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  • 1
    In: Molecules, MDPI AG, Vol. 26, No. 23 ( 2021-11-26), p. 7178-
    Abstract: The long-acting parenteral formulation of the HIV integrase inhibitor cabotegravir (GSK744) is currently being developed to prevent HIV infections, benefiting from infrequent dosing and high efficacy. The crystal structure can affect the bioavailability and efficacy of cabotegravir. However, the stability determination of crystal structures of GSK744 have remained a challenge. Here, we introduced an ab initio protocol to determine the stability of the crystal structures of pharmaceutical molecules, which were obtained from crystal structure prediction process starting from the molecular diagram. Using GSK744 as a case study, the ab initio predicted that Gibbs free energy provides reliable further refinement of the predicted crystal structures and presents its capability for becoming a crystal stability determination approach in the future. The proposed work can assist in the comprehensive screening of pharmaceutical design and can provide structural predictions and stability evaluation for pharmaceutical crystals.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2008644-1
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  • 2
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2023
    In:  Briefings in Bioinformatics Vol. 24, No. 1 ( 2023-01-19)
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 24, No. 1 ( 2023-01-19)
    Abstract: Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their applications in large systems. Here, we design a transfer-learning-based deep learning (TDL) protocol for effective FQM calculations (TDL-FQM) on proteins. By incorporating a transfer-learning algorithm into deep neural network (DNN), the TDL-FQM protocol is capable of performing calculations at any given accuracy using models trained from small datasets with high-precision and knowledge learned from large amount of low-level calculations. The high-level double-hybrid DFT functional and high-level quality of basis set is used in this work as a case study to evaluate the performance of TDL-FQM, where the selected 15 proteins are predicted to have a mean absolute error of 0.01 kcal/mol/atom for potential energy and an average root mean square error of 1.47 kcal/mol/$ {\rm A^{^{ \!\!\!o}}} $ for atomic forces. The proposed TDL-FQM approach accelerates the FQM calculation more than thirty thousand times faster in average and presents more significant benefits in efficiency as the size of protein increases. The ability to learn knowledge from one task to solve related problems demonstrates that the proposed TDL-FQM overcomes the limitation of standard DNN and has a strong power to predict proteins with high precision, which solves the challenge of high precision prediction in large chemical and biological systems.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    American Chemical Society (ACS) ; 2022
    In:  ACS Materials Letters Vol. 4, No. 8 ( 2022-08-01), p. 1436-1445
    In: ACS Materials Letters, American Chemical Society (ACS), Vol. 4, No. 8 ( 2022-08-01), p. 1436-1445
    Type of Medium: Online Resource
    ISSN: 2639-4979 , 2639-4979
    Language: English
    Publisher: American Chemical Society (ACS)
    Publication Date: 2022
    detail.hit.zdb_id: 3004109-0
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  • 4
    Online Resource
    Online Resource
    International Union of Crystallography (IUCr) ; 2022
    In:  Journal of Applied Crystallography Vol. 55, No. 5 ( 2022-10-01), p. 1247-1254
    In: Journal of Applied Crystallography, International Union of Crystallography (IUCr), Vol. 55, No. 5 ( 2022-10-01), p. 1247-1254
    Abstract: Drug molecules undergo changes to their intermolecular binding patterns under extreme conditions, leading to structural phase transitions which produce different polymorphs. Polymorphism of aspirin (acetylsalicylic acid), one of the most widely consumed medications, has attracted many scientists, chemists and pharmacologists to identify its stable polymorphs and phase transformations at ambient temperatures and pressures. Here, density functional theory at the ωB97XD/6-31G* functional level is utilized to calculate the lattice constants, volumes, Gibbs free energies, vibrational spectra, stabilities and phase transitions of aspirin forms I and II at different pressures and temperatures. These computations confirm that phase transformation occurs between these two forms of aspirin at higher pressures (from 3 to 5 GPa) and near room temperatures. Taking aspirin as a case study, this work can help design, produce and store drugs, guiding scientists, chemists and pharmacologists to perform further experiments.
    Type of Medium: Online Resource
    ISSN: 1600-5767
    Language: Unknown
    Publisher: International Union of Crystallography (IUCr)
    Publication Date: 2022
    detail.hit.zdb_id: 2020879-0
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  • 5
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2021
    In:  Briefings in Bioinformatics Vol. 22, No. 2 ( 2021-03-22), p. 1225-1231
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 22, No. 2 ( 2021-03-22), p. 1225-1231
    Abstract: The lack of a vaccine or any effective treatment for the aggressive novel coronavirus disease (COVID-19) has created a sense of urgency for the discovery of effective drugs. Several repurposing pharmaceutical candidates have been reported or envisaged to inhibit the emerging infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but their binding sites, binding affinities and inhibitory mechanisms are still unavailable. In this study, we use the ligand-protein docking program and molecular dynamic simulation to ab initio investigate the binding mechanism and inhibitory ability of seven clinically approved drugs (Chloroquine, Hydroxychloroquine, Remdesivir, Ritonavir, Beclabuvir, Indinavir and Favipiravir) and a recently designed α-ketoamide inhibitor (13b) at the molecular level. The results suggest that Chloroquine has the strongest binding affinity with 3CL hydrolase (Mpro) among clinically approved drugs, indicating its effective inhibitory ability for SARS-CoV-2. However, the newly designed inhibitor 13b shows potentially improved inhibition efficiency with larger binding energy compared with Chloroquine. We further calculate the important binding site residues at the active site and demonstrate that the MET 165 and HIE 163 contribute the most for 13b, while the MET 165 and GLN 189 for Chloroquine, based on residual energy decomposition analysis. The proposed work offers a higher research priority for 13b to treat the infection of SARS-CoV-2 and provides theoretical basis for further design of effective drug molecules with stronger inhibition.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2021
    In:  Briefings in Bioinformatics Vol. 22, No. 6 ( 2021-11-05)
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 22, No. 6 ( 2021-11-05)
    Abstract: Full-quantum mechanics (QM) calculations are extraordinarily precise but difficult to apply to large systems, such as biomolecules. Motivated by the massive demand for efficient calculations for large systems at the full-QM level and by the significant advances in machine learning, we have designed a neural network-based two-body molecular fractionation with conjugate caps (NN-TMFCC) approach to accelerate the energy and atomic force calculations of proteins. The results show very high precision for the proposed NN potential energy surface models of residue-based fragments, with energy root-mean-squared errors (RMSEs) less than 1.0 kcal/mol and force RMSEs less than 1.3 kcal/mol/Å for both training and testing sets. The proposed NN-TMFCC method calculates the energies and atomic forces of 15 representative proteins with full-QM precision in 10–100 s, which is thousands of times faster than the full-QM calculations. The computational complexity of the NN-TMFCC method is independent of the protein size and only depends on the number of residue species, which makes this method particularly suitable for rapid prediction of large systems with tens of thousands or even hundreds of thousands of times acceleration. This highly precise and efficient NN-TMFCC approach exhibits considerable potential for performing energy and force calculations, structure predictions and molecular dynamics simulations of proteins with full-QM precision.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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  • 7
    Online Resource
    Online Resource
    MDPI AG ; 2023
    In:  Molecules Vol. 28, No. 11 ( 2023-05-25), p. 4346-
    In: Molecules, MDPI AG, Vol. 28, No. 11 ( 2023-05-25), p. 4346-
    Abstract: Depression, a mental disorder that plagues the world, is a burden on many families. There is a great need for new, fast-acting antidepressants to be developed. N-methyl-D-aspartic acid (NMDA) is an ionotropic glutamate receptor that plays an important role in learning and memory processes and its TMD region is considered as a potential target to treat depression. However, due to the unclear binding sites and pathways, the mechanism of drug binding lacks basic explanation, which brings great complexity to the development of new drugs. In this study, we investigated the binding affinity and mechanisms of an FDA-approved antidepressant (S-ketamine) and seven potential antidepressants (R-ketamine, memantine, lanicemine, dextromethorphan, Ro 25-6981, ifenprodil, and traxoprodil) targeting the NMDA receptor by ligand–protein docking and molecular dynamics simulations. The results indicated that Ro 25-6981 has the strongest binding affinity to the TMD region of the NMDA receptor among the eight selected drugs, suggesting its potential effective inhibitory effect. We also calculated the critical binding-site residues at the active site and found that residues Leu124 and Met63 contributed the most to the binding energy by decomposing the free energy contributions on a per-residue basis. We further compared S-ketamine and its chiral molecule, R-ketamine, and found that R-ketamine had a stronger binding capacity to the NMDA receptor. This study provides a computational reference for the treatment of depression targeting NMDA receptors, and the proposed results will provide potential strategies for further antidepressant development and is a useful resource for the future discovery of fast-acting antidepressant candidates.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2008644-1
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  • 8
    In: SSRN Electronic Journal, Elsevier BV
    Type of Medium: Online Resource
    ISSN: 1556-5068
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
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  • 9
    In: Small Methods, Wiley
    Abstract: Deep learning has proven promising in biological and chemical applications, aiding in accurate predictions of properties such as atomic forces, energies, and material band gaps. Traditional methods with rotational invariance, one of the most crucial physical laws for predictions made by machine learning, have relied on Fourier transforms or specialized convolution filters, leading to complex model design and reduced accuracy and efficiency. However, models without rotational invariance exhibit poor generalization ability across datasets. Addressing this contradiction, this work proposes a rotationally invariant graph neural network, named RotNet, for accurate and accelerated quantum mechanical calculations that can overcome the generalization deficiency caused by rotations of molecules. RotNet ensures rotational invariance through an effective transformation and learns distance and angular information from atomic coordinates. Benchmark experiments on three datasets (protein fragments, electronic materials, and QM9) demonstrate that the proposed RotNet framework outperforms popular baselines and generalizes well to spatial data with varying rotations. The high accuracy, efficiency, and fast convergence of RotNet suggest that it has tremendous potential to significantly facilitate studies of protein dynamics simulation and materials engineering while maintaining physical plausibility.
    Type of Medium: Online Resource
    ISSN: 2366-9608 , 2366-9608
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2884448-8
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  • 10
    Online Resource
    Online Resource
    Elsevier BV ; 2021
    In:  Computational and Structural Biotechnology Journal Vol. 19 ( 2021), p. 4184-4191
    In: Computational and Structural Biotechnology Journal, Elsevier BV, Vol. 19 ( 2021), p. 4184-4191
    Type of Medium: Online Resource
    ISSN: 2001-0370
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 2694435-2
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