Computer-Aided Drug Discovery and Design: Recent Advances and Future Prospects.
ADME
ADMET
Antitarget
Collaborative knowledge
Collective knowledge
Computer-aided drug design
Computer-assisted drug design
Computer-guided drug design
Cooperative knowledge
Data integration
De novo drug design
Deep learning
Drug design
Druggability
Druggability prediction
Ensemble learning
Fragment-based drug design
In silico screening
Ligand-based approaches
Machine learning
Molecular dynamics
Molecular optimization
Molecular target
Omics
Open source
Pharmacological target
Pharmacophore
Pocket prediction
QSAR
Structure-based approaches
Target validation
Target-based approaches
Virtual screening
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2024
2024
Historique:
medline:
8
9
2023
pubmed:
7
9
2023
entrez:
7
9
2023
Statut:
ppublish
Résumé
Computer-aided drug discovery and design involve the use of information technologies to identify and develop, on a rational ground, chemical compounds that align a set of desired physicochemical and biological properties. In its most common form, it involves the identification and/or modification of an active scaffold (or the combination of known active scaffolds), although de novo drug design from scratch is also possible. Traditionally, the drug discovery and design processes have focused on the molecular determinants of the interactions between drug candidates and their known or intended pharmacological target(s). Nevertheless, in modern times, drug discovery and design are conceived as a particularly complex multiparameter optimization task, due to the complicated, often conflicting, property requirements.This chapter provides an updated overview of in silico approaches for identifying active scaffolds and guiding the subsequent optimization process. Recent groundbreaking advances in the field have also analyzed the integration of state-of-the-art machine learning approaches in every step of the drug discovery process (from prediction of target structure to customized molecular docking scoring functions), integration of multilevel omics data, and the use of a diversity of computational approaches to assist target validation and assess plausible binding pockets.
Identifiants
pubmed: 37676590
doi: 10.1007/978-1-0716-3441-7_1
doi:
Substances chimiques
Hydrolases
EC 3.-
Types de publication
Review
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1-20Informations de copyright
© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Klabunde T, Everts A (2005) GPCR antitarget modeling: pharmacophore models for biogenic amine binding GPCRs to avoid GPCR-mediated side effects. Chembiochem 6:876–889
doi: 10.1002/cbic.200400369
pubmed: 15791686
Raschi E, Vasina V, Poluzzi E et al (2008) The hERG K+ channel: target and antitarget strategies in drug development. Pharmacol Res 57:181–195
doi: 10.1016/j.phrs.2008.01.009
pubmed: 18329284
Crivori P (2008) Computational models for P-glycoprotein substrates and inhibitors. In: Vaz RJ, Klabunde T (eds) Anti-atrgets: prediction and prevention of drug side effects. Wiley-VCH, Weinheim
Zamora I (2008) Site of metabolism predictions: facts and experiences. In: Vaz RJ, Klabunde T (eds) Anti-targets: prediction and prevention of drug side effects. Wiley-VCH, Weinheim
Fallico M, Alberca LN, Prada Gori DN et al (2022) Machine learning search of novel selective NaV1.2 and NaV1.6 inhibitors as potential treatment against Dravet syndrome. In: Ribeiro PRDA, Cota VR, Barone DAC, de Oliveira ACM (eds) Computational neuroscience. LAWCN 2021. Communications in computer and information science, vol 1519. Springer, Cham
Fatoba AJ, Okpeku M, Adeleke MA (2021) Subtractive genomics approach for identification of novel therapeutic drug targets in Mycoplasma genitalium. Pathogens 10:921
pmcid: 8402164
doi: 10.3390/pathogens10080921
pubmed: 34451385
Süntar I (2020) Importance of ethnopharmacological studies in drug discovery: role of medicinal plants. Phytochem Rev 19:1199–1209
doi: 10.1007/s11101-019-09629-9
Entzeroth M, Flotow H, Condron P (2009) Overview of high-throughput screening. Curr Protoc Pharmacol Chapter 9:Unit 9.4
Maia EHB, Assis LC, de Oliveira TA et al (2020) Structure-based virtual screening: from classical to artificial intelligence. Front Chem 8:343
pmcid: 7200080
doi: 10.3389/fchem.2020.00343
pubmed: 32411671
Mouchlis VD, Afantitis A, Serra A et al (2021) Advances in de novo drug design: from conventional to machine learning methods. Int J Mol Sci 22:1676
pmcid: 7915729
doi: 10.3390/ijms22041676
pubmed: 33562347
Kirsch P, Hartman AM, Hirsch AKH et al (2019) Concepts and core principles of fragment-based drug design. Molecules 24:4309
pmcid: 6930586
doi: 10.3390/molecules24234309
pubmed: 31779114
Romano P, Giugno R, Pulvirenti A (2011) Tools and collaborative environments for bioinformatics research. Brief Bioinform 12:549–561
pmcid: 3220874
doi: 10.1093/bib/bbr055
pubmed: 21984743
Gorgulla C, Boeszoermenyi A, Wang ZF et al (2020) An open-source drug discovery platform enables ultra-large virtual screens. Nature 580:663–668
pmcid: 8352709
doi: 10.1038/s41586-020-2117-z
pubmed: 32152607
Cox PB, Gupta R (2022) Contemporary computational applications and tools in drug discovery. ACS Med Chem Lett 13:1016–1029
pmcid: 9290028
doi: 10.1021/acsmedchemlett.1c00662
pubmed: 35859884
Prada Gori DN, Alberca LN, Rodriguez S et al (2022) LIDeB Tools: a Latin American resource of freely available, open-source cheminformatics apps. Artif Intell Life Sci 2:10049
Hartenfeller M, Schneider G (2011) De novo drug design. Methods Mol Biol 672:299–323
doi: 10.1007/978-1-60761-839-3_12
pubmed: 20838974
Kuttruff CA, Eastgate MD, Baran PS (2014) Natural product synthesis in the age of scalability. Nat Prod Rep 31:419–432
doi: 10.1039/C3NP70090A
pubmed: 24337165
Nicolaou CA, Brown N (2013) Multi-objective optimization methods in drug design. Drug Discov Today Technol 10:e427–e435
doi: 10.1016/j.ddtec.2013.02.001
pubmed: 24050140
Talevi A (2016) Tailored multi-target agents. Applications and design considerations. Curr Pharm Des 22:3164–3170
doi: 10.2174/1381612822666160308141203
pubmed: 26951108
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25
doi: 10.1016/S0169-409X(96)00423-1
Pajouhesh H, Lenz GR (2005) Medicinal chemical properties of successful central nervous system drugs. NeuroRx 2:542–553
doi: 10.1602/neurorx.2.4.541
Gupta S, Kesarla R, Omri A (2013) Formulation strategies to improve the bioavailability of poorly absorbed drugs with special emphasis on self-emulsifying systems. ISRN Pharm 2013:848043
pmcid: 3888743
pubmed: 24459591
Miller DC, Klute W, Calabrese A et al (2009) Optimising metabolic stability in lipophilic chemical space: the identification of a metabolic stable pyrazolopyrimidine CRF-1 receptor antagonist. Bioorg Med Chem Lett 19:6144–6147
doi: 10.1016/j.bmcl.2009.09.016
pubmed: 19782566
Wager TT, Hou X, Verhoest PR et al (2016) Central nervous system multiparameter optimization desirability: application in drug discovery. ACS Chem Neurosci 7:767–775
doi: 10.1021/acschemneuro.6b00029
pubmed: 26991242
Glen RC, Galloway WR, Spring DR et al (2016) Multiple-parameter optimization in drug discovery: example of the 5-HT1B GPCR. Mol Inform 35:599–605
doi: 10.1002/minf.201600056
pubmed: 27870241
Ghose AK, Ott GR, Hudkins RL (2017) Technically Extended MultiParameter Optimization (TEMPO): an advanced robust scoring scheme to calculate central nervous system druggability and monitor lead optimization. ACS Chem Neurosci 8:147–154
doi: 10.1021/acschemneuro.6b00273
pubmed: 27741392
Winter R, Montanari F, Steffen A et al (2019) Efficient multi-objective molecular optimization in a continuous latent space. Chem Sci 10:8016–8024
pmcid: 6836962
doi: 10.1039/C9SC01928F
pubmed: 31853357
Pennington LD, Muegge I (2021) Holistic drug design for multiparameter optimization in modern small molecule drug discovery. Bioorg Med Chem Lett 41:128003
doi: 10.1016/j.bmcl.2021.128003
pubmed: 33798703
He X (2009) Integration of physical, chemical, mechanical and biopharmaceutical properties in solid dosage oral form development. In: Solid dosage oral forms: pharmaceutical theory and practice. Academic Press, Burlington
Csermely P, Korcsmáros T, Kiss HJ et al (2013) Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 138:333–408
pmcid: 3647006
doi: 10.1016/j.pharmthera.2013.01.016
pubmed: 23384594
Wang J, Guo Z, Fu Y et al (2017) Weak-binding molecules are not drugs?-toward a systematic strategy for finding effective weak-binding drugs. Brief Bioinform 18:321–332
pubmed: 26962012
Talevi A (2022) Antiseizure medication discovery: recent and future paradigm shifts. Epilepsia Open 7(Suppl 1):S133–S141
pmcid: 9340309
pubmed: 35090197
Gashaw I, Ellinghaus P, Sommer A et al (2011) What makes a good drug target. Drug Discov Today 16:1037–1043
doi: 10.1016/j.drudis.2011.09.007
pubmed: 21945861
Knowles J, Gromo G (2003) Target selection in drug discovery. Nat Rev Drug Discov 2:63–69
doi: 10.1038/nrd986
pubmed: 12509760
Schmidtke P, Barril X (2010) Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J Med Chem 53:5858–5867
doi: 10.1021/jm100574m
pubmed: 20684613
Yuan Y, Pei J, Lai L (2013) Binding site detection and druggability prediction of protein targets for structure-based drug design. Curr Pharm Des 19:2326–2333
doi: 10.2174/1381612811319120019
pubmed: 23082974
Barril X (2013) Druggability predictions: methods, limitations and applications. Wires Comput Mol Sci 3:327–338
doi: 10.1002/wcms.1134
Talevi A, Carrillo C, Comini M (2019) The thiol-polyamine metabolism of Trypanosoma cruzi: molecular targets and drug repurposing strategies. Curr Med Chem 26:6614–6635
doi: 10.2174/0929867325666180926151059
pubmed: 30259812
Tonge PJ (2018) Drug-target kinetics in drug discovery. ACS Chem Neurosci 9:29–39
doi: 10.1021/acschemneuro.7b00185
pubmed: 28640596
Feng Y, Wang Q, Wang T (2017) Drug target protein-protein interaction networks: a systematic perspective. Biomed Res Int 2017:1289259
pmcid: 5485489
doi: 10.1155/2017/1289259
pubmed: 28691014
Viacava Follis A (2021) Centrality of drug targets in protein networks. BMC Bioinf 22:527
doi: 10.1186/s12859-021-04342-x
Sabetian S, Shamsir MS (2019) Computer aided analysis of disease linked protein networks. Bioinformation 15:513–522
pmcid: 6704336
doi: 10.6026/97320630015513
pubmed: 31485137
Casas AI, Hassan AA, Larsen SJ et al (2019) From single drug targets to synergistic network pharmacology in ischemic stroke. Proc Natl Acad Sci U S A 116:7129–7136
pmcid: 6452748
doi: 10.1073/pnas.1820799116
pubmed: 30894481
Schidlitzki A, Bascuñana P, Srivastava PK et al (2020) Proof-of-concept that network pharmacology is effective to modify development of acquired temporal lobe epilepsy. Neurobiol Dis 134:104664
doi: 10.1016/j.nbd.2019.104664
pubmed: 31678583
Kim B, Jo J, Han J et al (2017) In silico re-identification of properties of drug target proteins. BMC Bioinf 18:248
doi: 10.1186/s12859-017-1639-3
Dezső Z, Ceccarelli M (2020) Machine learning prediction of oncology drug targets based on protein and network properties. BMC Bioinf 21:104
doi: 10.1186/s12859-020-3442-9
Chen S, Jiang H, Cao Y et al (2016) Drug target identification using network analysis: taking active components in Sini decoction as an example. Sci Rep 6:24245
pmcid: 4837341
doi: 10.1038/srep24245
pubmed: 27095146
Ji X, Freudenberg JM, Agarwal P (2019) Integrating biological networks for drug target prediction and prioritization. Methods Mol Biol 1903:203–218
doi: 10.1007/978-1-4939-8955-3_12
pubmed: 30547444
Capra JA, Singh M (2007) Predicting functionally important residues from sequence conservation. Bioinformatics 23:1875–1882
doi: 10.1093/bioinformatics/btm270
pubmed: 17519246
Yang J, Roy A, Zhang Y (2013) Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics 29:2588–2595
pmcid: 3789548
doi: 10.1093/bioinformatics/btt447
pubmed: 23975762
Han B, Salituro FG, Blanco MJ (2020) Impact of allosteric modulation in drug discovery: innovation in emerging chemical modalities. ACS Med Chem Lett 11:1810–1819
pmcid: 7549105
doi: 10.1021/acsmedchemlett.9b00655
pubmed: 33062158
Liu T, Ish-Shalom S, Torng W et al (2018) Biological and functional relevance of CASP predictions. Proteins 86(Suppl 1):374–386
doi: 10.1002/prot.25396
pubmed: 28975675
Clark JJ, Orban ZJ, Carlson HA (2020) Predicting binding sites from unbound versus bound protein structures. Sci Rep 10:15856
pmcid: 7522209
doi: 10.1038/s41598-020-72906-7
pubmed: 32985584
Kuzmanic A, Bowman GR, Juarez-Jimenez J et al (2020) Investigating cryptic binding sites by molecular dynamics simulations. Acc Chem Res 53:654–661
pmcid: 7263906
doi: 10.1021/acs.accounts.9b00613
pubmed: 32134250
Smith RD, Carlson HA (2021) Identification of cryptic binding sites using MixMD with standard and accelerated molecular dynamics. J Chem Inf Model 61:1287–1299
pmcid: 8091066
doi: 10.1021/acs.jcim.0c01002
pubmed: 33599485
Paul F, Weikl TR (2016) How to distinguish conformational selection and induced fit based on chemical relaxation rates. PLoS Comput Biol 12:e1005067
pmcid: 5026370
doi: 10.1371/journal.pcbi.1005067
pubmed: 27636092
Vajda S, Beglov D, Wakefield AE et al (2018) Cryptic binding sites on proteins: definition, detection, and druggability. Curr Opin Chem Biol 44:1–8
pmcid: 6088748
doi: 10.1016/j.cbpa.2018.05.003
pubmed: 29800865
Martinez-Rosell G, Lovera S, Sands ZA et al (2020) PlayMolecule CrypticScout: predicting protein cryptic sites using mixed-solvent molecular simulations. J Chem Inf Model 60:2314–2324
doi: 10.1021/acs.jcim.9b01209
pubmed: 32175736
Zheng W (2021) Predicting cryptic ligand binding sites based on normal modes guided conformational sampling. Proteins 89:416–426
doi: 10.1002/prot.26027
pubmed: 33244830
Aromolaran O, Aromolaran D, Isewon I et al (2021) Machine learning approach to gene essentiality prediction: a review. Brief Bioinform 22(5):bbab128
doi: 10.1093/bib/bbab128
pubmed: 33842944
Basler G (2015) Computational prediction of essential metabolic genes using constraint-based approaches. Gene Essentiality 1279:183–204
doi: 10.1007/978-1-4939-2398-4_12
Pushpakom S, Iorio F, Eyers PA et al (2019) Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov 18:41–58
doi: 10.1038/nrd.2018.168
pubmed: 30310233
Talevi A, Bellera CL (2020) Challenges and opportunities with drug repurposing: finding strategies to find alternative uses of therapeutics. Expert Opin Drug Discov 15:397–401
doi: 10.1080/17460441.2020.1704729
pubmed: 31847616
Szymanski P, Markowicz M, Mikiciuk-Olasik E (2012) Adaptation of high-throughput screening in drug discovery – toxicological screening. Int J Mol Sci 13:427–452
doi: 10.3390/ijms13010427
pubmed: 22312262
Harris CJ, Hill RD, Sheppard DW, Slater MJ, Stouten PF (2011) The design and application of target-focused compound libraries. Comb Chem High Throughput Screen 14(6):521–531
pmcid: 3182092
doi: 10.2174/138620711795767802
pubmed: 21521154
Welsch ME, Snyder SA, Stockwell BR (2010) Privileged scaffolds for library design and drug discovery. Curr Opin Chem Biol 14:347–361
pmcid: 2908274
doi: 10.1016/j.cbpa.2010.02.018
pubmed: 20303320
Jumper J, Evans R, Pritzel A et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589
pmcid: 8371605
doi: 10.1038/s41586-021-03819-2
pubmed: 34265844
Baek M, DiMaio F, Anishchenko I et al (2021) Accurate prediction of protein structures and interactions using a three-track neural network. Science 373:871–876
pmcid: 7612213
doi: 10.1126/science.abj8754
pubmed: 34282049
Lin Z, Akin H, Rao R et al (2022) Language models of protein sequences at the scale of evolution enable accurate structure prediction. bioRxiv 2022.07.20.500902
Mirdita M, Schütze K, Moriwaki Y et al (2022) ColabFold: making protein folding accessible to all. Nat Methods 19:679–682
pmcid: 9184281
doi: 10.1038/s41592-022-01488-1
pubmed: 35637307
Varadi M, Anyango S, Deshpande M et al (2022) AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 50:D439–D444
doi: 10.1093/nar/gkab1061
pubmed: 34791371
Procacci P (2016) Reformulating the entropic contribution of molecular docking scoring functions. J Comput Chem 37(19):1819–1827
doi: 10.1002/jcc.24397
pubmed: 27231844
Gilson MK, Zhou HX (2007) Calculation of protein-ligand binding affinities. Annu Rev Biophys Biomol Struct 36:21–42
doi: 10.1146/annurev.biophys.36.040306.132550
pubmed: 17201676
Bello M, Martínez-Archundia M, Correa-Basurto J (2013) Automated docking for novel drug discovery. Expert Opin Drug Discov 8:821–834
doi: 10.1517/17460441.2013.794780
pubmed: 23642085
Bodnarchuck MS (2016) Water, water, everywhere… It’s time to stop and think. Drug Discov Today 21:1139–1146
doi: 10.1016/j.drudis.2016.05.009
Mysinger MM, Schoichet BK (2010) Rapid context-dependent ligand desolvation in molecular docking. J Chem Inf Model 50:1561–1573
doi: 10.1021/ci100214a
pubmed: 20735049
Li H, Sze KH, Lu G et al (2020) Machine-learning scoring functions for structure-based virtual screening. Wires Comput Mol Sci 11:e1478
doi: 10.1002/wcms.1478
Zhang X, Shen C, Guo X et al (2021) ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions. J Cheminform 13:6
pmcid: 7860246
doi: 10.1186/s13321-021-00486-3
pubmed: 33541407
Yang C, Chen EA, Zhang Y (2022) Protein-ligand docking in the machine-learning era. Molecules 27:4568
pmcid: 9323102
doi: 10.3390/molecules27144568
pubmed: 35889440
Ge H, Wang Y, Li C et al (2013) Molecular dynamics-based virtual screening: accelerating the drug discovery process by high-performance computing. J Chem Inf Model 53:2757–2764
doi: 10.1021/ci400391s
pubmed: 24001302
Wang L, Wu Y, Deng Y et al (2015) Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 137:2695–2703
doi: 10.1021/ja512751q
pubmed: 25625324
Llanos MA, Alberca LN, Larrea SCV et al (2022) Homology modeling and molecular dynamics simulations of Trypanosoma cruzi phosphodiesterase b1. Chem Biodivers 19:e202100712
doi: 10.1002/cbdv.202100712
pubmed: 34813143
Lavechia A (2015) Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today 20:318–331
doi: 10.1016/j.drudis.2014.10.012
Lemmen C, Zimmermann M, Lengauer T (2002) Multiple molecular superpositioning as an effective tool for virtual database screening. In: Virtual screening: an alternative or complement to high-throughput screening? 1st edn. Kluwer Academic Publishers, Marburg
Kristensen TG, Nielsen J, Pedersen CNS (2013) Methods for similarity-based virtual screening. Comput Struct Biotechnol J 5:e201302009
pmcid: 3962175
doi: 10.5936/csbj.201302009
pubmed: 24688702
Talevi A, Bruno-Blanch LE (2016) Virtual screening applications in the search of novel antiepileptic drug candidates. In: Antiepileptic drug discovery. Novel approaches. Humana Press, New York
doi: 10.1007/978-1-4939-6355-3
Schneidman-Duhovny D, Dror O, Inbar Y et al (2008) Deterministic pharmacophore detection via multiple flexible alignment of drug-like molecules. J Comput Biol 15:737–754
pmcid: 2699263
doi: 10.1089/cmb.2007.0130
pubmed: 18662104
Cottrell SJ, Gillet VJ, Taylor R et al (2004) Generation of multiple pharmacophore hypothesis using multiobjective optimization techniques. J Comput Aided Mol Des 18:665–682
doi: 10.1007/s10822-004-5523-7
pubmed: 15865060
Pirhadi S, Shiri F, Ghasemi JB (2013) Methods and applications of structure based pharmacophores in drug discovery. Curr Top Med Chem 13:1036–1047
doi: 10.2174/1568026611313090006
pubmed: 23651482
Zhang Q, Muegge I (2006) Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: ranking, voting, and consensus scoring. J Med Chem 9:1536–1548
doi: 10.1021/jm050468i
Krüger DM, Evers A (2010) Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors. ChemMedChem 5:148–158
doi: 10.1002/cmdc.200900314
pubmed: 19908272
Talevi A, Gavernet L, Bruno-Blanch LE (2009) Combined virtual screening strategies. Curr Comput Aided Drug Des 5:23–37
doi: 10.2174/157340909787580854
Pouliot M, Jeanmart S (2016) Pan Assay Interference Compounds (PAINS) and other promiscuous compounds in antifungal research. J Med Chem 59:497–503
doi: 10.1021/acs.jmedchem.5b00361
pubmed: 26313340
Walters WP, Stahl MT, Murcko MA (1998) Virtual screening – an overview. Drug Discov Today 3:160–178
doi: 10.1016/S1359-6446(97)01163-X
Zhu T, Cao S, Su PC et al (2013) Hit identification and optimization in virtual screening: practical recommendations based upon a critical literature analysis. J Med Chem 56:6560–6572
pmcid: 3772997
doi: 10.1021/jm301916b
pubmed: 23688234
Ripphausen P, Nisius B, Pletason L et al (2010) Quo vadis, virtual screening? A comprehensive survey of prospective applications. J Med Chem 53:8461–8467
doi: 10.1021/jm101020z
pubmed: 20929257
Neetoo-Isseliee Z, MacKenzie AE, Southern C et al (2013) High-throughput identification and characterization of novel, species-selective GPR35 agonists. J Pharmacol Exp Ther 344:568–578
doi: 10.1124/jpet.112.201798
Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3:711–716
doi: 10.1038/nrd1470
pubmed: 15286737
Schuster D, Laggner C, Langer T (2005) Why drugs fail – a study on side effects in new chemical entities. Curr Pharm Des 11:3545–3559
doi: 10.2174/138161205774414510
pubmed: 16248807
Talevi A (2016) Computational approaches for innovative antiepileptic drug discovery. Expert Opin Drug Discov 11:1001–1016
doi: 10.1080/17460441.2016.1216965
pubmed: 27454246
Wang S, Dong G, Sheng C (2019) Structural simplification of natural products. Chem Rev 119:4180–4220
doi: 10.1021/acs.chemrev.8b00504
pubmed: 30730700
Brown N, Lewis RA (2006) Exploiting QSAR methods in lead optimization. Curr Opin Drug Discov Devel 9:419–424
pubmed: 16889226
Wong WWL, Burkowski FJ (2009) A constructive approach for discovering new drug leads: using a kernel methodology for the inverse-QSAR problem. J Cheminform 1:4
pmcid: 2816860
doi: 10.1186/1758-2946-1-4
pubmed: 20142987
Miyako T, Kaneko H, Funatsu K (2016) Inverse QSPR/QSAR analysis for chemical structure generation (from y to x). J Chem Inf Model 56:286–299
doi: 10.1021/acs.jcim.5b00628
Waring MJ, Arrowsmith J, Leach AR et al (2015) An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat Rev Drug Discov 14:475–486
doi: 10.1038/nrd4609
pubmed: 26091267
Cook D, Brown D, Alexander R et al (2014) Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat Rev Drug Discov 13:419–431
doi: 10.1038/nrd4309
pubmed: 24833294
Roberts RA, Kavanagh SL, Mellor HR et al (2014) Reducing attrition in drug development: smart loading preclinical safety assessment. Drug Discov Today 19:341–347
doi: 10.1016/j.drudis.2013.11.014
pubmed: 24269835
Veber DF, Johnson SR, Cheng HY et al (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623
doi: 10.1021/jm020017n
pubmed: 12036371
Price DA, Blagg J, Jones L et al (2009) Physicochemical drug properties associated with in vivo toxicological outcomes: a review. Expert Opin Drug Metab Toxicol 5:921–931
doi: 10.1517/17425250903042318
pubmed: 19519283
Sutherland JJ, Raymond JW, Stevens JL et al (2012) Relating molecular properties and in vitro assay results to in vivo drug disposition and toxicity outcomes. J Med Chem 55:6455–6466
doi: 10.1021/jm300684u
pubmed: 22716080
Doak BC, Zheng J, Dobritzsch D et al (2016) How beyond rule of 5 drugs and clinical candidates bind to their targets. J Med Chem 59:2312–2327
doi: 10.1021/acs.jmedchem.5b01286
pubmed: 26457449
Doak BC, Over B, Giordanetto F et al (2014) Oral druggable space beyond the rule of 5: insights from drugs and clinical candidates. Chem Biol 21:1115–1142
doi: 10.1016/j.chembiol.2014.08.013
pubmed: 25237858
Lipinski CA (2016) Rule of five in 2015 and beyond: target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions. Adv Drug Deliv Rev 101:34–41
doi: 10.1016/j.addr.2016.04.029
pubmed: 27154268
Bergström CAS, Charman WN, Porter CJH (2016) Computational prediction of formulation strategies for beyond-rule-of-5 compounds. Adv Drug Deliv Rev 101:6–21
doi: 10.1016/j.addr.2016.02.005
pubmed: 26928657