Page 108 - AJWEP-v22i3
P. 108

Sonsare, et al.

                   Machine-Learning  Model  to Partition  Soil  Organic   38.  Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK,
                   Carbon into its Centennially Stable and Active Fractions   Chavda  VP.  Artificial  intelligence  in  pharmaceutical
                   Based on Rock-Eval(r) Thermal  Analysis. Germany:    technology and drug delivery design.  Pharmaceutics.
                   European Geosciences Union; 2025.                    2023;15(7):1916.
                   doi: 10.5194/egusphere-egu24-11107                   doi: 10.3390/pharmaceutics15071916
                28.  Torralba-Sanchez  TL, Di  Toro DM,  Dmitrenko O,   39.  Han R, Yoon H, Kim G, Lee H, Lee Y. Revolutionizing
                   Murillo-Gelvez J, Tratnyek PG. Modeling the partitioning   medicinal  chemistry:  The  application  of  artificial
                   of anionic carboxylic and perfluoroalkyl carboxylic and   intelligence (AI) in early drug discovery. Pharmaceuticals
                   sulfonic acids to octanol and membrane lipid. Environ   (Basel). 2023;16(9):1259.
                   Toxicol Chem. 2023;42(11):2317-2328.                 doi: 10.3390/ph16091259
                   doi: 10.1002/etc.5716                            40.  Environment news futures. Asian J Water Environ Pollut.
                29.  Khawar MI, Mahmood A, Nabi D. Exploring  the  role   2024;21(5):95-98.
                   of  octanol-water  partition  coefficient  and  Henry’s  law      doi: 10.3233/AJW240064
                   constant in predicting the lipid-water partition coefficients   41.  Lallawmzuali G, Devi AS, Liana T, Hriatsaka V, Singh
                   of organic chemicals. Sci Rep. 2022;12(1):14936.     AP, Lalhriatpuia  C.  Assessment  of the  heavy  metal
                   doi: 10.1038/s41598-022-19452-6                      contaminations  of roadside  soil in  Aizawl,  Mizoram
                30.  Patel C, Roy D. Octanol-water partition coefficients of   (India):  An in-depth analysis utilising  advanced
                   fluorinated  drug  molecules  with  continuum  solvation   scientific methodologies. Asian J Water Environ Pollut.
                   models. J Phys Chem A. 2022;126(26):4185-4190.       2024;21(5):37-47.
                   doi: 10.1021/acs.jpca.2c02172                        doi: 10.3233/AJW240058
                31.  Baskaran S, Podagatlapalli  A, Sangion  A,  Wania F.   42.  Kaur I, Gulati A, Lamba PS, Jain A, Taneja H, Syal JS.
                   Predicting the temperature dependence of the octanol-air   Water quality assessment using machine  learning:
                   partition ratio: A new model for estimating $$\delta {U^{ \  A focus on coliform prediction in water. Asian J Water
                   circ}_{\text{OA}}}$$. J Solut Chem. 2023;52(1):51-69.  Environ Pollut. 2024;21(5):19-26.
                   doi: 10.1007/s10953-022-01214-7                      doi: 10.3233/AJW240056
                32.  Singh B, Crasto M, Ravi  K, Singh S. Pharmaceutical   43.  Xu L,  Dong Z, Fang L,  et  al. OrthoVenn2:  A  web
                   advances:  Integrating  artificial  intelligence  in  QSAR,   server for whole-genome  comparison and annotation
                   combinatorial  and green  chemistry  practices.  Intell   of orthologous clusters across multiple species. Nucleic
                   Pharm. 2024;2:598-608.                               Acids Res. 2019;47(W1):W52-W58.
                   doi: 10.1016/j.ipha.2024.05.005                      doi: 10.1093/nar/gkz333
                33.  Arab M, Faramarz  MG, Hashim K.  Applications  of   44.  Huerta-Cepas  J, Szklarczyk D, Heller  D,  et al.
                   computational and statistical models for optimizing the   EggNOG  5.0:  A  hierarchical,  functionally  and
                   electrochemical  removal  of  cephalexin  antibiotic  from   phylogenetically annotated orthology resource based on
                   water. Water (Switzerland). 2022;14(3):344.          5090 organisms and 2502 viruses.  Nucleic  Acids  Res.
                   doi: 10.3390/w14030344                               2019;47(D1):D309-D314.
                34.  Ågerstrand M, Berg C, Björlenius B, et al. Improving      doi: 10.1093/nar/gky1085
                   environmental risk assessment of human pharmaceuticals.   45.  Altenhoff AM, Vesztrocy AW, Bernard C, et al. OMA
                   Environ Sci Technol. 2015;49(9):5336-5345.           orthology in 2024: Improved prokaryote coverage,
                   doi: 10.1021/acs.est.5b00302                         ancestral and extant GO enrichment, a revamped synteny
                35.  Soares  TA, Nunes-Alves  A, Mazzolari  A, Ruggiu  F,   viewer and more in the OMA ecosystem. Nucleic Acids
                   Wei  GW,  Merz  K.  The  (Re)-evolution  of  quantitative   Res. 2024;52(D1):D513-D521.
                   structure-activity relationship (QSAR) studies propelled      doi: 10.1093/nar/gkad1020
                   by the surge of machine learning methods. J Chem Inf   46.  Zdobnov  EM, Kuznetsov  D,  Tegenfeldt  F,  Manni  M,
                   Model. 2022;62(22):5317-5320.                        Berkeley  M, Kriventseva EV. OrthoDB in 2020:
                   doi: 10.1021/acs.jcim.2c01422                        Evolutionary  and functional  annotations  of  orthologs.
                36.  Shen X,  Wang R, Xiong X,  et  al. Metabolic  reaction   Nucleic Acids Res. 2021;49(D1):D389-D393.
                   network-based  recursive  metabolite  annotation     doi: 10.1093/nar/gkaa1009
                   for   untargeted  metabolomics.  Nat  Commun.    47.  Neves BJ, Braga RC, Melo-Filho CC, Moreira-Filho JT,
                   2019;10(1):1516.                                     Muratov EN,  Andrade CH. QSAR-based  virtual
                   doi: 10.1038/s41467-019-09550-x                      screening: Advances and applications in drug discovery.
                37.  Luo  Y, Zhao X, Zhou J,  et al. A  network integration   Front Pharmacol. 2018;9:1275.
                   approach  for drug-target  interaction  prediction  and      doi: 10.3389/fphar.2018.01275
                   computational  drug repositioning from heterogeneous   48.  Dintakurthy  Y, Krishna Innmuri R,  Vanteru  A,
                   information. Nat Commun. 2017;8(1):573.              ThotakurixA.  Emerging  Applications  of  Artificial
                   doi: 10.1038/s41467-017-00680-8                      Intelligence  in Edge Computing:  A  Comprehensive



                Volume 22 Issue 3 (2025)                       102                           doi: 10.36922/AJWEP025070041
   103   104   105   106   107   108   109   110   111   112   113