molecular properties
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2022 ◽  
Vol 28 (2) ◽  
Author(s):  
Anahita Bakhshandeh ◽  
Fatemeh Ardestani ◽  
Hamid Reza Ghorbani ◽  
Masoud Darvish Ganji

2022 ◽  
Author(s):  
Eugen Hruska ◽  
Ariel Gale ◽  
Xiao Huang ◽  
Fang Liu

The availability of large, high-quality data sets is crucial for artificial intelligence design and discovery in chemistry. Despite the essential roles of solvents in chemistry, the rapid computational data set generation of solution-phase molecular properties at the quantum mechanical level of theory was previously hampered by the complicated simulation procedure. Software toolkits that can automate the procedure to set up high-throughput explicit-solvent quantum chemistry (QC) calculations for arbitrary solutes and solvents in an open-source framework are still lacking. We developed AutoSolvate, an open-source toolkit to streamline the workflow for QC calculation of explicitly solvated molecules. It automates the solvated-structure generation, force field fitting, configuration sampling, and the final extraction of microsolvated cluster structures that QC packages can readily use to predict molecular properties of interest. AutoSolvate is available through both a command line interface and a graphical user interface, making it accessible to the broader scientific community. To improve the quality of the initial structures generated by AutoSolvate, we investigated the dependence of solute-solvent closeness on solute/solvent identities and trained a machine learning model to predict the closeness and guide initial structure generation. Finally, we tested the capability of AutoSolvate for rapid data set curation by calculating the outer-sphere reorganization energy of a large data set of 166 redox couples, which demonstrated the promise of the AutoSolvate package for chemical discovery efforts.


2022 ◽  
Vol 12 ◽  
Author(s):  
Yinping Shi ◽  
Yuqing Hua ◽  
Baobao Wang ◽  
Ruiqiu Zhang ◽  
Xiao Li

Drug induced nephrotoxicity is a major clinical challenge, and it is always associated with higher costs for the pharmaceutical industry and due to detection during the late stages of drug development. It is desirable for improving the health outcomes for patients to distinguish nephrotoxic structures at an early stage of drug development. In this study, we focused on in silico prediction and insights into the structural basis of drug induced nephrotoxicity, based on reliable data on human nephrotoxicity. We collected 565 diverse chemical structures, including 287 nephrotoxic drugs on humans in the real world, and 278 non-nephrotoxic approved drugs. Several different machine learning and deep learning algorithms were employed for in silico model building. Then, a consensus model was developed based on three best individual models (RFR_QNPR, XGBOOST_QNPR, and CNF). The consensus model performed much better than individual models on internal validation and it achieved prediction accuracy of 86.24% external validation. The results of analysis of molecular properties differences between nephrotoxic and non-nephrotoxic structures indicated that several key molecular properties differ significantly, including molecular weight (MW), molecular polar surface area (MPSA), AlogP, number of hydrogen bond acceptors (nHBA), molecular solubility (LogS), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). These molecular properties may be able to play an important part in the identification of nephrotoxic chemicals. Finally, 87 structural alerts for chemical nephrotoxicity were mined with f-score and positive rate analysis of substructures from Klekota-Roth fingerprint (KRFP). These structural alerts can well identify nephrotoxic drug structures in the data set. The in silico models and the structural alerts could be freely accessed via https://ochem.eu/article/140251 and http://www.sapredictor.cn, respectively. We hope the results should provide useful tools for early nephrotoxicity estimation in drug development.


Author(s):  
Neeharika Yamsani ◽  
Raja Sundararajan

Aim: The study aims to design & synthesize novel thiazole derivatives as potent antitubercular agents with minimal side effects. Background: The emergence and rapid spread of multi-drug resistant infectious microbial flora embracing a variety of bacterial as well as mycobacterium strains are causing a threat to public health worldwide. Objective: Owing to the importance, we designed compounds with thiazole functionality coupled with Schiff base and thiosemicarbazide, predicted the molecular properties and antitubercular potency of designed compounds by the in-silico method, and synthesized fifteen novel thiazole analogs, characterized and tested in vivo antitubercular, antibacterial and antioxidant potencies. Methods: Molinspiration online tool was used to predict the molecular properties and molecular docking was used to predict the antitubercular potency. FT-IR, 1H-NMR, 13C-NMR, Mass spectroscopy and bases of elemental analysis are employed to confirm the structure of compounds. 10-Fold serial dilution method, agar streak dilution test and DPPH radical scavenging methods are used to estimate antitubercular, antibacterial and antioxidant potency of title analogs, respectively. Results: Multi-step synthesis was used to synthesize a variety of novel thiazole derivatives coupled with Schiff base and thiosemicarbazide. Synthesized title compounds displayed a varying degree of antitubercular, antibacterial and antioxidant activities (mild to good). The title compounds possessing deactivating group exhibited superior activities than activating group, while unsubstituted analogs displayed intermediate activities. In addition, para-substituted analogs showed slightly higher activity than the corresponding meta substituted analogs. Conclusion: Among fifteen tested title compounds, the potent compound of this series was found to be 1-(4-nitrobenzylidene)-4-(4-(4-methoxyphenyl)thiazol-2-yl)thiosemicarbazide (BTS14), which might be extended as a novel class of antitubercular and antibacterial agents.


2022 ◽  
Author(s):  
Alexander S. Sharipov ◽  
Boris I. Loukhovitski ◽  
Ekaterina E. Loukhovitskaya

2022 ◽  
Vol 88 ◽  
pp. 104900
Author(s):  
Juping Yu ◽  
Meng Ye ◽  
Kaidong Li ◽  
Feng Wang ◽  
Xuexia Shi ◽  
...  

Marine Drugs ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 42
Author(s):  
Claire Laguionie-Marchais ◽  
A. Louise Allcock ◽  
Bill J. Baker ◽  
Ellie-Ann Conneely ◽  
Sarah G. Dietrick ◽  
...  

Phylum Cnidaria has been an excellent source of natural products, with thousands of metabolites identified. Many of these have not been screened in bioassays. The aim of this study was to explore the potential of 5600 Cnidaria natural products (after excluding those known to derive from microbial symbionts), using a systematic approach based on chemical space, drug-likeness, predicted toxicity, and virtual screens. Previous drug-likeness measures: the rule-of-five, quantitative estimate of drug-likeness (QED), and relative drug likelihoods (RDL) are based on a relatively small number of molecular properties. We augmented this approach using reference drug and toxin data sets defined for 51 predicted molecular properties. Cnidaria natural products overlap with drugs and toxins in this chemical space, although a multivariate test suggests that there are some differences between the groups. In terms of the established drug-likeness measures, Cnidaria natural products have generally lower QED and RDL scores than drugs, with a higher prevalence of metabolites that exceed at least one rule-of-five threshold. An index of drug-likeness that includes predicted toxicity (ADMET-score), however, found that Cnidaria natural products were more favourable than drugs. A measure of the distance of individual Cnidaria natural products to the centre of the drug distribution in multivariate chemical space was related to RDL, ADMET-score, and the number of rule-of-five exceptions. This multivariate similarity measure was negatively correlated with the QED score for the same metabolite, suggesting that the different approaches capture different aspects of the drug-likeness of individual metabolites. The contrasting of different drug similarity measures can help summarise the range of drug potential in the Cnidaria natural product data set. The most favourable metabolites were around 210–265 Da, quite often sesquiterpenes, with a moderate degree of complexity. Virtual screening against cancer-relevant targets found wide evidence of affinities, with Glide scores <−7 in 19% of the Cnidaria natural products.


2021 ◽  
Author(s):  
Aashutosh Vihani ◽  
Maira H Nagai ◽  
Conan Juan ◽  
Claire A de March ◽  
Xiaoyang S Hu ◽  
...  

Olfactory receptors (ORs) constitute the largest multi-gene family in the mammalian genome, with hundreds to thousands of loci in humans and mice respectively. The rapid expansion of this massive family of genes has been generated by numerous duplication and diversification events throughout evolutionary history. This size, similarity, and diversity has made it challenging to define the principles by which ORs encode olfactory stimuli. Here, we performed a broad surveying of OR responses, using an in vivo strategy, against a diverse panel of odorants. We then used the resulting interaction profiles to uncover relationships between OR responses, odorants, odor molecular properties, and OR sequences. Our data and analyses revealed that ORs generally exhibited sparse tuning towards odorants and their molecular properties. Odor molecular property similarity between pairs of odorants was informative of odor response similarity. Finally, ORs sharing response to an odorant possessed amino acids at poorly conserved sites that exhibited both, predictive power towards odorant selectivity and convergent evolution. The localization of these residues occurred primarily at the interface of the upper halves of the transmembrane domains, implying that canonical positions govern odor selectivity across ORs. Altogether, our results provide a basis for translating odorants into receptor neuron responses for the unraveling of mammalian odor coding.


2021 ◽  
Vol 8 ◽  
Author(s):  
Taichi Tsuneishi ◽  
Masataka Takahashi ◽  
Masaki Tsujimura ◽  
Keiichi Kojima ◽  
Hiroshi Ishikita ◽  
...  

Rhodopsins act as photoreceptors with their chromophore retinal (vitamin-A aldehyde) and they regulate light-dependent biological functions. Archaerhodopsin-3 (AR3) is an outward proton pump that has been widely utilized as a tool for optogenetics, a method for controlling cellular activity by light. To characterize the retinal binding cavity of AR3, we synthesized a dimethyl phenylated retinal derivative, (2E,4E,6E,8E)-9-(2,6-Dimethylphenyl)-3,7-dimethylnona-2,4,6,8-tetraenal (DMP-retinal). QM/MM calculations suggested that DMP-retinal can be incorporated into the opsin of AR3 (archaeopsin-3, AO3). Thus, we introduced DMP-retinal into AO3 to obtain the non-natural holoprotein (AO3-DMP) and compared some molecular properties with those of AO3 with the natural A1-retinal (AO3-A1) or AR3. Light-induced pH change measurements revealed that AO3-DMP maintained slow outward proton pumping. Noteworthy, AO3-DMP had several significant changes in its molecular properties compared with AO3-A1 as follows; 1) spectroscopic measurements revealed that the absorption maximum was shifted from 556 to 508 nm and QM/MM calculations showed that the blue-shift was due to the significant increase in the HOMO-LUMO energy gap of the chromophore with the contribution of some residues around the chromophore, 2) time-resolved spectroscopic measurements revealed the photocycling rate was significantly decreased, and 3) kinetical spectroscopic measurements revealed the sensitivity of the chromophore binding Schiff base to attack by hydroxylamine was significantly increased. The QM/MM calculations show that a cavity space is present at the aromatic ring moiety in the AO3-DMP structure whereas it is absent at the corresponding β-ionone ring moiety in the AO3-A1 structure. We discuss these alterations of the difference in interaction between the natural A1-retinal and the DMP-retinal with binding cavity residues.


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