Modern Strategies for Cns Drug Discovery: Integrating Cadd And Deep Learning For Therapeutic Advances
Keywords:
Ligand-Based Drug Design (LBDD), Structure-Based Drug Design (the SBDD method), and Computer-Aided Drug Design (CADD), Deep Learning (CNNs, RNNs, LSTM), CNSMolGen model
Abstract
Developing long-term therapies for diseases of the brain and nervous system (central nervous system), such as neurological and psychiatric disorders, is challenging due to high medication rates for failure and high expenses. Computer-Aided medication Design (CADD) provides a crucial strategy to address these issues, enabling more targeted and cost-effective drug development. The two CADD approaches of Ligand-Based Drug Design (the LBDD method) and Structure-Based Drugs Design (the SBDD method) focus on identifying key ligand physical and chemical and structural properties without requiring knowledge of the target structure. Drug design has been significantly improved in recent years by the combination of CADD with bioinformatics, including proteomics, metabolomics, and genomics. Potential treatment candidates for conditions like Alzheimer's, Parkinson's, neuropathic pain, and Virtual high-throughput drug screening as well as deep machine learning techniques such as convolutional networks of neurons (CNNs), networks of recurrent neurons (RNNs), as well as long short-term memories (LSTM) networks have played a significant role in the discovery of schizophrenia. More than 90% of compounds with desired CNS pharmacological properties are produced using the CNSMolGen model, a unique molecular generation system that makes use of bidirectional recurrent neural networks (BiRNNs). This integrated strategy has the potential to improve treatment results, speed up CNS drug discovery, and cut down on the time and expense of medication development.
References
1. Weller J, Budson A. Current understanding of Alzheimer’s disease diagnosis and treatment. F1000Research. 2018;7.
2. de Bie RM, Clarke CE, Espay AJ, Fox SH, Lang AE. Initiation of pharmacological therapy in Parkinson's disease: when, why, and how. The Lancet Neurology. 2020 May 1;19(5):452-61.
3. De Hert M, Detraux J, Van Winkel R, Yu W, Correll CU. Metabolic and cardiovascular adverse effects associated with antipsychotic drugs. Nature Reviews Endocrinology. 2012 Feb;8(2):114-26.
4. Harrison RK. Phase II and phase III failures: 2013–2015. Nat Rev Drug Discov. 2016 Dec 1;15(12):817-8.
5. Brown DG, Wobst HJ. A decade of FDA-approved drugs (2010–2019): trends and future directions. Journal of medicinal chemistry. 2021 Feb 22;64(5):2312-38.
6. Page B, Center UB, Wiener D. Estimating the economic and budgetary effects of research investments.
7. Dorahy G, Chen JZ, Balle T. Computer-aided drug design towards new psychotropic and neurological drugs. Molecules. 2023 Jan 30;28(3):1324.
8. Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. British journal of pharmacology. 2011 Mar;162(6):1239-49.
9. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews. 1997 Jan 15;23(1-3):3-25.
10. Acharya C, Coop A, E Polli J, D MacKerell A. Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Current computer-aided drug design. 2011 Mar 1;7(1):10-22.
11. Weigelt J. Structural genomics—impact on biomedicine and drug discovery. Experimental cell research. 2010 May 1;316(8):1332-8.
12. Salum LB, Polikarpov I, Andricopulo AD. Structure-based approach for the study of estrogen receptor binding affinity and subtype selectivity. Journal of chemical information and modeling. 2008 Nov 24;48(11):2243-53.
13. Dos Santos RN, Ferreira LG, Andricopulo AD. Practices in molecular docking and structure-based virtual screening. Computational drug discovery and design. 2018:31-50.
14. Bacilieri M, Moro S. Ligand-based drug design methodologies in drug discovery process: an overview. Current drug discovery technologies. 2006 Sep 1;3(3):155-65.
15. Drwal MN, Griffith R. Combination of ligand-and structure-based methods in virtual screening. Drug Discovery Today: Technologies. 2013 Sep 1;10(3):e395-401.
16. Gayathiri E, Prakash P, Kumaravel P, Jayaprakash J, Ragunathan MG, Sankar S, Pandiaraj S, Thirumalaivasan N, Thiruvengadam M, Govindasamy R. Computational approaches for modeling and structural design of biological systems: A comprehensive review. Progress in Biophysics and Molecular Biology. 2023 Oct 9.
17. Valasani KR, Vangavaragu JR, Day VW, Yan SS. Structure based design, synthesis, pharmacophore modeling, virtual screening, and molecular docking studies for identification of novel cyclophilin D inhibitors. Journal of chemical information and modeling. 2014 Mar 24;54(3):902-12.
18. Talevi A, Bellera C. An update on the novel methods for the discovery of antiseizure and antiepileptogenic medications: where are we in 2024?. Expert Opinion on Drug Discovery. 2024 Aug 2;19(8):975-90.
19. Fang Y. Ligand–receptor interaction platforms and their applications for drug discovery. Expert opinion on drug discovery. 2012 Oct 1;7(10):969-88.
20. Kahsai AW, Xiao K, Rajagopal S, Ahn S, Shukla AK, Sun J, Oas TG, Lefkowitz RJ. Multiple ligand-specific conformations of the β2-adrenergic receptor. Nature chemical biology. 2011 Oct;7(10):692-700.
21. Shoichet BK, Kobilka BK. Structure-based drug screening for G-protein-coupled receptors. Trends in pharmacological sciences. 2012 May 1;33(5):268-72.
22. Chandrika BR, Subramanian J, Sharma SD. Managing protein flexibility in docking and its applications. Drug discovery today. 2009 Apr 1;14(7-8):394-400.
23. Durrant JD, McCammon JA. Computer-aided drug-discovery techniques that account for receptor flexibility. Current opinion in pharmacology. 2010 Dec 1;10(6):770-4.
24. Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Current computer-aided drug design. 2011 Jun 1;7(2):146-57.
25. López-Vallejo F, Caulfield T, Martínez-Mayorga K, A Giulianotti M, Nefzi A, A Houghten R, L Medina-Franco J. Integrating virtual screening and combinatorial chemistry for accelerated drug discovery. Combinatorial Chemistry & High Throughput Screening. 2011 Jul 1;14(6):475-87.
26. Huang SY, Zou X. Advances and challenges in protein-ligand docking. International journal of molecular sciences. 2010 Aug 18;11(8):3016-34.
27. López-Vallejo F, Caulfield T, Martínez-Mayorga K, A Giulianotti M, Nefzi A, A Houghten R, L Medina-Franco J. Integrating virtual screening and combinatorial chemistry for accelerated drug discovery. Combinatorial Chemistry & High Throughput Screening. 2011 Jul 1;14(6):475-87.
28. Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH. Structure-based virtual screening for drug discovery: a problem-centric review. The AAPS journal. 2012 Mar;14:133-41.
29. 刘杰, 王任小. Classification of Current Scoring Functions.
30. Wang R, Lu Y, Wang S. Comparative evaluation of 11 scoring functions for molecular docking. Journal of medicinal chemistry. 2003 Jun 5;46(12):2287-303.
31. Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G. A critical assessment of docking programs and scoring functions. Journal of medicinal chemistry. 2006 Oct 5;49(20):5912-31.
32. Cross JB, Thompson DC, Rai BK, Baber JC, Fan KY, Hu Y, Humblet C. Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. Journal of chemical information and modeling. 2009 Jun 22;49(6):1455-74.
33. Xu W, Lucke AJ, Fairlie DP. Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets. Journal of Molecular Graphics and Modelling. 2015 Apr 1;57:76-88.
34. Cheng T, Li X, Li Y, Liu Z, Wang R. Comparative assessment of scoring functions on a diverse test set. Journal of chemical information and modeling. 2009 Apr 27;49(4):1079-93.
35. Houston DR, Walkinshaw MD. Consensus docking: improving the reliability of docking in a virtual screening context. Journal of chemical information and modeling. 2013 Feb 25;53(2):384-90.
36. Prathipati P, Dixit A, Saxena AK. Computer-aided drug design: Integration of structure-based and ligand-based approaches in drug design. Current Computer-Aided Drug Design. 2007 Jun 1;3(2):133-48.
37. Park S, Kwon Y, Jung H, Jang S, Lee H, Kim W. CSgator: an integrated web platform for compound set analysis. Journal of Cheminformatics. 2019 Dec;11:1-8.
38. Rudrapal M, Egbuna C, editors. Computer aided drug design (CADD): From ligand-based methods to structure-based approaches. Elsevier; 2022 May 26.
39. Lavecchia A, Di Giovanni C. Virtual screening strategies in drug discovery: a critical review. Current medicinal chemistry. 2013 Aug 1;20(23):2839-60.
40. Schultz TW, Cronin MT, Walker JD, Aptula AO. Quantitative structure–activity relationships (QSARs) in toxicology: a historical perspective. Journal of Molecular structure: THEOCHEM. 2003 Mar 7;622(1-2):1-22.
41. Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V. Advances in computational methods to predict the biological activity of compounds. Expert opinion on drug discovery. 2010 Jul 1;5(7):633-54.
42. Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V. QSAR modeling: where have you been? Where are you going to?. Journal of medicinal chemistry. 2014 Jun 26;57(12):4977-5010.
43. Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. Journal of the American Chemical Society. 1988 Aug;110(18):5959-67.
44. Cramer RD. Topomer CoMFA: a design methodology for rapid lead optimization. Journal of medicinal chemistry. 2003 Jan 30;46(3):374-88.
45. Putz MV, Lacrămă AM. Introducing spectral structure activity relationship (S-SAR) analysis. Application to ecotoxicology. International Journal of Molecular Sciences. 2007 May 22;8(5):363-91.
46. Gohlke H, Klebe G. DrugScore meets CoMFA: adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledge-based pair-potentials to a particular protein. Journal of medicinal chemistry. 2002 Sep 12;45(19):4153-70.
47. Datar PA, Khedkar SA, Malde AK, Coutinho EC. Comparative residue interaction analysis (CoRIA): a 3D-QSAR approach to explore the binding contributions of active site residues with ligands. Journal of computer-aided molecular design. 2006 Jun;20:343-60.
48. G. Damale M, N. Harke S, A. Kalam Khan F, B. Shinde D, N. Sangshetti J. Recent advances in multidimensional QSAR (4D-6D): a critical review. Mini reviews in medicinal chemistry. 2014 Jan 1;14(1):35-55.
49. Leahy DE, Cartmell J, Enoch S, Krstajic D, Bowen J. Automated QSPR through competitive workflow. Journal of Computer-Aided Molecular Design. 2005.
50. Davis AM, Wood DJ. Quantitative structure–activity relationship models that stand the test of time. Molecular Pharmaceutics. 2013 Apr 1;10(4):1183-90.
51. Vuorinen A, Schuster D. Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling. Methods. 2015 Jan 1;71:113-34.
52. Horvath D. Pharmacophore-based virtual screening. Chemoinformatics and computational chemical biology. 2011:261-98.
53. Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA. PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. Journal of computer-aided molecular design. 2006 Oct;20:647-71.
54. Dixon SL, Smondyrev AM, Rao SN. PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chemical biology & drug design. 2006 May;67(5):370-2.
55. Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. Journal of chemical information and modeling. 2005 Jan 24;45(1):160-9.
56. Vuorinen A, Schuster D. Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling. Methods. 2015 Jan 1;71:113-34.
57. Klabunde T, Giegerich C, Evers A. Sequence-derived three-dimensional pharmacophore models for G-protein-coupled receptors and their application in virtual screening. Journal of medicinal chemistry. 2009 May 14;52(9):2923-32.
58. Jacob L, Hoffmann B, Stoven V, Vert JP. Virtual screening of GPCRs: an in silico chemogenomics approach. BMC bioinformatics. 2008 Dec;9:1-6.
59. Bajorath J. Integration of virtual and high-throughput screening. Nature Reviews Drug Discovery. 2002 Nov;1(11):882-94.
60. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews. 1997 Jan 15;23(1-3):3-25.
61. Congreve M, Carr R, Murray C, Jhoti H. A'rule of three'for fragment-based lead discovery?. Drug discovery today. 2003 Oct 1;8(19):876-7.
62. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. Journal of medicinal chemistry. 2002 Jun 6;45(12):2615-23.
63. Athanasiadis E, Cournia Z, Spyrou G. ChemBioServer: a web-based pipeline for filtering, clustering and visualization of chemical compounds used in drug discovery. Bioinformatics. 2012 Nov 15;28(22):3002-3.
64. Lagorce D, Maupetit J, Baell J, Sperandio O, Tufféry P, Miteva MA, Galons H, Villoutreix BO. The FAF-Drugs2 server: a multistep engine to prepare electronic chemical compound collections. Bioinformatics. 2011 Jul;27(14):2018-20.
65. Lagorce D, Sperandio O, Galons H, Miteva MA, Villoutreix BO. FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects. BMC bioinformatics. 2008 Dec;9:1-9.
66. Karatzas E, Zamora JE, Athanasiadis E, Dellis D, Cournia Z, Spyrou GM. ChemBioServer 2.0: an advanced web server for filtering, clustering and networking of chemical compounds facilitating both drug discovery and repurposing. Bioinformatics. 2020 Apr 15;36(8):2602-4.
67. Jeffrey P, Summerfield S. Assessment of the blood–brain barrier in CNS drug discovery. Neurobiology of disease. 2010 Jan 1;37(1):33-7.
68. Ananthula RS, Ravikumar M, Pramod AB, Madala KK, Mahmood SK. Strategies for generating less toxic P-selectin inhibitors: Pharmacophore modeling, virtual screening and counter pharmacophore screening to remove toxic hits. Journal of Molecular Graphics and Modelling. 2008 Nov 1;27(4):546-57.
69. Rydberg P, Gloriam DE, Zaretzki J, Breneman C, Olsen L. SMARTCyp: a 2D method for prediction of cytochrome P450-mediated drug metabolism. ACS medicinal chemistry letters. 2010 Jun 10;1(3):96-100.
70. Cruciani G, Carosati E, De Boeck B, Ethirajulu K, Mackie C, Howe T, Vianello R. MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist. Journal of medicinal chemistry. 2005 Nov 3;48(22):6970-9.
71. Gleeson MP, Montanari D. Strategies for the generation, validation and application of in silico ADMET models in lead generation and optimization. Expert Opinion on Drug Metabolism & Toxicology. 2012 Nov 1;8(11):1435-46.
72. Friedman CP, Altman RB, Kohane IS, McCormick KA, Miller PL, Ozbolt JG, Shortliffe EH, Stormo GD, Szczepaniak MC, Tuck D, Williamson J. Training the next generation of informaticians: The impact of “BISTI” and bioinformatics—A report from the American College of Medical Informatics. Journal of the American Medical Informatics Association. 2004 May 1;11(3):167-72.
73. Richards WG. Computer-aided drug design. Pure and applied chemistry. 1994 Jan 1;66(8):1589-96.
74. Park DS, Kim JM, Lee YB, Ahn CH. QSID Tool: a new three-dimensional QSAR environmental tool. Journal of computer-aided molecular design. 2008 Dec;22(12):873-83.
75. Kurogi Y, Guner OF. Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Current medicinal chemistry. 2001 Jul 1;8(9):1035-55.
76. Rarey M, Dixon JS. Feature trees: a new molecular similarity measure based on tree matching. Journal of computer-aided molecular design. 1998 Sep;12:471-90.
77. Anastasiou E, Lorentz KO, Stein GJ, Mitchell PD. Prehistoric schistosomiasis parasite found in the Middle East. The Lancet Infectious Diseases. 2014 Jul 1;14(7):553-4.
78. McInnes C. Virtual screening strategies in drug discovery. Current opinion in chemical biology. 2007 Oct 1;11(5):494-502.
79. Klebe G. Virtual ligand screening: strategies, perspectives and limitations. Drug discovery today. 2006 Jul 1;11(13-14):580-94.
80. Alvarez JC. High-throughput docking as a source of novel drug leads. Current opinion in chemical biology. 2004 Aug 1;8(4):365-70.
81. Ghosh S, Nie A, An J, Huang Z. Structure-based virtual screening of chemical libraries for drug discovery. Current opinion in chemical biology. 2006 Jun 1;10(3):194-202.
82. Scarsi M, Podvinec M, Roth A, Hug H, Kersten S, Albrecht H, Schwede T, Meyer UA, Rücker C. Sulfonylureas and glinides exhibit peroxisome proliferator-activated receptor γ activity: a combined virtual screening and biological assay approach. Molecular pharmacology. 2007 Feb 1;71(2):398-406.
83. Kraemer O, Hazemann I, Podjarny AD, Klebe G. Virtual screening for inhibitors of human aldose reductase. Proteins: Structure, Function, and Bioinformatics. 2004 Jun 1;55(4):814-23.
84. Lu IL, Mahindroo N, Liang PH, Peng YH, Kuo CJ, Tsai KC, Hsieh HP, Chao YS, Wu SY. Structure-based drug design and structural biology study of novel nonpeptide inhibitors of severe acute respiratory syndrome coronavirus main protease. Journal of medicinal chemistry. 2006 Aug 24;49(17):5154-61.
85. Lahana R. How many leads from HTS?. Drug discovery today. 1999 Oct;4(10):447-8.
86. Carr RA, Congreve M, Murray CW, Rees DC. Fragment-based lead discovery: leads by design. Drug discovery today. 2005 Jul 15;10(14):987-92.
87. Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V. Deep neural nets as a method for quantitative structure–activity relationships. Journal of chemical information and modeling. 2015 Feb 23;55(2):263-74.
88. Raina R, Madhavan A, Ng AY. Large-scale deep unsupervised learning using graphics processors. InProceedings of the 26th annual international conference on machine learning 2009 Jun 14 (pp. 873-880).
89. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015 May 28;521(7553):436-44.
90. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic acids research. 2012 Jan 1;40(D1):D1100-7.
91. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic acids research. 2009 Jul 1;37(suppl_2):W623-33.
92. Keshavarzi Arshadi A, Salem M, Firouzbakht A, Yuan JS. MolData, a molecular benchmark for disease and target based machine learning. Journal of Cheminformatics. 2022 Mar 7;14(1):10.
93. Smalley E. AI-powered drug discovery captures pharma interest. Nature Biotechnology. 2017 Jul 1;35(7):604-6.
94. Schmidhuber J. Deep learning in neural networks: An overview. Neural networks. 2015 Jan 1;61:85-117.
95. Cadieu CF, Hong H, Yamins DL, Pinto N, Ardila D, Solomon EA, Majaj NJ, DiCarlo JJ. Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLoS computational biology. 2014 Dec 18;10(12):e1003963.
96. LeCun Y, Boser B, Denker J, Henderson D, Howard R, Hubbard W, Jackel L. Handwritten digit recognition with a back-propagation network. Advances in neural information processing systems. 1989;2.
97. Sauer S, Matter H, Hessler G, Grebner C. Integrating Reaction Schemes, Reagent Databases, and Virtual Libraries into Fragment-Based Design by Reinforcement Learning. Journal of Chemical Information and Modeling. 2023 Sep 5;63(18):5709-26.
98. Francoeur PG, Masuda T, Sunseri J, Jia A, Iovanisci RB, Snyder I, Koes DR. Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design. Journal of chemical information and modeling. 2020 Aug 31;60(9):4200-15.
99. Lim J, Ryu S, Park K, Choe YJ, Ham J, Kim WY. Predicting drug–target interaction using a novel graph neural network with 3D structure-embedded graph representation. Journal of chemical information and modeling. 2019 Aug 23;59(9):3981-8.
100. Nguyen TH, Tran PT, Pham NQ, Hoang VH, Hiep DM, Ngo ST. Identifying possible AChE inhibitors from drug-like molecules via machine learning and experimental studies. ACS omega. 2022 Jun 8;7(24):20673-82.
101. Gauthier S, Webster C, Servaes S, Morais JA, Rosa-Neto P. World Alzheimer Report 2022 life after diagnosis: navigating treatment, care and support. 2022. London, UK: Alzheimer's Disease International.
102. Iida M, Iwata M, Yamanishi Y. Network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets. Bioinformatics. 2020 Jul;36(Supplement_1):i516-24.
103. Ivanova L, Karelson M, Dobchev DA. Multitarget approach to drug candidates against Alzheimer’s disease related to AChE, SERT, BACE1 and GSK3β protein targets. Molecules. 2020 Apr 17;25(8):1846.
104. Liu J, Peng D, Li J, Dai Z, Zou X, Li Z. Identification of potential Parkinson’s disease drugs based on multi-source data fusion and convolutional neural network. Molecules. 2022 Jul 26;27(15):4780.
105. Khan A, Kaushik AC, Ali SS, Ahmad N, Wei DQ. Deep-learning-based target screening and similarity search for the predicted inhibitors of the pathways in Parkinson's disease. RSC advances. 2019;9(18):10326-39.
106. Tan S, Gong X, Liu H, Yao X. Virtual screening and biological activity evaluation of new potent inhibitors targeting LRRK2 kinase domain. ACS Chemical Neuroscience. 2021 Aug 13;12(17):3214-24.
107. Peng Y, Dong H, Welsh WJ. Comprehensive 3D-QSAR model predicts binding affinity of structurally diverse sigma 1 receptor ligands. Journal of Chemical Information and Modeling. 2018 Nov 29;59(1):486-97.
108. Kang KM, Lee I, Nam H, Kim YC. AI-based prediction of new binding site and virtual screening for the discovery of novel P2X3 receptor antagonists. European Journal of Medicinal Chemistry. 2022 Oct 5;240:114556.
109. Yu Y, Dong H, Peng Y, Welsh WJ. QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs. Molecules. 2021 Aug 30;26(17):5270.
110. Zhang H, He X, Wang X, Yu B, Zhao S, Jiao P, Jin H, Liu Z, Wang K, Zhang L, Zhang L. Design, synthesis and biological activities of piperidine-spirooxadiazole derivatives as α7 nicotinic receptor antagonists. European Journal of Medicinal Chemistry. 2020 Dec 1;207:112774.
111. Gou R, Yang J, Guo M, Chen Y, Xue W. CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design. Journal of Chemical Information and Modeling. 2024 May 13.
112. Bung N, Krishnan SR, Roy A. An in silico explainable multiparameter optimization approach for de novo drug design against proteins from the central nervous system. Journal of Chemical Information and Modeling. 2022 May 17;62(11):2685-95.
2. de Bie RM, Clarke CE, Espay AJ, Fox SH, Lang AE. Initiation of pharmacological therapy in Parkinson's disease: when, why, and how. The Lancet Neurology. 2020 May 1;19(5):452-61.
3. De Hert M, Detraux J, Van Winkel R, Yu W, Correll CU. Metabolic and cardiovascular adverse effects associated with antipsychotic drugs. Nature Reviews Endocrinology. 2012 Feb;8(2):114-26.
4. Harrison RK. Phase II and phase III failures: 2013–2015. Nat Rev Drug Discov. 2016 Dec 1;15(12):817-8.
5. Brown DG, Wobst HJ. A decade of FDA-approved drugs (2010–2019): trends and future directions. Journal of medicinal chemistry. 2021 Feb 22;64(5):2312-38.
6. Page B, Center UB, Wiener D. Estimating the economic and budgetary effects of research investments.
7. Dorahy G, Chen JZ, Balle T. Computer-aided drug design towards new psychotropic and neurological drugs. Molecules. 2023 Jan 30;28(3):1324.
8. Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. British journal of pharmacology. 2011 Mar;162(6):1239-49.
9. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews. 1997 Jan 15;23(1-3):3-25.
10. Acharya C, Coop A, E Polli J, D MacKerell A. Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Current computer-aided drug design. 2011 Mar 1;7(1):10-22.
11. Weigelt J. Structural genomics—impact on biomedicine and drug discovery. Experimental cell research. 2010 May 1;316(8):1332-8.
12. Salum LB, Polikarpov I, Andricopulo AD. Structure-based approach for the study of estrogen receptor binding affinity and subtype selectivity. Journal of chemical information and modeling. 2008 Nov 24;48(11):2243-53.
13. Dos Santos RN, Ferreira LG, Andricopulo AD. Practices in molecular docking and structure-based virtual screening. Computational drug discovery and design. 2018:31-50.
14. Bacilieri M, Moro S. Ligand-based drug design methodologies in drug discovery process: an overview. Current drug discovery technologies. 2006 Sep 1;3(3):155-65.
15. Drwal MN, Griffith R. Combination of ligand-and structure-based methods in virtual screening. Drug Discovery Today: Technologies. 2013 Sep 1;10(3):e395-401.
16. Gayathiri E, Prakash P, Kumaravel P, Jayaprakash J, Ragunathan MG, Sankar S, Pandiaraj S, Thirumalaivasan N, Thiruvengadam M, Govindasamy R. Computational approaches for modeling and structural design of biological systems: A comprehensive review. Progress in Biophysics and Molecular Biology. 2023 Oct 9.
17. Valasani KR, Vangavaragu JR, Day VW, Yan SS. Structure based design, synthesis, pharmacophore modeling, virtual screening, and molecular docking studies for identification of novel cyclophilin D inhibitors. Journal of chemical information and modeling. 2014 Mar 24;54(3):902-12.
18. Talevi A, Bellera C. An update on the novel methods for the discovery of antiseizure and antiepileptogenic medications: where are we in 2024?. Expert Opinion on Drug Discovery. 2024 Aug 2;19(8):975-90.
19. Fang Y. Ligand–receptor interaction platforms and their applications for drug discovery. Expert opinion on drug discovery. 2012 Oct 1;7(10):969-88.
20. Kahsai AW, Xiao K, Rajagopal S, Ahn S, Shukla AK, Sun J, Oas TG, Lefkowitz RJ. Multiple ligand-specific conformations of the β2-adrenergic receptor. Nature chemical biology. 2011 Oct;7(10):692-700.
21. Shoichet BK, Kobilka BK. Structure-based drug screening for G-protein-coupled receptors. Trends in pharmacological sciences. 2012 May 1;33(5):268-72.
22. Chandrika BR, Subramanian J, Sharma SD. Managing protein flexibility in docking and its applications. Drug discovery today. 2009 Apr 1;14(7-8):394-400.
23. Durrant JD, McCammon JA. Computer-aided drug-discovery techniques that account for receptor flexibility. Current opinion in pharmacology. 2010 Dec 1;10(6):770-4.
24. Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Current computer-aided drug design. 2011 Jun 1;7(2):146-57.
25. López-Vallejo F, Caulfield T, Martínez-Mayorga K, A Giulianotti M, Nefzi A, A Houghten R, L Medina-Franco J. Integrating virtual screening and combinatorial chemistry for accelerated drug discovery. Combinatorial Chemistry & High Throughput Screening. 2011 Jul 1;14(6):475-87.
26. Huang SY, Zou X. Advances and challenges in protein-ligand docking. International journal of molecular sciences. 2010 Aug 18;11(8):3016-34.
27. López-Vallejo F, Caulfield T, Martínez-Mayorga K, A Giulianotti M, Nefzi A, A Houghten R, L Medina-Franco J. Integrating virtual screening and combinatorial chemistry for accelerated drug discovery. Combinatorial Chemistry & High Throughput Screening. 2011 Jul 1;14(6):475-87.
28. Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH. Structure-based virtual screening for drug discovery: a problem-centric review. The AAPS journal. 2012 Mar;14:133-41.
29. 刘杰, 王任小. Classification of Current Scoring Functions.
30. Wang R, Lu Y, Wang S. Comparative evaluation of 11 scoring functions for molecular docking. Journal of medicinal chemistry. 2003 Jun 5;46(12):2287-303.
31. Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G. A critical assessment of docking programs and scoring functions. Journal of medicinal chemistry. 2006 Oct 5;49(20):5912-31.
32. Cross JB, Thompson DC, Rai BK, Baber JC, Fan KY, Hu Y, Humblet C. Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. Journal of chemical information and modeling. 2009 Jun 22;49(6):1455-74.
33. Xu W, Lucke AJ, Fairlie DP. Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets. Journal of Molecular Graphics and Modelling. 2015 Apr 1;57:76-88.
34. Cheng T, Li X, Li Y, Liu Z, Wang R. Comparative assessment of scoring functions on a diverse test set. Journal of chemical information and modeling. 2009 Apr 27;49(4):1079-93.
35. Houston DR, Walkinshaw MD. Consensus docking: improving the reliability of docking in a virtual screening context. Journal of chemical information and modeling. 2013 Feb 25;53(2):384-90.
36. Prathipati P, Dixit A, Saxena AK. Computer-aided drug design: Integration of structure-based and ligand-based approaches in drug design. Current Computer-Aided Drug Design. 2007 Jun 1;3(2):133-48.
37. Park S, Kwon Y, Jung H, Jang S, Lee H, Kim W. CSgator: an integrated web platform for compound set analysis. Journal of Cheminformatics. 2019 Dec;11:1-8.
38. Rudrapal M, Egbuna C, editors. Computer aided drug design (CADD): From ligand-based methods to structure-based approaches. Elsevier; 2022 May 26.
39. Lavecchia A, Di Giovanni C. Virtual screening strategies in drug discovery: a critical review. Current medicinal chemistry. 2013 Aug 1;20(23):2839-60.
40. Schultz TW, Cronin MT, Walker JD, Aptula AO. Quantitative structure–activity relationships (QSARs) in toxicology: a historical perspective. Journal of Molecular structure: THEOCHEM. 2003 Mar 7;622(1-2):1-22.
41. Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V. Advances in computational methods to predict the biological activity of compounds. Expert opinion on drug discovery. 2010 Jul 1;5(7):633-54.
42. Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V. QSAR modeling: where have you been? Where are you going to?. Journal of medicinal chemistry. 2014 Jun 26;57(12):4977-5010.
43. Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. Journal of the American Chemical Society. 1988 Aug;110(18):5959-67.
44. Cramer RD. Topomer CoMFA: a design methodology for rapid lead optimization. Journal of medicinal chemistry. 2003 Jan 30;46(3):374-88.
45. Putz MV, Lacrămă AM. Introducing spectral structure activity relationship (S-SAR) analysis. Application to ecotoxicology. International Journal of Molecular Sciences. 2007 May 22;8(5):363-91.
46. Gohlke H, Klebe G. DrugScore meets CoMFA: adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledge-based pair-potentials to a particular protein. Journal of medicinal chemistry. 2002 Sep 12;45(19):4153-70.
47. Datar PA, Khedkar SA, Malde AK, Coutinho EC. Comparative residue interaction analysis (CoRIA): a 3D-QSAR approach to explore the binding contributions of active site residues with ligands. Journal of computer-aided molecular design. 2006 Jun;20:343-60.
48. G. Damale M, N. Harke S, A. Kalam Khan F, B. Shinde D, N. Sangshetti J. Recent advances in multidimensional QSAR (4D-6D): a critical review. Mini reviews in medicinal chemistry. 2014 Jan 1;14(1):35-55.
49. Leahy DE, Cartmell J, Enoch S, Krstajic D, Bowen J. Automated QSPR through competitive workflow. Journal of Computer-Aided Molecular Design. 2005.
50. Davis AM, Wood DJ. Quantitative structure–activity relationship models that stand the test of time. Molecular Pharmaceutics. 2013 Apr 1;10(4):1183-90.
51. Vuorinen A, Schuster D. Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling. Methods. 2015 Jan 1;71:113-34.
52. Horvath D. Pharmacophore-based virtual screening. Chemoinformatics and computational chemical biology. 2011:261-98.
53. Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA. PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. Journal of computer-aided molecular design. 2006 Oct;20:647-71.
54. Dixon SL, Smondyrev AM, Rao SN. PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chemical biology & drug design. 2006 May;67(5):370-2.
55. Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. Journal of chemical information and modeling. 2005 Jan 24;45(1):160-9.
56. Vuorinen A, Schuster D. Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling. Methods. 2015 Jan 1;71:113-34.
57. Klabunde T, Giegerich C, Evers A. Sequence-derived three-dimensional pharmacophore models for G-protein-coupled receptors and their application in virtual screening. Journal of medicinal chemistry. 2009 May 14;52(9):2923-32.
58. Jacob L, Hoffmann B, Stoven V, Vert JP. Virtual screening of GPCRs: an in silico chemogenomics approach. BMC bioinformatics. 2008 Dec;9:1-6.
59. Bajorath J. Integration of virtual and high-throughput screening. Nature Reviews Drug Discovery. 2002 Nov;1(11):882-94.
60. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews. 1997 Jan 15;23(1-3):3-25.
61. Congreve M, Carr R, Murray C, Jhoti H. A'rule of three'for fragment-based lead discovery?. Drug discovery today. 2003 Oct 1;8(19):876-7.
62. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. Journal of medicinal chemistry. 2002 Jun 6;45(12):2615-23.
63. Athanasiadis E, Cournia Z, Spyrou G. ChemBioServer: a web-based pipeline for filtering, clustering and visualization of chemical compounds used in drug discovery. Bioinformatics. 2012 Nov 15;28(22):3002-3.
64. Lagorce D, Maupetit J, Baell J, Sperandio O, Tufféry P, Miteva MA, Galons H, Villoutreix BO. The FAF-Drugs2 server: a multistep engine to prepare electronic chemical compound collections. Bioinformatics. 2011 Jul;27(14):2018-20.
65. Lagorce D, Sperandio O, Galons H, Miteva MA, Villoutreix BO. FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects. BMC bioinformatics. 2008 Dec;9:1-9.
66. Karatzas E, Zamora JE, Athanasiadis E, Dellis D, Cournia Z, Spyrou GM. ChemBioServer 2.0: an advanced web server for filtering, clustering and networking of chemical compounds facilitating both drug discovery and repurposing. Bioinformatics. 2020 Apr 15;36(8):2602-4.
67. Jeffrey P, Summerfield S. Assessment of the blood–brain barrier in CNS drug discovery. Neurobiology of disease. 2010 Jan 1;37(1):33-7.
68. Ananthula RS, Ravikumar M, Pramod AB, Madala KK, Mahmood SK. Strategies for generating less toxic P-selectin inhibitors: Pharmacophore modeling, virtual screening and counter pharmacophore screening to remove toxic hits. Journal of Molecular Graphics and Modelling. 2008 Nov 1;27(4):546-57.
69. Rydberg P, Gloriam DE, Zaretzki J, Breneman C, Olsen L. SMARTCyp: a 2D method for prediction of cytochrome P450-mediated drug metabolism. ACS medicinal chemistry letters. 2010 Jun 10;1(3):96-100.
70. Cruciani G, Carosati E, De Boeck B, Ethirajulu K, Mackie C, Howe T, Vianello R. MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist. Journal of medicinal chemistry. 2005 Nov 3;48(22):6970-9.
71. Gleeson MP, Montanari D. Strategies for the generation, validation and application of in silico ADMET models in lead generation and optimization. Expert Opinion on Drug Metabolism & Toxicology. 2012 Nov 1;8(11):1435-46.
72. Friedman CP, Altman RB, Kohane IS, McCormick KA, Miller PL, Ozbolt JG, Shortliffe EH, Stormo GD, Szczepaniak MC, Tuck D, Williamson J. Training the next generation of informaticians: The impact of “BISTI” and bioinformatics—A report from the American College of Medical Informatics. Journal of the American Medical Informatics Association. 2004 May 1;11(3):167-72.
73. Richards WG. Computer-aided drug design. Pure and applied chemistry. 1994 Jan 1;66(8):1589-96.
74. Park DS, Kim JM, Lee YB, Ahn CH. QSID Tool: a new three-dimensional QSAR environmental tool. Journal of computer-aided molecular design. 2008 Dec;22(12):873-83.
75. Kurogi Y, Guner OF. Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Current medicinal chemistry. 2001 Jul 1;8(9):1035-55.
76. Rarey M, Dixon JS. Feature trees: a new molecular similarity measure based on tree matching. Journal of computer-aided molecular design. 1998 Sep;12:471-90.
77. Anastasiou E, Lorentz KO, Stein GJ, Mitchell PD. Prehistoric schistosomiasis parasite found in the Middle East. The Lancet Infectious Diseases. 2014 Jul 1;14(7):553-4.
78. McInnes C. Virtual screening strategies in drug discovery. Current opinion in chemical biology. 2007 Oct 1;11(5):494-502.
79. Klebe G. Virtual ligand screening: strategies, perspectives and limitations. Drug discovery today. 2006 Jul 1;11(13-14):580-94.
80. Alvarez JC. High-throughput docking as a source of novel drug leads. Current opinion in chemical biology. 2004 Aug 1;8(4):365-70.
81. Ghosh S, Nie A, An J, Huang Z. Structure-based virtual screening of chemical libraries for drug discovery. Current opinion in chemical biology. 2006 Jun 1;10(3):194-202.
82. Scarsi M, Podvinec M, Roth A, Hug H, Kersten S, Albrecht H, Schwede T, Meyer UA, Rücker C. Sulfonylureas and glinides exhibit peroxisome proliferator-activated receptor γ activity: a combined virtual screening and biological assay approach. Molecular pharmacology. 2007 Feb 1;71(2):398-406.
83. Kraemer O, Hazemann I, Podjarny AD, Klebe G. Virtual screening for inhibitors of human aldose reductase. Proteins: Structure, Function, and Bioinformatics. 2004 Jun 1;55(4):814-23.
84. Lu IL, Mahindroo N, Liang PH, Peng YH, Kuo CJ, Tsai KC, Hsieh HP, Chao YS, Wu SY. Structure-based drug design and structural biology study of novel nonpeptide inhibitors of severe acute respiratory syndrome coronavirus main protease. Journal of medicinal chemistry. 2006 Aug 24;49(17):5154-61.
85. Lahana R. How many leads from HTS?. Drug discovery today. 1999 Oct;4(10):447-8.
86. Carr RA, Congreve M, Murray CW, Rees DC. Fragment-based lead discovery: leads by design. Drug discovery today. 2005 Jul 15;10(14):987-92.
87. Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V. Deep neural nets as a method for quantitative structure–activity relationships. Journal of chemical information and modeling. 2015 Feb 23;55(2):263-74.
88. Raina R, Madhavan A, Ng AY. Large-scale deep unsupervised learning using graphics processors. InProceedings of the 26th annual international conference on machine learning 2009 Jun 14 (pp. 873-880).
89. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015 May 28;521(7553):436-44.
90. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic acids research. 2012 Jan 1;40(D1):D1100-7.
91. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic acids research. 2009 Jul 1;37(suppl_2):W623-33.
92. Keshavarzi Arshadi A, Salem M, Firouzbakht A, Yuan JS. MolData, a molecular benchmark for disease and target based machine learning. Journal of Cheminformatics. 2022 Mar 7;14(1):10.
93. Smalley E. AI-powered drug discovery captures pharma interest. Nature Biotechnology. 2017 Jul 1;35(7):604-6.
94. Schmidhuber J. Deep learning in neural networks: An overview. Neural networks. 2015 Jan 1;61:85-117.
95. Cadieu CF, Hong H, Yamins DL, Pinto N, Ardila D, Solomon EA, Majaj NJ, DiCarlo JJ. Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLoS computational biology. 2014 Dec 18;10(12):e1003963.
96. LeCun Y, Boser B, Denker J, Henderson D, Howard R, Hubbard W, Jackel L. Handwritten digit recognition with a back-propagation network. Advances in neural information processing systems. 1989;2.
97. Sauer S, Matter H, Hessler G, Grebner C. Integrating Reaction Schemes, Reagent Databases, and Virtual Libraries into Fragment-Based Design by Reinforcement Learning. Journal of Chemical Information and Modeling. 2023 Sep 5;63(18):5709-26.
98. Francoeur PG, Masuda T, Sunseri J, Jia A, Iovanisci RB, Snyder I, Koes DR. Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design. Journal of chemical information and modeling. 2020 Aug 31;60(9):4200-15.
99. Lim J, Ryu S, Park K, Choe YJ, Ham J, Kim WY. Predicting drug–target interaction using a novel graph neural network with 3D structure-embedded graph representation. Journal of chemical information and modeling. 2019 Aug 23;59(9):3981-8.
100. Nguyen TH, Tran PT, Pham NQ, Hoang VH, Hiep DM, Ngo ST. Identifying possible AChE inhibitors from drug-like molecules via machine learning and experimental studies. ACS omega. 2022 Jun 8;7(24):20673-82.
101. Gauthier S, Webster C, Servaes S, Morais JA, Rosa-Neto P. World Alzheimer Report 2022 life after diagnosis: navigating treatment, care and support. 2022. London, UK: Alzheimer's Disease International.
102. Iida M, Iwata M, Yamanishi Y. Network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets. Bioinformatics. 2020 Jul;36(Supplement_1):i516-24.
103. Ivanova L, Karelson M, Dobchev DA. Multitarget approach to drug candidates against Alzheimer’s disease related to AChE, SERT, BACE1 and GSK3β protein targets. Molecules. 2020 Apr 17;25(8):1846.
104. Liu J, Peng D, Li J, Dai Z, Zou X, Li Z. Identification of potential Parkinson’s disease drugs based on multi-source data fusion and convolutional neural network. Molecules. 2022 Jul 26;27(15):4780.
105. Khan A, Kaushik AC, Ali SS, Ahmad N, Wei DQ. Deep-learning-based target screening and similarity search for the predicted inhibitors of the pathways in Parkinson's disease. RSC advances. 2019;9(18):10326-39.
106. Tan S, Gong X, Liu H, Yao X. Virtual screening and biological activity evaluation of new potent inhibitors targeting LRRK2 kinase domain. ACS Chemical Neuroscience. 2021 Aug 13;12(17):3214-24.
107. Peng Y, Dong H, Welsh WJ. Comprehensive 3D-QSAR model predicts binding affinity of structurally diverse sigma 1 receptor ligands. Journal of Chemical Information and Modeling. 2018 Nov 29;59(1):486-97.
108. Kang KM, Lee I, Nam H, Kim YC. AI-based prediction of new binding site and virtual screening for the discovery of novel P2X3 receptor antagonists. European Journal of Medicinal Chemistry. 2022 Oct 5;240:114556.
109. Yu Y, Dong H, Peng Y, Welsh WJ. QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs. Molecules. 2021 Aug 30;26(17):5270.
110. Zhang H, He X, Wang X, Yu B, Zhao S, Jiao P, Jin H, Liu Z, Wang K, Zhang L, Zhang L. Design, synthesis and biological activities of piperidine-spirooxadiazole derivatives as α7 nicotinic receptor antagonists. European Journal of Medicinal Chemistry. 2020 Dec 1;207:112774.
111. Gou R, Yang J, Guo M, Chen Y, Xue W. CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design. Journal of Chemical Information and Modeling. 2024 May 13.
112. Bung N, Krishnan SR, Roy A. An in silico explainable multiparameter optimization approach for de novo drug design against proteins from the central nervous system. Journal of Chemical Information and Modeling. 2022 May 17;62(11):2685-95.
How to Cite
Bipin Singh, Vishal Kajla, Nisha yadav, Pratibha Sharma, Tichakunda Xavier Mharazanye, Dipendra Kohar, Muskaan, & Shalu Kashyap. (1). Modern Strategies for Cns Drug Discovery: Integrating Cadd And Deep Learning For Therapeutic Advances. Revista Electronica De Veterinaria, 26(1), 26-38. https://doi.org/10.69980/redvet.v26i1.1656
Section
Articles
