References
- Ratni H, Ebeling M, Baird J, et al. Discovery of risdiplam, a selective survival of motor neuron-2 (SMN2) gene splicing modifier for the treatment of spinal muscular atrophy (SMA). J Med Chem. 2018;61(15):6501–6517. doi: 10.1021/acs.jmedchem.8b00741
- Warner KD, Hajdin CE, Weeks KM. Principles for targeting RNA with drug-like small molecules. Nat Rev Drug Discov. 2018;17(8):547–558. doi: 10.1038/nrd.2018.93
- ENCODE Project Consortium, Birney E, Stamatoyannopoulos JA, et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature. 2007;447(7146):799–816.
- Esteller M. Non-coding RNAs in human disease. Nat Rev Genet. 2011;12(12):861–874. doi: 10.1038/nrg3074
- Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18(6):463–477. doi: 10.1038/s41573-019-0024-5
- Jiménez-Luna J, Grisoni F, Weskamp N, et al. Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opin Drug Discov. 2021;16(9):949–959. doi: 10.1080/17460441.2021.1909567
- Garber K. Drugging RNA. Nat Biotechnol. 2023;41(6):745–749. doi: 10.1038/s41587-023-01790-z
- Jiménez-Luna J, Grisoni F, Schneider G. Drug discovery with explainable artificial intelligence. Nat Mach Intell. 2020;2(10):573–584. doi: 10.1038/s42256-020-00236-4
- Hughes JP, Rees S, Kalindjian SB, et al. Principles of early drug discovery. Br J Pharmacol. 2011;162(6):1239–1249. doi: 10.1111/j.1476-5381.2010.01127.x
- Bagewadi S, Bobic T, Hofmann-Apitius M, et al. Detecting miRNA mentions and relations in biomedical literature. F1000Res. 2014;3:205. doi: 10.12688/f1000research.4591.2
- Jiang Q, Wang Y, Hao Y, et al. miR2disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2009;37(Database issue):D98–D104. doi: 10.1093/nar/gkn714
- Naeem H, Küffner R, Csaba G, et al. miRsel: automated extraction of associations between microRnas and genes from the biomedical literature. BMC Bioinf. 2010;11(1):135. doi: 10.1186/1471-2105-11-135
- Lamurias A, Clarke LA, Couto FM, et al. Extracting microRNA-gene relations from biomedical literature using distant supervision. PLoS One. 2017;12(3):e0171929. doi: 10.1371/journal.pone.0171929
- Joppich M, Weber C, Zimmer R. Using context-sensitive text mining to identify miRnas in different stages of atherosclerosis. Thromb Haemost. 2019;119(8):1247–1264. doi: 10.1055/s-0039-1693165
- Carlevaro-Fita J, Lanzós A, Feuerbach L, et al. Cancer LncRNA census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis. Commun Biol. 2020;3(1):56. doi: 10.1038/s42003-019-0741-7
- Xu F, Wang Y, Ling Y, et al. dbDEMC 3.0: functional exploration of differentially expressed miRnas in cancers of human and model organisms. Int J Genomics Proteomics. 2022;20(3):446–454. doi: 10.1016/j.gpb.2022.04.006
- Zhou B, Ji B, Liu K, et al. EVLncRNAs 2.0: an updated database of manually curated functional long non-coding RNAs validated by low-throughput experiments. Nucleic Acids Res. 2021;49(D1):D86–D91. doi: 10.1093/nar/gkaa1076
- Cui C, Zong B, Fan R, et al. HMDD v4.0: a database for experimentally supported human microRNA-disease associations. Nucleic Acids Res. 2024;52(D1):D1327–D1332. doi: 10.1093/nar/gkad717
- Gao Y, Shang S, Guo S, et al. Lnc2Cancer 3.0: an updated resource for experimentally supported lncRna/circrna cancer associations and web tools based on RNA-seq and scRNA-seq data. Nucleic Acids Res. 2021;49(D1):D1251–D1258. doi: 10.1093/nar/gkaa1006
- Bao Z, Yang Z, Huang Z, et al. LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases. Nucleic Acids Res. 2019;47(D1):D1034–D1037. doi: 10.1093/nar/gky905
- Yang Y, Wang D, Miao Y, et al. lncRNASNP v3: an updated database for functional variants in long non-coding RNAs. Nucleic Acids Res. 2023;51(D1):D192–D198. doi: 10.1093/nar/gkac981
- Jiang Q, Wang Y, Hao Y, et al. miR2disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2009;37(Database):D98–D104. doi: 10.1093/nar/gkn714
- Xie B, Ding Q, Han H, et al. miRcancer: a microRNA-cancer association database constructed by text mining on literature. Bioinformatics. 2013;29(5):638–644. doi: 10.1093/bioinformatics/btt014
- Chen J, Lin J, Hu Y, et al. Rnadisease v4.0: an updated resource of RNA-associated diseases, providing RNA-disease analysis, enrichment and prediction. Nucleic Acids Res. 2023;51(D1):D1397–D1404. doi: 10.1093/nar/gkac814
- Li J, Han L, Roebuck P, et al. TANRIC: an interactive open platform to explore the function of lncRnas in cancer. Cancer Res. 2015;75(18):3728–3737. doi: 10.1158/0008-5472.CAN-15-0273
- Zhao T, Xu J, Liu L, et al. Identification of cancer-related lncRnas through integrating genome, regulome and transcriptome features. Mol Biosyst. 2015;11(1):126–136. doi: 10.1039/C4MB00478G
- Zhang X, Wang J, Li J, et al. CRlncRC: a machine learning-based method for cancer-related long noncoding RNA identification using integrated features. BMC Med Genomics. 2018;11(Suppl 6):120. doi: 10.1186/s12920-018-0436-9
- Khalid R, Naveed H, Khalid Z. Computational prediction of disease related lncRnas using machine learning. Sci Rep. 2023;13(1):806. doi: 10.1038/s41598-023-27680-7
- Ning S, Zhang J, Wang P, et al. Lnc2Cancer: a manually curated database of experimentally supported lncRnas associated with various human cancers. Nucleic Acids Res. 2016;44(D1):D980–D985. doi: 10.1093/nar/gkv1094
- Chen G, Wang Z, Wang D, et al. LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic Acids Res. 2013;41(D1):D983–D986. doi: 10.1093/nar/gks1099
- Ning L, Cui T, Zheng B, et al. MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation. Nucleic Acids Res. 2021;49(D1):D160–D164. doi: 10.1093/nar/gkaa707
- Morishita EC. Discovery of RNA-targeted small molecules through the merging of experimental and computational technologies. Expert Opin Drug Discov. 2023;18(2):207–226. doi: 10.1080/17460441.2022.2134852
- Xia T, Santa Lucia J Jr, Burkard ME, et al. Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs. Biochemistry. 1998;37(42):14719–14735. doi: 10.1021/bi9809425
- Mathews DH, Sabina J, Zuker M, et al. Expanded sequence dependence of thermodynamic parameters provides improved prediction of RNA secondary structure. J Mol Biol. 1999;288(5):911–940. doi: 10.1006/jmbi.1999.2700
- Mathews DH, Disney MD, Childs JL, et al. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc Natl Acad Sci U S A. 2004;101(19):7287–7292. doi: 10.1073/pnas.0401799101
- Lu ZJ, Turner DH, Mathews DH. A set of nearest-neighbor parameters for predicting the enthalpy change of RNA secondary structure formation. Nucleic Acids Res. 2006;34(17):4912–4924. doi: 10.1093/nar/gkl472
- Dawson WK, Shino A, Kawai G, et al. Developing an updated strategy for estimating the free-energy parameters in RNA duplexes. Int J Mol Sci. 2021;22(18):9708. doi: 10.3390/ijms22189708
- Lu W, Tang Y, Wu H, et al. Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter. BMC Bioinf. 2019;20(Suppl 25):684. doi: 10.1186/s12859-019-3258-7
- Do CB, Woods DA, Batzoglou S. Contrafold: RNA secondary structure prediction without physics-based models. Bioinformatics. 2006;22(14):e90–e98. doi: 10.1093/bioinformatics/btl246
- Zakov S, Goldberg Y, Elhadad M, et al. Rich parameterization improves RNA structure prediction. J Comput Biol. 2011;18(11):1525–1542. doi: 10.1089/cmb.2011.0184
- Wayment-Steele HK, Kladwang W, Strom AI, et al. RNA secondary structure packages evaluated and improved by high-throughput experiments. Nat Methods. 2022;19(10):1234–1242. doi: 10.1038/s41592-022-01605-0
- Bindewald E, Shapiro BA. RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers. RNA. 2006;12(3):342–352. doi: 10.1261/rna.2164906
- Akiyama M, Sato Y, Sakakibara Y. A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model. J Bioinform Comput Biol. 2018;16(6):1840025. doi: 10.1142/S0219720018400255
- Sato K, Akiyama M, Sakakibara Y. RNA secondary structure prediction using deep learning with thermodynamic integration. Nat Commun. 2021;12(1):941. doi: 10.1038/s41467-021-21194-4
- Singh J, Hanson J, Paliwal K, et al. RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning. Nat Commun. 2019;10(1):5407. doi: 10.1038/s41467-019-13395-9
- Townshend RJL, Eismann S, Watkins AM, et al. Geometric deep learning of RNA structure. Science. 2021;373(6558):1047–1051. doi: 10.1126/science.abe5650
- Frellsen J, Moltke I, Thiim M, et al. A probabilistic model of RNA conformational space. PLoS Comput Biol. 2009;5(6):e1000406. doi: 10.1371/journal.pcbi.1000406
- Pearce R, Omenn GS, Zhang Y. De novo RNA tertiary structure prediction at atomic resolution using geometric potentials from deep learning. bioRxiv Prepint. 2022. doi: 10.1101/2022.05.15.491755
- Shen T, Hu Z, Peng Z, et al. E2Efold-3D: End-to-end deep learning method for accurate de novo RNA 3D structure prediction. arXiv Preprint. 2022. doi: 10.48550/arXiv.2207.01586
- Zirbel CG, Roll J, Sweeney BA, et al. Identifying novel sequence variants of RNA 3D motifs. Nucleic Acids Res. 2015;43(15):7504–7520. doi: 10.1093/nar/gkv651
- Theis C, Höner Zu Siederdissen C, Hofacker IL, et al. Automatic identification of RNA 3D modules with discriminative power in RNA structural alignments. Nucleic Acids Res. 2013;41(22):9999–10009. doi: 10.1093/nar/gkt795
- Cruz JA, Westhof E. Sequence-based identification of 3D structural modules in RNA with RMDetect. Nat Methods. 2011;8(6):513–521. doi: 10.1038/nmeth.1603
- Li J, Zu W, Wang J, et al. RNA3DCNN: local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks. PLoS Comput Biol. 2018;14(11):e1006514. doi: 10.1371/journal.pcbi.1006514
- Wang Z, Xu J. A conditional random fields method for RNA sequence–structure relationships modeling and conformation sampling. Bioinformatics. 2011;27(13):i102–i110. doi: 10.1093/bioinformatics/btr232
- Andronescu M, Condon A, Hoos HH, et al. Efficient parameter estimation for RNA secondary structure prediction. Bioinformatics. 2007;23(13):i19–i28. doi: 10.1093/bioinformatics/btm223
- Rivas E, Lang R, Eddy SR. A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more. RNA. 2012;18(2):193–212. doi: 10.1261/rna.030049.111
- Danaee P, Rouches M, Wiley M, et al. bpRNA: large-scale automated annotation and analysis of RNA secondary structure. Nucleic Acids Res. 2018;46(11):5381–5394. doi: 10.1093/nar/gky285
- Leontis NB, Westhof E. Analysis of RNA motifs. Curr Opin Struct Biol. Curr Opin Struct Biol. 2003;13(3):300–308. doi: 10.1016/S0959-440X(03)00076-9
- Boniecki M, Lach G, Dawson WK, et al. SimRNA: a course-grained method for RNA folding simulations and 3D structure prediction. Nucleic Acids Res. 2016;44(7):e63. doi: 10.1093/nar/gkv1479
- Watkins AM, Rangan R, Das R. FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds. Structure. 2020;28(8):963–976. doi: 10.1016/j.str.2020.05.011
- Zeng P, Li J, Ma W, et al. Rsite: a computational method to identify the functional sites of noncoding RNAs. Sci Rep. 2015;5(1):9179. doi: 10.1038/srep09179
- Zeng P, Cui Q. Rsite2: an efficient computational method to predict the functional sites of noncoding RNAs. Sci Rep. 2016;6(1):19016. doi: 10.1038/srep19016
- Wang K, Jian Y, Wang H, et al. Rbind: computational network method to predict RNA binding sites. Bioinformatics. 2018;34(18):3131–3136. doi: 10.1093/bioinformatics/bty345
- Su H, Peng Z, Yang J, et al. Recognition of small molecule–RNA binding sites using RNA sequence and structure. Bioinformatics. 2021;37(1):36–42. doi: 10.1093/bioinformatics/btaa1092
- Xie J, Frank AT. Mining for ligandable cavities in RNA. ACS Med Chem Lett. 2021;12(6):928–934. doi: 10.1021/acsmedchemlett.1c00068
- Kozlovskii I, Popov P. Structure-based deep learning for binding site detection in nucleic acid macromolecules. NAR Genom Bioinform. 2021;3(4):lqab111. doi: 10.1093/nargab/lqab111
- Jiang Z, Xiao SR, Liu R. Dissecting and predicting different types of binding sites in nucleic acids based on structural information. Brief Bioinform. 2022;23(1):bbab411. doi: 10.1093/bib/bbab411
- Wang K, Zhou R, Wu Y, et al. Rlbind: a deep learning method to predict RNA–ligand binding sites. Brief Bioinform. 2023;24(1):bbac486. doi: 10.1093/bib/bbac486
- Haniff HS, Knerr L, Chen JL, et al. Target-directed approaches for screening small molecules against RNA targets. SLAS Discov. 2020;25(8):869–894. doi: 10.1177/2472555220922802
- Childs-Disney JL, Yang X, Gibaut QMR, et al. Targeting RNA structures with small molecules. Nat Rev Drug Discov. 2022;21(10):736–762. doi: 10.1038/s41573-022-00521-4
- Daldrop P, Reyes FE, Robinson DA, et al. Novel ligands for a purine riboswitch discovered by RNA-ligand docking. Chem Biol. 2011;18(3):324–335. doi: 10.1016/j.chembiol.2010.12.020
- Stelzer AC, Frank AT, Kratz JD, et al. Discovery of selective bioactive small molecules by targeting an RNA dynamic ensemble. Nat Chem Biol. 2011;7(8):553–559. doi: 10.1038/nchembio.596
- Ganser LR, Lee J, Rangadurai A, et al. High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble. Nat Struct Mol Biol. 2018;25(5):425–434. doi: 10.1038/s41594-018-0062-4
- Kallert E, Fischer TR, Schneider S, et al. Protein-based virtual screening tools applied for RNA-ligand docking identify new binders of the preQ1-riboswitch. J Chem Inf Model. 2022;62(17):4134–4148. doi: 10.1021/acs.jcim.2c00751
- Rocca R, Polerà N, Juli G, et al. Hit identification of novel small molecules interfering with MALAT1 triplex by a structure-based virtual screening. Arch Pharm (Weinheim). 2023;356(8):e2300134. doi: 10.1002/ardp.202300134
- Haga CL, Yang XD, Gheit IS, et al. Graph neural networks for the identification of novel inhibitors of a small RNA. SLAS Discov. 2023;28(8):402–409. doi: 10.1016/j.slasd.2023.10.002
- Pfeffer P, Gohlke H. DrugScoreRNA—knowledge-based scoring function to predict RNA-ligand interactions. J Chem Inf Model. 2007;47(5):1868–1876. doi: 10.1021/ci700134p
- Chen L, Calin GA, Zhang S. Novel insights of structure-based modeling for RNA-targeted drug discovery. J Chem Inf Model. 2012;52(10):2741–2753. doi: 10.1021/ci300320t
- Philips A, Milanowska K, Lach G, et al. LigandRNA: computational predictor of RNA-ligand interactions. RNA. 2013;19(12):1605–1616. doi: 10.1261/rna.039834.113
- Yan Z, Wang J. SPA-LN: a scoring function of ligand-nucleic acid interactions via optimizing both specificity and affinity. Nucleic Acids Res. 2017;45(12):e110. doi: 10.1093/nar/gkx255
- Chhabra S, Xie J, Frank AT. Rnaposers: machine leaning classifiers for ribonucleic acid–ligand poses. J Phys Chem B. 2020;124(22):4436–4445. doi: 10.1021/acs.jpcb.0c02322
- Stefaniak F, Bujnicki JM. AnnapuRNA: a scoring function for predicting small molecule binding poses. PLoS Comput Biol. 2021;17(2):e1008309. doi: 10.1371/journal.pcbi.1008309
- Oliver C, Mallet V, Gendron RS, et al. Augmented base pairing networks encode RNA–small molecule binding preferences. Nucleic Acids Res. 2020;48(14):7690–7699. doi: 10.1093/nar/gkaa583
- Disney MD, Winkelsas AM, Velagapudi SP, et al. Inforna 2.0: a platform for the sequence-based design of small molecules targeting structured RNAs. ACS Chem Biol. 2016;11(6):1720–1728. doi: 10.1021/acschembio.6b00001
- Zhou Y, Jiang Y, Chen SJ. RNA-ligand molecular docking: advances and challenges. Wiley Interdiscip Rev Comput Mol Sci. 2022;12(3):e1571. doi: 10.1002/wcms.1571
- Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem. 2010;53(7):2719–2740. doi: 10.1021/jm901137j
- Rizvi NF, Santa Maria JP Jr, Nahvi A, et al. Targeting RNA with small molecules: identification of selective, RNA-binding small molecules occupying drug-like chemical space. SLAS Discov. 2020;25(4):384–396. doi: 10.1177/2472555219885373
- Yazdani K, Jordan D, Yang M, et al. Machine learning informs RNA-binding chemical space. Angew Chem Int Ed Engl. 2023;62(11):e202211358. doi: 10.1002/anie.202211358
- Wicks SL, Morgan BS, Wilson AW, et al. Probing bioactive chemical space to discover RNA-targeted small molecules. bioRxiv preprint. 2023.
- Vicens Q, Mondragón E, Reyes FE, et al. Structure–activity relationship of flavin analogues that target the flavin Mononucleotide Riboswitch. ACS Chem Biol. 2018;13(10):2908–2919. doi: 10.1021/acschembio.8b00533
- Connelly CM, Numata T, Boer RE, et al. Synthetic ligands for PreQ1 riboswitches provide structural and mechanistic insights into targeting RNA tertiary structure. Nat Commun. 2019;10(1):1501. doi: 10.1038/s41467-019-09493-3
- Menichelli E, Lam BJ, Wang Y, et al. Discovery of small molecules that target a tertiary-structured RNA. Proc Natl Acad Sci U S A. 2022;119(48):e2213117119. doi: 10.1073/pnas.2213117119
- Shino A, Otsu M, Imai K, et al. Probing RNA–small molecule interactions using biophysical and computational approaches. ACS Chem Biol. 2023;18(11):2368–2376. doi: 10.1021/acschembio.3c00287
- Patwardhan NN, Cai Z, Juru AU, et al. Driving factors in amiloride recognition of HIV RNA targets. Org Biomol Chem. 2019;17(42):9313–9320. doi: 10.1039/C9OB01702J
- Cai Z, Zafferani M, Akande OM, et al. Quantitative structure-activity relationship (QSAR) study predicts small-molecule binding to RNA structure. J Med Chem. 2022;65(10):7262–7277. doi: 10.1021/acs.jmedchem.2c00254
- Szulc NA, Mackiewicz Z, Bujnicki JM, et al. Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA. Brief Bioinform. 2023;24(4):bbad187. doi: 10.1093/bib/bbad187
- Krishnan SR, Roy A, Gromiha MM. Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning. Brief Bioinform. 2024;25(2):bbae002. doi: 10.1093/bib/bbae002
- Krishnan SR, Roy A, Gromiha MM. R-SIM: a database of binding affinities for RNA-small molecule interactions. J Mol Biol. 2022;435(14):167914. doi: 10.1016/j.jmb.2022.167914
- Dobson CM. Chemical space and biology. Nature. 2004;432(7019):824–828. doi: 10.1038/nature03192
- Grimberg H, Tiwari VS, Tam B, et al. Machine learning approaches to optimize small-molecule inhibitors for RNA targeting. J Chemoinform. 2022;14(1):4. doi: 10.1186/s13321-022-00583-x
- Nakamura S, Ueno H, inventors; Takeda Pharmaceutical Company Limited, assignee. Method of screening compound regulating the translation of specific mRNA. WIPO (PCT) patent W02006054788A1.
- Rezayi S, Kalhori SRN, Saeedi S. Effectiveness of artificial intelligence for personalized medicine in neoplasms: a systematic review. Biomed Res Int. 2022;2022:7842566. doi: 10.1155/2022/7842566
- Ganser LR, Kelly ML, Herschlag D, et al. The roles of structural dynamics in the cellular functions of RNAs. Nat Rev Mol Cell Biol. 2019;20(8):474–489. doi: 10.1038/s41580-019-0136-0
- Šponer J, Bussi G, Krepl M, et al. RNA structural dynamics as captured by molecular simulations: a comprehensive overview. Chem Rev. 2018;118(8):4177–4338. doi: 10.1021/acs.chemrev.7b00427
- Bergonzo C, Henriksen NM, Roe DR, et al. Highly sampled tetranucleotide and tetraloop motifs enable evaluation of common RNA force fields. RNA. 2015;21(9):1578–1590. doi: 10.1261/rna.051102.115
- Tran T, Disney MD. Identifying the preferred RNA motifs and chemotypes that interact by probing millions of combinations. Nat Commun. 2012;3(1):1125. doi: 10.1038/ncomms2119
- Morgan BS, Forte JE, Culver RN, et al. Discovery of key physicochemical, structural, and spatial properties of RNA-targeted bioactive ligands. Angew Chem Int Ed Engl. 2017;56(43):13498–13502. doi: 10.1002/anie.201707641
- Morgan BS, Sanaba BG, Donlic A, et al. R-BIND: and interactive database for exploring and developing RNA-targeted chemical probes. ACS Chem Biol. 2019;14(12):2691–2700. doi: 10.1021/acschembio.9b00631
- Haniff HS, Knerr L, Liu X, et al. Design of a small molecule that stimulates vascular endothelial growth factor a enabled by screening RNA fold–small molecule interactions. Nat Chem. 2020;12(10):952–961. doi: 10.1038/s41557-020-0514-4
- Padroni G, Patwardhan NN, Schapira M, et al. Systematic analysis of the interactions driving small molecule-RNA recognition. RSC Med Chem. 2020;11(7):802–813. doi: 10.1039/D0MD00167H
- Donlic A, Swanson EG, Chiu LY, et al. R-BIND 2.0: an updated database of bioactive RNA-targeting small molecules and associated RNA secondary structures. ACS Chem Biol. 2022;17(6):1556–1566. doi: 10.1021/acschembio.2c00224
- Walters WP, Murcko M. Assessing the impact of generative AI on medicinal chemistry. Nat Biotechnol. 2020;38(2):143–145. doi: 10.1038/s41587-020-0418-2
- Mervin L, Genheden S, Engkvist O. AI for drug design: from explicit rules to deep learning. Artif Intell Life Sci. 2022;2:100041. doi: 10.1016/j.ailsci.2022.100041
- Kierner S, Kucharski J, Kerner Z. Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decisions: a scoping review. J Biomed Inform. 2023;144:104428. doi: 10.1016/j.jbi.2023.104428
- Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: addressing ethical challenges. PLoS Med. 2018;15(11):e1002689. doi: 10.1371/journal.pmed.1002689