Drug Design
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2021 ◽  
Sheikh Arslan Sehgal ◽  
Rana Adnan Tahir ◽  
Muhammad Waqas

2021 ◽  
Vol 22 (18) ◽  
pp. 9983
Jintae Kim ◽  
Sera Park ◽  
Dongbo Min ◽  
Wankyu Kim

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug–target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

Molecules ◽  
2021 ◽  
Vol 26 (18) ◽  
pp. 5554
Yen-Hua Huang ◽  
Qingdan Du ◽  
Zhihao Jiang ◽  
Gordon J. King ◽  
Brett M. Collins ◽  

Cyclotides have attracted great interest as drug design scaffolds because of their unique cyclic cystine knotted topology. They are classified into three subfamilies, among which the bracelet subfamily represents the majority and comprises the most bioactive cyclotides, but are the most poorly utilized in drug design applications. A long-standing challenge has been the very low in vitro folding yields of bracelets, hampering efforts to characterize their structures and activities. Herein, we report substantial increases in bracelet folding yields enabled by a single point mutation of residue Ile-11 to Leu or Gly. We applied this discovery to synthesize mirror image enantiomers and used quasi-racemic crystallography to elucidate the first crystal structures of bracelet cyclotides. This study provides a facile strategy to produce bracelet cyclotides, leading to a general method to easily access their atomic resolution structures and providing a basis for development of biotechnological applications.

Molecules ◽  
2021 ◽  
Vol 26 (18) ◽  
pp. 5567
Wenhua Li ◽  
Minghui Li ◽  
Jing Qi

Glutathione (GSH) is involved in and regulates important physiological functions of the body as an essential antioxidant. GSH plays an important role in anti-oxidation, detoxification, anti-aging, enhancing immunity and anti-tumor activity. Herein, based on the physiological properties of GSH in different diseases, mainly including the strong reducibility of GSH, high GSH content in tumor cells, and the NADPH depletion when GSSH is reduced to GSH, we extensively report the design principles, effect, and potential problems of various nano-drugs in diabetes, cancer, nervous system diseases, fluorescent probes, imaging, and food. These studies make full use of the physiological and pathological value of GSH and develop excellent design methods of nano-drugs related to GSH, which shows important scientific significance and prominent application value for the related diseases research that GSH participates in or responds to.

2021 ◽  
Naruki Yoshikawa ◽  
Kentaro Rikimaru ◽  
Kazuki Yamamoto

Many computer-aided drug design (CADD) methods using deep learning have recently been proposed to explore the chemical space toward novel scaffolds efficiently. However, there is a tradeoff between the ease of generating novel structures and the chemical feasibility of structural formulas. To overcome the limitations of computational filtering, we have implemented a web application that allows easy compound sanitization by humans. The application is available at https://sanitizer.chemical.space/.

2021 ◽  
Vol 28 ◽  
Yu-He Yang ◽  
Jia-Shu Wang ◽  
Shi-Shi Yuan ◽  
Meng-Lu Liu ◽  
Wei Su ◽  

: Protein-ligand interactions are necessary for majority protein functions. Adenosine-5’-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is cost-ineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.

Nanxin Liu ◽  
Qinhua Chen ◽  
Qingqing Zhang ◽  
Jin Wang ◽  
Ru Si ◽  

: Cancer is the second leading cause of human death after cardiovascular disease, and the most used drugs in clinics are cytotoxic agents. However, these drugs have some inherent disadvantages, such as the risk of toxicity, low selectivity, poor solubility, and so on. To overcome these shortcomings, a variety of drug delivery strategies based on prodrugs have been developed. The application of drug delivery systems can optimize ADME properties of cytotoxic agents and improve their selectivity at the target, thereby greatly enhancing the anticancer effect in clinics. At present, it has become mainstream in drug design. This review systematically summarized the studies of prodrug-based drug delivery systems over the past five to ten years, according to four aspects, solubility, controlled release, in situ concentration, and targeting.

2021 ◽  
Vol 118 (37) ◽  
pp. e2111173118
Lauren E. Stopfer ◽  
Aaron S. Gajadhar ◽  
Bhavin Patel ◽  
Sebastien Gallien ◽  
Dennie T. Frederick ◽  

Absolute quantification measurements (copies per cell) of peptide major histocompatibility complex (pMHC) antigens are necessary to inform targeted immunotherapy drug design; however, existing methods for absolute quantification have critical limitations. Here, we present a platform termed SureQuant-IsoMHC, utilizing a series of pMHC isotopologues and internal standard-triggered targeted mass spectrometry to generate an embedded multipoint calibration curve to determine endogenous pMHC concentrations for a panel of 18 tumor antigens. We apply SureQuant-IsoMHC to measure changes in expression of our target panel in a melanoma cell line treated with a MEK inhibitor and translate this approach to estimate antigen concentrations in melanoma tumor biopsies.

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