei Xu, Roufen Chen, Liteng Shen, Jian Gao, Lei Xu, Xinglong Chi, Nengming Lin, Lixin Zhou, Zheyuan Shen, Bo Zhang
Summary: This study successfully identified potential WEE1 inhibitors through virtual screening, and compound 4 exhibited excellent inhibitory activity and anti-proliferative effects, indicating its potential as a WEE1 inhibitor.
FRONTIERS IN PHARMACOLOGY
(2023)
Article
Engineering, Chemical
Hasan Can Gulbalkan, Zeynep Pinar Haslak, Cigdem Altintas, Alper Uzun, Seda Keskin
Summary: Covalent organic frameworks (COFs) have emerged as promising adsorbents and membranes for gas separation. This study explores the performance of IL/COF composites in CO2/N2 adsorption and membrane separation through computational screening methods. The incorporation of ILs significantly improves the adsorption selectivity and performance scores of COFs. The nature of the interactions between CO2, N2, COFs, and IL-incorporated composites was evaluated using DFT calculations. IL/COF membranes exhibit high CO2 permeability and comparable selectivity to traditional polymer and zeolite membranes, showing potential for flue gas separation.
SEPARATION AND PURIFICATION TECHNOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Ummu Raihanah Hashim, Aidah Jumahat, Mohammad Jawaid
Summary: Basalt fibre (BF) is a promising natural reinforcing material for polymer composites, and when modified with nanofillers like graphene nanoplatelets (GNP) and nanosilica (NS), it shows enhanced mechanical properties. The incorporation of hybrid nanofillers contributes to the improvement in the mechanical properties of the composites, especially in terms of modulus and strength. This study demonstrates the potential of BF as an alternative to synthetic glass fibre for mechanical components and structures.
Article
Chemistry, Multidisciplinary
Aisha Okmi, Xuemei Xiao, Yue Zhang, Rui He, Olugbenga Olunloyo, Sumner B. Harris, Tara Jabegu, Ningxin Li, Diren Maraba, Yasmeen Sherif, Ondrej Dyck, Ivan Vlassiouk, Kai Xiao, Pei Dong, Baoxing Xu, Sidong Lei
Summary: This study reports a newly observed free-standing graphene-water membrane structure, which replaces the conventional solid-state supporting media with liquid film to sustain the graphene integrity and continuity. Experimental observation, theoretical model, and molecular dynamics simulations consistently indicate that the high surface tension of pure water and its large contact angling with graphene are essential factors for forming such a membrane structure.
Review
Chemistry, Multidisciplinary
Maya A. Farha, Shawn French, Eric D. Brown
Summary: The lack of success in new antibiotic drug discovery is partly due to a lack of understanding of the bacterial cell system as a whole. System-level approaches have the potential to be powerful tools for innovation and advancement in antibacterial research.
ACCOUNTS OF CHEMICAL RESEARCH
(2021)
Article
Chemistry, Multidisciplinary
Yafei Wang, Bonita Goh, Phalgun Nelaturu, Thien Duong, Najlaa Hassan, Raphaelle David, Michael Moorehead, Santanu Chaudhuri, Adam Creuziger, Jason Hattrick-Simpers, Dan J. Thoma, Kumar Sridharan, Adrien Couet
Summary: This article presents an integrated approach using high-throughput alloy synthesis, corrosion testing, and modeling to accelerate the development of corrosion-resistant alloys for molten salt applications. It also uncovers a sacrificial protection mechanism in the corrosion of Cr-Fe-Mn-Ni alloys in molten salts, providing new insights for the design of high-temperature molten salt corrosion-resistant alloys.
Article
Chemistry, Physical
Dong Hyeon Mok, Jongseung Kim, Seoin Back
Summary: In order to achieve a renewable and sustainable energy cycle, researchers have been working on finding catalysts with desired properties. Various screening strategies have been proposed, but most of them require computationally intensive calculations to validate the stability and synthesizability of candidate materials. This problem can be addressed by using machine learning methods to reduce the number of calculations. In this study, a direction-based crystal graph convolutional neural network (D-CGCNN) was developed to accurately predict the formation energy of relaxed structures using initial structures as inputs.
CHEMISTRY OF MATERIALS
(2023)
Article
Computer Science, Artificial Intelligence
Hongliang He, Yanli Fan
Summary: This study proposes a novel hybrid ensemble model that combines tree-based and deep learning methods, utilizing feature generation and interaction learning to achieve significant performance improvement in default prediction tasks.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Multidisciplinary Sciences
Zahra Ebrahim Nataj, Youming Xu, Dylan Wright, Jonas O. Brown, Jivtesh Garg, Xi Chen, Fariborz Kargar, Alexander A. Balandin
Summary: In this study, the authors investigate the thermal conductivity of graphene composites at different temperatures. They find that the thermal conductivity can be both higher and lower than that of pure epoxy, depending on the graphene filler loading and temperature. They explain this counter-intuitive trend by the increasing effect of thermal boundary resistance at cryogenic temperatures and the anomalous thermal percolation threshold.
NATURE COMMUNICATIONS
(2023)
Article
Materials Science, Ceramics
Shayan Angizi, Farzaneh Shayeganfar, Mandi Hasanzadeh Azar, Abdolreza Simchi
CERAMICS INTERNATIONAL
(2020)
Article
Chemistry, Multidisciplinary
Mehran Amiri, Javad Beheshtian, Farzaneh Shayeganfar, Mahdi Faghihnasiri, Rouzbeh Shahsavari, Ali Ramazani
Article
Chemistry, Multidisciplinary
Mahdi Faghihnasiri, Vahid Najafi, Farzaneh Shayeganfar, Ali Ramazani
APPLIED SCIENCES-BASEL
(2020)
Review
Materials Science, Multidisciplinary
Shayan Angizi, Sayed Ali Ahmad Alem, Mahdi Hasanzadeh Azar, Farzaneh Shayeganfar, Max Manning, Amir Hatamie, Amir Pakdel, Abdolreza Simchi
Summary: This review explores the fundamental properties of 2D and 0D hexagonal boron nitride nanomaterials, discussing the transition of properties from 2D to zero-dimensional space and analyzing the advantages and disadvantages of different synthesis methods. Special attention is given to the surface chemistry of BNQD nanocrystals and their applications in various fields.Moreover, the ongoing challenges and future directions for BNQD research are also discussed.
PROGRESS IN MATERIALS SCIENCE
(2022)
Article
Biochemistry & Molecular Biology
Elham Rezayei, Javad Beheshtian, Farzaneh Shayeganfar, Ali Ramazani
Summary: In this study, two selective mechanisms for detecting dopamine, uric acid, and ascorbic acid biomolecules on pristine boron nitride nanosheets and functionalized BNNS with tryptophan have been illustrated through density functional density calculation and charge population analysis.
JOURNAL OF MOLECULAR MODELING
(2022)
Correction
Biochemistry & Molecular Biology
Elham Rezayei, Javad Beheshtian, Farzaneh Shayeganfar, Ali Ramazani
JOURNAL OF MOLECULAR MODELING
(2022)
Article
Multidisciplinary Sciences
Farzaneh Shayeganfar
Summary: This study investigates the strain effect on the electronic band structure and valley Hall conductivity of TMD nanoribbons. The findings reveal that strain can induce a phase transition from semiconductor to metal and modify the valley Hall conductivity by generating new energy levels.
SCIENTIFIC REPORTS
(2022)
Article
Multidisciplinary Sciences
Ziba Torkashvand, Kavoos Mirabbaszadeh, Farzaneh Shayeganfar, Changgu Lee
Summary: In this study, the magneto-optical Kerr effect of oxygen functionalized and doped hexagonal boron nitride (hBN) was investigated using first-principles calculations. The results showed that the functionalized hBN material can withstand high-temperature conditions while maintaining stable oxygen bridge-bonding, and it exhibits giant opto/magnetic responses. These findings provide a foundation for the potential design of magneto-optical devices.
SCIENTIFIC REPORTS
(2022)
Article
Materials Science, Multidisciplinary
Mina Bahrami, Farzaneh Shayeganfar, Kavoos Mirabbaszadeh, Ali Ramazani
Summary: This study introduces semi hydrogenated SiB (H-SiB) as an effective anode material for lithium-ion batteries (LIBs) and sodium-ion batteries (NIBs). H-SiB exhibits desirable electronic properties and theoretical capacity. The low diffusion barrier heights and enhanced electronic conductivity of H-SiB contribute to the rate of charging and discharging process. The findings suggest that H-SiB could be a promising flexible electrode material for LIBs.
Article
Chemistry, Physical
Fatemeh Ershadi Moghaddam, Farzaneh Shayeganfar, Ali Ramazani
Summary: This study investigates the physical and chemical adsorption of CO2 on boron-rich nanotubes. The results show that boron-rich BC4N nanotubes undergo physical adsorption, while boron-rich BC2N and p-BCN nanotubes exhibit both chemical and physical adsorption. Additionally, the sensitivity factor and adsorption energy of boron-rich BC2N nanotubes are higher than those of other hybrid nanotubes, making them a promising candidate for CO2 capture under ambient conditions.
JOURNAL OF MATERIALS CHEMISTRY A
(2023)
Article
Chemistry, Multidisciplinary
Mahdi Faghihnasiri, S. Hannan Mousavi, Farzaneh Shayeganfar, Aidin Ahmadi, Javad Beheshtian
Article
Chemistry, Physical
Mahdi Faghihnasiri, Javad Beheshtian, Farzaneh Shayeganfar, Rouzbeh Shahsavari
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2020)
Article
Nanoscience & Nanotechnology
Ali Ramazani, Farzaneh Shayeganfar, Jaafar Jalilian, Nicholas X. Fang
Article
Chemistry, Multidisciplinary
Farzaneh Shayeganfar, Javad Beheshtian, Rouzbeh Shahsavari