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Статті в журналах з теми "Ab initio prediction"

Автор: Grafiati

Опубліковано: 11 березня 2023

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1

Hardin, Corey, Taras V. Pogorelov, and Zaida Luthey-Schulten. "Ab initio protein structure prediction." Current Opinion in Structural Biology 12, no. 2 (April 2002): 176–81. http://dx.doi.org/10.1016/s0959-440x(02)00306-8.

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2

Derreumaux, Philippe. "Ab initio polypeptide structure prediction." Theoretical Chemistry Accounts: Theory, Computation, and Modeling (Theoretica Chimica Acta) 104, no. 1 (May 12, 2000): 1–6. http://dx.doi.org/10.1007/s002149900095.

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3

Leusen, Frank J. J. "Ab initio prediction of polymorphs." Journal of Crystal Growth 166, no. 1-4 (September 1996): 900–903. http://dx.doi.org/10.1016/0022-0248(96)00099-1.

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4

Popelier, Paul. "pKa prediction from ab initio calculations." Research Outreach, no. 109 (August 30, 2019): 90–93. http://dx.doi.org/10.32907/ro-109-9093.

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5

van Eijck, B. P. "Ab Initio Prediction of Crystal Structures." Acta Crystallographica Section A Foundations of Crystallography 56, s1 (August 25, 2000): s3. http://dx.doi.org/10.1107/s0108767300021188.

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6

Shang, Bo, Lan-Feng Yuan, Xiao Cheng Zeng, and Jinlong Yang. "Ab Initio Prediction of Amorphous B84." Journal of Physical Chemistry A 114, no. 6 (February 18, 2010): 2245–49. http://dx.doi.org/10.1021/jp907976y.

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7

Weinreich, Jan, Dominik Lemm, Guido Falk von Rudorff, and O. Anatole von Lilienfeld. "Ab initio machine learning of phase space averages." Journal of Chemical Physics 157, no. 2 (July 14, 2022): 024303. http://dx.doi.org/10.1063/5.0095674.

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Анотація:

Equilibrium structures determine material properties and biochemical functions. We here propose to machine learn phase space averages, conventionally obtained by ab initio or force-field-based molecular dynamics (MD) or Monte Carlo (MC) simulations. In analogy to ab initio MD, our ab initio machine learning (AIML) model does not require bond topologies and, therefore, enables a general machine learning pathway to obtain ensemble properties throughout the chemical compound space. We demonstrate AIML for predicting Boltzmann averaged structures after training on hundreds of MD trajectories. The AIML output is subsequently used to train machine learning models of free energies of solvation using experimental data and to reach competitive prediction errors (mean absolute error ∼ 0.8 kcal/mol) for out-of-sample molecules—within milliseconds. As such, AIML effectively bypasses the need for MD or MC-based phase space sampling, enabling exploration campaigns of Boltzmann averages throughout the chemical compound space at a much accelerated pace. We contextualize our findings by comparison to state-of-the-art methods resulting in a Pareto plot for the free energy of solvation predictions in terms of accuracy and time.

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8

Nishimura, T., S. Nakamura, and K. Shimizu. "Application of ab initio prediction of protein structures." Seibutsu Butsuri 43, supplement (2003): S33. http://dx.doi.org/10.2142/biophys.43.s33_5.

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9

Gdanitz, Robert J. "Ab initio prediction of molecular crystal structures." Current Opinion in Solid State and Materials Science 3, no. 4 (August 1998): 414–18. http://dx.doi.org/10.1016/s1359-0286(98)80054-5.

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10

Stanke, M., O. Keller, I. Gunduz, A. Hayes, S. Waack, and B. Morgenstern. "AUGUSTUS: ab initio prediction of alternative transcripts." Nucleic Acids Research 34, Web Server (July 1, 2006): W435—W439. http://dx.doi.org/10.1093/nar/gkl200.

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11

Derreumaux, Philippe. "ChemInform Abstract: Ab initio Polypeptide Structure Prediction." ChemInform 31, no. 49 (December 5, 2000): no. http://dx.doi.org/10.1002/chin.200049293.

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12

Pierri, Ciro Leonardo, Anna De Grassi, and Antonio Turi. "Lattices for ab initio protein structure prediction." Proteins: Structure, Function, and Bioinformatics 73, no. 2 (April 23, 2008): 351–61. http://dx.doi.org/10.1002/prot.22070.

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13

Quintas-Sánchez, Ernesto, and Richard Dawes. "Spectroscopy and Scattering Studies Using Interpolated Ab Initio Potentials." Annual Review of Physical Chemistry 72, no. 1 (April 20, 2021): 399–421. http://dx.doi.org/10.1146/annurev-physchem-090519-051837.

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Анотація:

The Born–Oppenheimer potential energy surface (PES) has come a long way since its introduction in the 1920s, both conceptually and in predictive power for practical applications. Nevertheless, nearly 100 years later—despite astonishing advances in computational power—the state-of-the-art first-principles prediction of observables related to spectroscopy and scattering dynamics is surprisingly limited. For example, the water dimer, (H2O)2, with only six nuclei and 20 electrons, still presents a formidable challenge for full-dimensional variational calculations of bound states and is considered out of reach for rigorous scattering calculations. The extremely poor scaling of the most rigorous quantum methods is fundamental; however, recent progress in development of approximate methodologies has opened the door to fairly routine high-quality predictions, unthinkable 20 years ago. In this review, in relation to the workflow of spectroscopy and/or scattering studies, we summarize progress and challenges in the component areas of electronic structure calculations, PES fitting, and quantum dynamical calculations.

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14

TITOV, IGOR I., and PAVEL S. VOROZHEYKIN. "AB INITIO HUMAN miRNA AND PRE-miRNA PREDICTION." Journal of Bioinformatics and Computational Biology 11, no. 06 (December 2013): 1343009. http://dx.doi.org/10.1142/s0219720013430099.

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MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that play an important role in post-transcriptional regulation of gene expression. In this paper, we present a web server for ab initio prediction of the human miRNAs and their precursors. The prediction methods are based on the hidden Markov Models and the context-structural characteristics. By taking into account the identified patterns of primary and secondary structures of the pre-miRNAs, a new HMM model is proposed and the existing context-structural Markov model is modified. The evaluation of the method performance has shown that it can accurately predict novel human miRNAs. Comparing with the existing methods we demonstrate that our method has a higher prediction quality both for human pre-miRNAs and miRNAs. The models have also showed good results in the prediction of the mouse miRNAs. The web server is available at http://wwwmgs.bionet.nsc.ru/mgs/programs/rnaanalys (mirror http://miRNA.at.nsu.ru ).

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15

Vangaveti, Sweta, Thom Vreven, Yang Zhang, and Zhiping Weng. "Integrating ab initio and template-based algorithms for protein–protein complex structure prediction." Bioinformatics 36, no. 3 (August 8, 2019): 751–57. http://dx.doi.org/10.1093/bioinformatics/btz623.

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Abstract Motivation Template-based and template-free methods have both been widely used in predicting the structures of protein–protein complexes. Template-based modeling is effective when a reliable template is available, while template-free methods are required for predicting the binding modes or interfaces that have not been previously observed. Our goal is to combine the two methods to improve computational protein–protein complex structure prediction. Results Here, we present a method to identify and combine high-confidence predictions of a template-based method (SPRING) with a template-free method (ZDOCK). Cross-validated using the protein–protein docking benchmark version 5.0, our method (ZING) achieved a success rate of 68.2%, outperforming SPRING and ZDOCK, with success rates of 52.1% and 35.9% respectively, when the top 10 predictions were considered per test case. In conclusion, a statistics-based method that evaluates and integrates predictions from template-based and template-free methods is more successful than either method independently. Availability and implementation ZING is available for download as a Github repository (https://github.com/weng-lab/ZING.git). Supplementary information Supplementary data are available at Bioinformatics online.

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16

Zhou, Hongyi, and Jeffrey Skolnick. "Ab Initio Protein Structure Prediction Using Chunk-TASSER." Biophysical Journal 93, no. 5 (September 2007): 1510–18. http://dx.doi.org/10.1529/biophysj.107.109959.

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17

Bonneau, Richard, Ingo Ruczinski, Jerry Tsai, and David Baker. "Contact order and ab initio protein structure prediction." Protein Science 11, no. 8 (August 2002): 1937–44. http://dx.doi.org/10.1110/ps.3790102.

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18

Bonneau, Richard, and David Baker. "Ab Initio Protein Structure Prediction: Progress and Prospects." Annual Review of Biophysics and Biomolecular Structure 30, no. 1 (June 2001): 173–89. http://dx.doi.org/10.1146/annurev.biophys.30.1.173.

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19

Dubey, Sandhya Parasnath, N. Gopalakrishna Kini, M. Sathish Kumar, and S. Balaji. "Ab initio protein structure prediction using GPU computing." Perspectives in Science 8 (September 2016): 645–47. http://dx.doi.org/10.1016/j.pisc.2016.06.046.

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20

Darby, James P., Mihails Arhangelskis, Athanassios D. Katsenis, Joseph M. Marrett, Tomislav Friščić, and Andrew J. Morris. "Ab Initio Prediction of Metal-Organic Framework Structures." Chemistry of Materials 32, no. 13 (June 2, 2020): 5835–44. http://dx.doi.org/10.1021/acs.chemmater.0c01737.

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21

Srinivasan, Rajgopal, and George D. Rose. "Ab initio prediction of protein structure using LINUS." Proteins: Structure, Function, and Genetics 47, no. 4 (May 2, 2002): 489–95. http://dx.doi.org/10.1002/prot.10103.

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22

Klepeis, J. L., and C. A. Floudas. "Ab initio prediction of helical segments in polypeptides." Journal of Computational Chemistry 23, no. 2 (December 20, 2001): 245–66. http://dx.doi.org/10.1002/jcc.10002.

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23

Karamertzanis, Panagiotis G., and Constantinos C. Pantelides. "Ab initio crystal structure prediction?I. Rigid molecules." Journal of Computational Chemistry 26, no. 3 (2004): 304–24. http://dx.doi.org/10.1002/jcc.20165.

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24

Pugh, William J., and Jonathan Hadgraft. "Ab initio prediction of human skin permeability coefficients." International Journal of Pharmaceutics 103, no. 2 (March 1994): 163–78. http://dx.doi.org/10.1016/0378-5173(94)90097-3.

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25

Shidong, Chu, Li Yingxia, Song Ni, and Guan Huashi. "Ab initio prediction of 29Si-NMR chemical shifts." Journal of Ocean University of Qingdao 1, no. 1 (April 2002): 63–65. http://dx.doi.org/10.1007/s11802-002-0033-5.

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26

Yuan, Xin, Yu Shao, and Christopher Bystroff. "Ab Initio Protein Structure Prediction Using Pathway Models." Comparative and Functional Genomics 4, no. 4 (2003): 397–401. http://dx.doi.org/10.1002/cfg.305.

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27

Megantara, Sandra, Mutakin Mutakin, and Jutti Levita. "PREDICTION OF LOG P AND SPECTRUM OF QUERCETINE, GLUCOSAMINE, AND ANDROGRAPHOLIDE AND ITS CORRELATION WITH LABORATORY ANALYSIS." International Journal of Pharmacy and Pharmaceutical Sciences 8, no. 11 (October 28, 2016): 33. http://dx.doi.org/10.22159/ijpps.2016v8i11.9101.

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Анотація:

Objective: This study was aimed to confirm the result of computational prediction of log P and spectrum (ultraviolet-visible, 1H-NMR, 13C-NMR) of quercetin, glucosamine and andrographolide with laboratory analysis.Methods: Quercetine, glucosamine and andrographolide, were downloaded from ChemSpider and were geometry optimised. Log P and spectrum were calculated and predicted and the data obtained were compared with laboratory results. The correlation was calculated by employing mean absolute deviation (MAD), mean square error (MSE), mean forecast error (MFE), and mean absolute percentage error (MAPE) parameters.Results: The smallest energy value of geometry optimisation was provided by ab initio method. Log P prediction showed good accuracy, with r-value 0.995 and p-value 0.05 respectively. The error parameters were: MAD 0.19; MSE 0.06; MFE 0.16, and MAPE 8.62%, respectively. Prediction of λ maximum by ab initio, semiempirical, and molecular mechanics were respectively: MAD 2.67, 6.67, and 28.67; MSE 8.67, 45.33, and 830; MFE 2.67, 6.67, and 28.67; and MAPE 1.10%, 2.79%, and 11.99%; r-value 0.997, 0.997, and 0.979; and p-value 0.044, 0.043, and 0.129. 1H-NMR and 13C-NMR spectra prediction were: MAD 0.73 and 1.58; MSE 1.15 and 7.41; MFE 0.27 and 0.69; MAPE 18.35% and 2.68%; r-value 0.942 and 0.986; and p-value 0.001 and 0.001.Conclusion: There is a positive correlation between computational ab initio calculation method with experimental results in predicting log P and spectrum of quercetine, glucosamine, and andrographolide.

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28

Šob, Mojmír, A. Kroupa, J. Pavlů, and J. Vřeštál. "Application of Ab Initio Electronic Structure Calculations in Construction of Phase Diagrams of Metallic Systems with Complex Phases." Solid State Phenomena 150 (January 2009): 1–28. http://dx.doi.org/10.4028/www.scientific.net/ssp.150.1.

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Ab initio electronic structure theory has achieved considerable reliability concerning predictions of physical and chemical properties and phenomena. It provides understanding of matter at the atomic and electronic scale with an unprecedented level of details and accuracy. In the present contribution, the electronic structure theory and state-of-the-art ab initio calculation methods in solids are briefly reviewed and the application of the calculated total energy differences between various phases (lattice stabilities) is illustrated on construction of phase diagrams by the CALPHAD (CALculation of PHAse Diagrams) method in systems containing phases with complex structures, as e.g. Laves phases or sigma phase. Particular examples include description of the Laves phases in the Cr-Nb, Cr-Ta and Cr-Zr systems, sigma-phase in the Fe-Cr system and prediction of the phase composition of ternary Fe-Cr-Mo system and super-austenitic steels. It is shown that the utilization of ab initio results introduces a solid basis of the energetics of systems with complex phases, allows to avoid unreliable estimates and extrapolations of Gibbs energies and brings more physics into the CALPHAD method.

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29

Prakoso, Nurcahyo Iman, Lukman Hakim, and Nuri Hidayati. "Molecular Modeling of An Analog Of Curcumin Compounds Pentagamavunon-0 (PGV-0) And Pentagamavunon-1 (PGV-1) Through Computational Chemistry Methods Ab-Initio HF/4-31G." Chemical 3, no. 1 (December 31, 2017): 28–39. http://dx.doi.org/10.20885/ijcr.vol2.iss1.art4.

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Анотація:

Breast cancer is the second largest number of cancer cases in Indonesia, after cervical cancer. The growth of these cancer cells can be prevented with compounds Pentagamavunon-0 (PGV-0) and Pentagamavunon-1 (PGV-1). This compound is an analog of curcumin compounds that have anti breast cancer activity. Modeling the structure of compound PGV-0 and PGV-1 through computational chemistry methods Ab-initio HF/4-31G could be used to predict the geometry and structure elucidation spectra associated with pharmacological activity such as anticancer compounds theoretically.This research involves modeling the structures and spectra prediction calculation compounds PGV-0 and PGV-1 by computational chemistry methods Ab-initio HF/4-31G, using Gaussian03W. The result using Ab-initio HF/4-31G method then compared with data from experimental geometry and the results of calculations with AM1.The results showed that computational chemistry methods Ab-initio HF/4-31G calculations give better results for modeling the structure compared semiempirik method AM1.

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30

Barua, Harsh, Anilkumar Gunnam, Balvant Yadav, Ashwini Nangia, and Nalini R. Shastri. "An ab initio molecular dynamics method for cocrystal prediction: validation of the approach." CrystEngComm 21, no. 47 (2019): 7233–48. http://dx.doi.org/10.1039/c9ce01436e.

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31

Xia, Qiong, Qi Xu, Bin Xu, and Lin Yi. "Ab initio prediction of thermoelectric performance of monolayer BiSbTe3." Journal of Solid State Chemistry 296 (April 2021): 121963. http://dx.doi.org/10.1016/j.jssc.2021.121963.

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32

Gao, Daquan, and Donald E. Williams. "Molecular packing groups and ab initio crystal-structure prediction." Acta Crystallographica Section A Foundations of Crystallography 55, no. 4 (July 1, 1999): 621–27. http://dx.doi.org/10.1107/s0108767398016134.

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Ab initio crystal structure prediction can proceed by minimization of the packing energy of Z independent molecules per cell, or alternatively by energy minimization taking one molecule as independent and Z-1 molecules as related by assumed space-group symmetry. In the former method, a large number of positional variables must be considered. In the latter method, a large number of space groups must be considered. An alternative, more efficient, procedure is proposed, where it is recognized that values of Z and the number of molecules in the asymmetric unit, Z′, impose restrictions on possible space groups. Examples of application of this method to crystal structure prediction are given.

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33

Caputo, Riccarda, and Adem Tekin. "Ab-initio crystal structure prediction. A case study: NaBH4." Journal of Solid State Chemistry 184, no. 7 (July 2011): 1622–30. http://dx.doi.org/10.1016/j.jssc.2011.05.006.

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34

Zheng, Ming-Jie, Izabela Szlufarska, and Dane Morgan. "Ab initio prediction of threshold displacement energies in ZrC." Journal of Nuclear Materials 471 (April 2016): 214–19. http://dx.doi.org/10.1016/j.jnucmat.2015.08.053.

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35

Derreumaux, Philippe. "Ab initio prediction of polypeptide structure from its sequence." Computer Physics Communications 121-122 (September 1999): 139–40. http://dx.doi.org/10.1016/s0010-4655(99)00299-4.

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36

Pulay, Peter, Geza Fogarasi, Xuefeng Zhou, and Patterson W. Taylor. "Ab initio prediction of vibrational spectra: A database approach." Vibrational Spectroscopy 1, no. 2 (December 1990): 159–65. http://dx.doi.org/10.1016/0924-2031(90)80030-8.

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37

Wong, Adrian T., and George B. Bacskay. "Ab initio prediction of the vibrational frequencies of N2S." Chemical Physics Letters 217, no. 1-2 (January 1994): 17–23. http://dx.doi.org/10.1016/0009-2614(93)e1351-g.

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38

Yousef, Mohamad, Tamer Abdelkader, and Khaled El-Bahnasy. "Performance comparison of ab initio protein structure prediction methods." Ain Shams Engineering Journal 10, no. 4 (December 2019): 713–19. http://dx.doi.org/10.1016/j.asej.2019.03.004.

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39

Fischer, Gad, and John P. Maier. "Ab initio prediction of the spectra of carbon cumulenes." Chemical Physics 223, no. 2-3 (November 1997): 149–58. http://dx.doi.org/10.1016/s0301-0104(97)00220-6.

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40

Ulmschneider, Jakob. "Ab-Initio Prediction of Antimicrobial Peptides Channels in Membranes." Biophysical Journal 114, no. 3 (February 2018): 377a. http://dx.doi.org/10.1016/j.bpj.2017.11.2086.

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41

Kromann, Jimmy C., Frej Larsen, Hadeel Moustafa, and Jan H. Jensen. "Prediction of pKa values using the PM6 semiempirical method." PeerJ 4 (August 11, 2016): e2335. http://dx.doi.org/10.7717/peerj.2335.

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The PM6 semiempirical method and the dispersion and hydrogen bond-corrected PM6-D3H+ method are used together with the SMD and COSMO continuum solvation models to predict pKa values of pyridines, alcohols, phenols, benzoic acids, carboxylic acids, and phenols using isodesmic reactions and compared to published ab initio results. The pKa values of pyridines, alcohols, phenols, and benzoic acids considered in this study can generally be predicted with PM6 and ab initio methods to within the same overall accuracy, with average mean absolute differences (MADs) of 0.6–0.7 pH units. For carboxylic acids, the accuracy (0.7–1.0 pH units) is also comparable to ab initio results if a single outlier is removed. For primary, secondary, and tertiary amines the accuracy is, respectively, similar (0.5–0.6), slightly worse (0.5–1.0), and worse (1.0–2.5), provided that di- and tri-ethylamine are used as reference molecules for secondary and tertiary amines. When applied to a drug-like molecule where an empirical pKa predictor exhibits a large (4.9 pH unit) error, we find that the errors for PM6-based predictions are roughly the same in magnitude but opposite in sign. As a result, most of the PM6-based methods predict the correct protonation state at physiological pH, while the empirical predictor does not. The computational cost is around 2–5 min per conformer per core processor, making PM6-based pKa prediction computationally efficient enough to be used for high-throughput screening using on the order of 100 core processors.

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42

Zagorac, J., D. Zagorac, M. Rosić, J. C. Schön, and B. Matović. "Structure prediction of aluminum nitride combining data mining and quantum mechanics." CrystEngComm 19, no. 35 (2017): 5259–68. http://dx.doi.org/10.1039/c7ce01039g.

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43

Escribano, Rafael, Pedro C. Gómez, Belén Maté, Germán Molpeceres, and Emilio Artacho. "Prediction of the near-IR spectra of ices by ab initio molecular dynamics." Physical Chemistry Chemical Physics 21, no. 18 (2019): 9433–40. http://dx.doi.org/10.1039/c9cp00857h.

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44

Carletta, A., J. Dubois, A. Tilborg, and J. Wouters. "Solid-state investigation on a new dimorphic substituted N-salicylidene compound: insights into its thermochromic behaviour." CrystEngComm 17, no. 18 (2015): 3509–18. http://dx.doi.org/10.1039/c5ce00283d.

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45

Liu, J. B., R. F. Zhang, and B. X. Liu. "Comparative study of nonequilibrium phase of A3B and AB3 types in the Ni–Mo system by first principles and thermodynamic calculations." Journal of Materials Research 17, no. 10 (October 2002): 2720–26. http://dx.doi.org/10.1557/jmr.2002.0393.

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In the Ni–Mo system, two possible nonequilibrium states located near Ni3Mo were predicted through calculation of the total energies of eight possible structures of the A3B type as a function of their lattice constant(s) by applying the Vienna ab initio simulation package. Comparatively, Gibbs free energy diagrams were constructed from Miedema's model and, in general, the calculated results were in accordance with the prediction from ab initio calculation. Besides, the prediction of one of the nonequilibrium states was in agreement with the fact that a metastable hexagonal-close-packed Ni3Mo phase was indeed observed in Ni75Mo25 multilayer films upon thermal annealing, and calculated lattice constants matched well with those determined by diffraction analysis in experiments as well.

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46

Moran, Robert F., David McKay, Chris J. Pickard, Andrew J. Berry, John M. Griffin, and Sharon E. Ashbrook. "Hunting for hydrogen: random structure searching and prediction of NMR parameters of hydrous wadsleyite." Physical Chemistry Chemical Physics 18, no. 15 (2016): 10173–81. http://dx.doi.org/10.1039/c6cp01529h.

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47

Han, Yanqiang, Hongyuan Luo, Qianqian Lu, Zeying Liu, Jinyun Liu, Jiarui Zhang, Zhiyun Wei, and Jinjin Li. "Quantum Mechanical-Based Stability Evaluation of Crystal Structures for HIV-Targeted Drug Cabotegravir." Molecules 26, no. 23 (November 26, 2021): 7178. http://dx.doi.org/10.3390/molecules26237178.

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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.

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Jouyban, Abolghasem. "Review of the Cosolvency Models for Predicting Drug Solubility in Solvent Mixtures: An Update." Journal of Pharmacy & Pharmaceutical Sciences 22 (September 23, 2019): 466–85. http://dx.doi.org/10.18433/jpps30611.

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The cosolvency models frequently used in solubility data modeling of drugs in mixed solvents were reviewed and their accuracies for calculating the solubility of solutes were briefly discussed. The models could be used either for correlation of the generated solubility data with temperature, solvent composition etc or for prediction of unmeasured solubility data using interpolation/extrapolation technique. Concerning the correlation results employing a given number of independent variables, the accuracies of the investigated models were comparable, since they could be converted to a single mathematical form, however, the accuracies were decreased when models emplyed more independent variables. The accurate correlative models could be employed for prediction purpose and/or screening the experimental solubility data to detect possible outliers. With regard to prediction results, the best predictions were made using the cosolvency models trained by a minimum number of experimental data points and an ab initio accurate prediction is not possible so far and further mathematical efforts are needed to provide such a tool. To connect this gap between available accurate correlative models with the ab initio predictive model, the generally trained models for calculating the solubility of various drugs in different binary mixtures, various drugs in a given binary solvent and also a given drug in various binary solvents at isothermal condition and/or different temperatures were reported. Available accuracy criteria used in the recent publications were reviewed including mean percentage deviation (MPD). The MPD for correlative models is 1-10% whereas the corresponding range for predictive models is 10-80% depend on the model capability and the number of independent variables employed by the model. This is an update for a review article published in this journal in 2008.

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49

Siqueira, Lucas, and Sandra Venske. "Ab Initio Protein Structure Prediction Using Evolutionary Approach: A Survey." Revista de Informática Teórica e Aplicada 28, no. 2 (August 29, 2021): 11–24. http://dx.doi.org/10.22456/2175-2745.111993.

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Protein Structure Prediction (PSP) problem is to determine the three-dimensional structure of a protein only from its primary structure. Misfolding of a protein causes human diseases. Thus, the knowledge of the structure and functionality of proteins, combined with the prediction of their structure is a complex problem and a challenge for the area of computational biology. The metaheuristic optimization algorithms are naturally applicable to support in solving NP-hard problems.These algorithms are bio-inspired, since they were designed based on procedures found in nature, such as the successful evolutionary behavior of natural systems. In this paper, we present a survey on methods to approach the \textit{ab initio} protein structure prediction based on evolutionary computing algorithms, considering both single and multi-objective optimization. An overview of the works is presented, with some details about which characteristics of the problem are considered, as well as specific points of the algorithms used. A comparison between the approaches is presented and some directions of the research field are pointed out.

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Yamauchi, Kunihiko, Tamio Oguchi, and Silvia Picozzi. "Ab-initio Prediction of Magnetoelectricity in Infinite-Layer CaFeO2and MgFeO2." Journal of the Physical Society of Japan 83, no. 9 (September 15, 2014): 094712. http://dx.doi.org/10.7566/jpsj.83.094712.

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