The timely arrival of novel materials plays a key role in bringing advances to society, as the pace at which major technological breakthroughs take place is usually dictated by the discovery rate at ...which novel materials are identified within chemical space. High‐throughput experimentation and computation strategy, now widely considered as a watershed in accelerating the discovery and optimization of novel materials in virtually every field, enables simultaneous screening, synthesis and characterization of large arrays of different material classes toward identification of the lead candidates for given system and targeted application. However, the ability to acquire data, through the continued advancement of automation platforms and workflows especially in the field of battery research and development, often outpaces the ability to optimally leverage obtained data for improved decision‐making. Closing this gap inevitably calls for adapted algorithms, development of reliable predictive models and enhanced integration with machine learning, deep learning, and artificial intelligence. This Review aims to highlight state‐of‐the‐art achievements along with an assessment of current and future challenges as well as resulting perspectives toward accelerated development of advanced battery electrolytes and their interfaces.
Accelerated discovery and development of novel battery materials, especially electrolytes and their interfaces within battery chemistries of interest, unquestionably call for complementary and synergistic high‐throughput experimentation (HTE) and a high‐throughput virtual screening approach. Well‐designed HTE workflows result in a wealth of experimental datasets as a solid foundation for enhanced technical decision making based on adapted machine learning‐based algorithms and high‐performance computing tools.
In December 2019, COVID-19 epidemic was described in Wuhan, China, and the infection has spread widely affecting hundreds of thousands. Herein, an effort was made to identify commercially available ...drugs in order to repurpose them against coronavirus by the means of structure-based virtual screening. In addition, ZINC15 library was used to identify novel leads against main proteases. Human TMPRSS2 3D structure was first generated using homology modeling approach. Our molecular docking study showed four potential inhibitors against Mpro enzyme, two available drugs (Talampicillin and Lurasidone) and two novel drug-like compounds (ZINC000000702323 and ZINC000012481889). Moreover, four promising inhibitors were identified against TMPRSS2; Rubitecan and Loprazolam drugs, and compounds ZINC000015988935 and ZINC000103558522. ADMET profile showed that the hits from our study are safe and drug-like compounds. Furthermore, molecular dynamic (MD) simulation and binding free energy calculation using the MM-PBSA method was performed to calculate the interaction energy of the top-ranked drugs.
Communicated by Ramaswamy H. Sarma
Calixarenes are displaying great potential for the development of new drug delivery systems, diagnostic imaging, biosensing devices and inhibitors of biological processes. In particular, calixarene ...derivatives are able to interact with many different enzymes and function as inhibitors. By screening of the potential drug target database (PDTD) with a reverse docking procedure, we identify and discuss a selection of 100 proteins that interact strongly with calix4arene. We also discover that leucine (23.5 %), isoleucine (11.3 %), phenylalanines (11.3 %) and valine (9.5 %) are the most frequent binding residues followed by hydrophobic cysteines and methionines and aromatic histidines, tyrosines and tryptophanes. Top binders are peroxisome proliferator‐activated receptors that already are targeted by commercial drugs, demonstrating the practical interest in calix4arene. Nuclear receptors, potassium channel, several carrier proteins, a variety of cancer‐related proteins and viral proteins are prominent in the list. It is concluded that calix4arene, which is characterized by facile access, well‐defined conformational characteristics, and ease of functionalization at both the lower and higher rims, could be a potential lead compound for the development of enzyme inhibitors and theranostic platforms.
A rational approach, was applied to design protein inhibitors using the calix4arene as molecular scaffold. By screening a database of druggable proteins with a reverse docking procedure, we identified and discussed many proteins of pharmaceutical interest.
The androgen receptor (AR, Uniprot: P10275) signaling plays a key role in the progression of prostate cancer, various AR-related ligands have been reported to treat prostate cancer. However, some ...resistance mechanisms limited the treating effect of these ligands. Since DBD binding or the allosteric binding sites in LBD of AR may allow the circumvention of some drug resistance mechanisms, anti-resistance is expected especially through the NTD (N-terminal domain) targeting. What's more, studies have shown that compounds including EPI-001 and its derivatives which bind to the Tau-5 region on NTD could be promising molecules for AR-based therapeutics. Herein, we employed aMD (accelerated molecular dynamics) simulation to fold Tau-5 unit proteins into native structure correctly. Subsequently, based on the predicted structural features of Tau-5, the virtual screening was conducted to discover new compounds targeting AR-NTD. We picked up 8 compounds (according to their docking scores and partly similar structural consists as known AR ligands) and analyzed their interaction with Tau-5, compared with the positive control EPI-001, four of the pick-up compounds showed better glide scores. Interestingly, although compound 8 had a lower docking score, it consisted of a similar component as the ligand EIQPN and the amide derivatives, this predicts that compound 8 has also the potential to be modified into an excellent AR-NTD binding molecule. These 8 compounds were all commercially available and could be tested to check whether there was a hit compound to bind the AR-NTD and to regulate its bio-activities. Together, this study described an in silico VLS approach to discover AR-NTD ligands and provided more choices for developing AR-targeted therapies.Communicated by Ramaswamy H. Sarma.
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•The DockThor-VS web server is a free platform for virtual screening for the scientific community that has been under continuous development since its first release in 2013.•In 2023, ...more than 27,000 jobs were submitted to the DockThor-VS web server, and we could track more than 330 citations to three of our methodological papers (published since 2020).•The DockThor suite has accuracy compared to the state-of-the-art protein–ligand docking methods for pose and affinity predictions.•We provided the ready-to-dock structures of relevant COVID-19-related structures, including clinically relevant non-synonym variations and distinct conformations representative of the flexibility of each receptor.•Recently, we added new COVID-19-related curated targets from SARS-CoV-2 (Nsp13, and Nsp3 - macro domain) and human host (TMPRSS2 and cathepsin B)•Recently, we included an updated database of FDA-approved drugs with protonation and tautomeric states within different pH ranges ready to be used in virtual screening experiments,•Recently, we include new datasets of compounds from natural sources ready to be used in virtual screening experiments.
The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein–ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein–ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.
High-throughput virtual screening (HTVS) is a leading biopharmaceutical technology that employs computational algorithms to uncover biologically active compounds from large-scale collections of ...chemical compound libraries. In addition, this method often leverages the precedence of screening focused libraries for assessing their binding affinities and improving physicochemical properties. Usually, developing a drug sometimes takes ages, and lessons are learnt from FDA-approved drugs. This screening strategy saves resources and time compared to laboratory testing in certain stages of drug discovery. Yet in-silico investigations remain challenging in some cases of drug discovery. For the last few decades, peptide-based drug discoveries have received remarkable momentum for several advantages over small molecules. Therefore, developing a high-fidelity HTVS platform for chemically versatile peptide libraries is highly desired. This review summarises the modern and frequently appreciated HTVS strategies for peptide libraries from 2011 to 2021. In addition, we focus on the software used for preparing peptide libraries, their screening techniques and shortcomings. An index of various HTVS methods reported here should assist researchers in identifying tools that could be beneficial for their peptide library screening projects.
This review delineates the scope of high-throughput virtual screening (HTVS) in peptide binder and drug discovery. It also discussed the software information for peptide library preparation and HTVS. The article concludes with a handful of examples of HTVS subjected to experimental validation. Display omitted
•Purpose of high-throughput virtual screen (HTVS) in peptide drug discovery. When to opt for HTVS over traditional HTS?•Milestone achieved and breakthrough with Virtual Screening Software (2011–2021).•Experimental target validation post-virtual peptide library screen and find confidence of in silico results.•Prospects and current limitations of HTVS.
Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial ...to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.
•There is an urgent need for the development of antimicrobial therapeutics to address the growing concern of AMR.•CADD techniques can screen large databases of compounds to identify potential antimicrobial candidates.•CADD provides insights into the discovery and development of new antimicrobial drugs, aiding in the fight against AMR.
Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in ...combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.
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•Computational techniques have proven to be essential in pre-clinical drug design.•Virtual screening is essential and more effective when used with other computational techniques such as MD and DFT.•There is direct link between the growth of theoretical research, multidisciplinary research and increase in CADD-linked drugs.•Experimental validation is crucial in relation to computational modelling.
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