Page 136 - ITPS-7-2
P. 136
INNOSC Theranostics and
Pharmacological Sciences PI3K-α inhibitors for cancer immunotherapy
molecular docking of thiazolidinedione based benzene 83. Chen D, Oezguen N, Urvil P, Ferguson C, Dann SM,
sulphonamide derivatives containing pyrazole core as Savidge TC. Regulation of protein-ligand binding affinity by
potential anti-diabetic agents. Bioorg Chem. 2018;76:98-112. hydrogen bond pairing. Sci Adv. 2016;2(3):e1501240.
doi: 10.1016/j.bioorg.2017.11.010 doi: 10.1126/sciadv.1501240
73. Mechelke M, Habeck M. Robust probabilistic superposition 84. Bulusu G, Desiraju GR. Strong and weak hydrogen bonds in
and comparison of protein structures. BMC Bioinformatics. protein-ligand recognition. J Indian Inst Sci. 2020;100:31-41.
2010;11:363. doi: 10.1007/s41745-019-00141-9
doi: 10.1186/1471-2105-11-363 85. Jurczyk J, Woo J, Kim SF, Dherange BD, Sarpong R, Levin
74. Darling HS. Do you have a standard way of interpreting MD. Single-atom logic for heterocycle editing. Nat Synth.
the standard deviation? A narrative review. Cancer Res Stat 2022;1:352-364.
Treat. 2022;5(4):728-733. doi: 10.1038/s44160-022-00052-1
doi: 10.4103/crst.crst_284_22 86. Barnes-Seeman D, Beck J, Springer C. Fluorinated
75. Lee DK, In J, Lee S. Standard deviation and standard error of compounds in medicinal chemistry: Recent applications,
the mean. Korean J Anesthesiol. 2015;68(3):220-223. synthetic advances and matched-pair analyses. Curr Top
Med Chem. 2014;14(7):855-864.
doi: 10.4097/kjae.2015.68.3.220
doi: 10.2174/1568026614666140202204242
76. Zhang D. A coefficient of determination for generalized
linear models. Am Stat. 2017;71(4):310-316. 87. Chandra G, Singh DV, Mahato GK, Patel S. Fluorine-a small
magic bullet atom in the drug development: Perspective to
doi: 10.1080/00031305.2016.1256839 FDA approved and COVID-19 recommended drugs. Chem
77. Prinz F, Schlange T, Asadullah K. Believe it or not: How Zvesti. 2023;77, 4085-4106.
much can we rely on published data on potential drug doi: 10.1007/s11696-023-02804-5
targets? Nat Rev Drug Discov. 2011;10:712.
88. U.S. Food and Drugs Administrations: Novel Drug Approvals
doi: 10.1038/nrd3439-c1 for 2021; 2021. Available from: https://www.fda.gov/
78. Jawarkar RD, Bakal RL, Khatale PN, et al. QSAR, drugs/new-drugs-fda-cders-new-molecular-entities-
pharmacophore modeling and molecular docking studies and-new-therapeutic-biological-products/novel-drug-
to identify structural alerts for some nitrogen heterocycles approvals-2021 [Last accessed on 2023 Sep 11].
as dual inhibitor of telomerase reverse transcriptase and 89. Pal S, Chandra G, Patel S, Singh S. Fluorinated nucleosides:
human telomeric G-quadruplex DNA. Futur J Pharm Sci. Synthesis, modulation in conformation and therapeutic
2021;7(1):231. application. Chem Rec. 2022;22:e202100335.
doi: 10.1186/s43094-021-00380-7 doi: 10.1002/tcr.202100335
79. Tropsha A, Cho SJ. Perspectives in Drug Discovery and 90. Shet H, Sahu R, Sanghvi YS, Kapdi AR. Strategies for the
Design. Vol. 12. United States: Springer; 1998. p. 57-69. synthesis of fluorinated nucleosides, nucleotides and
oligonucleotides. Chem Rec. 2022;22:e202200066.
doi: 10.1023/a:1017017601586
doi: 10.1002/tcr.202200066
80. Consonni V, Ballabio D, Todeschini R. Comments on the
definition of the Q2 Parameter for QSAR Validation. J Chem 91. Grygorenko OO, Melnykov KP, Holovach S, Demchuk O.
Inf Model. 2009;49(7):1669-1678. Fluorinated cycloalkyl building blocks for drug discovery.
ChemMedChem. 2022;17:e202200365.
doi: 10.1021/ci900115y
doi: 10.1002/cmdc.202200365
81. Nguyen XS, Mouaddib AI, Nguyen TP. Hierarchical gaussian
descriptor based on local pooling for action recognition. 92. Jena S, Dutta J, Tulsiyan KD, Sahu AK, Choudhury SS,
Mach Vis Appl. 2019;30(2):321-343. Biswal HS. Noncovalent interactions in proteins and nucleic
acids: Beyond hydrogen bonding and π-stacking. Chem Soc
doi: 10.1007/s00138-018-0989-9 Rev. 2022;51:4261-4286.
82. Raschka S, Wolf AJ, Bemister-Buffington J, Kuhn LA. doi: 10.1039/d2cs00133k
Protein-ligand interfaces are polarized: Discovery of a
strong trend for intermolecular hydrogen bonds to favor 93. Hirota S, Lin YW. Design of artificial metalloproteins/
donors on the protein side with implications for predicting metalloenzymes by tuning noncovalent interactions. J Biol
and designing ligand complexes. J Comput Aided Mol Des. Inorg Chem. 2018;23:7-25.
2018;32(4):511-528. doi: 10.1007/s00775-017-1506-8
doi: 10.1007/s10822-018-0105-2 94. Yunta MJR. It is important to compute intramolecular
Volume 7 Issue 2 (2024) 26 doi: 10.36922/itps.2340

