DOGS: Reaction-Driven de novo Design of Bioactive Compounds
Open accessAbstract
We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated 'in silico' assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H₄ receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
DOGS applies 58 well-established chemical transformation rules to a set of 25144 readily available synthetic blocks from the Sigma-Aldrich catalog.
“This algorithm applies 58 well-established chemical transformation rules to a set of 25144 readily available synthetic blocks from the Sigma-Aldrich catalog.”
From QSAR without borders (2020) · cites this paper as [154]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139 · see citing works
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(96)00423-1 · Cited by 11,484 · see citing works
Partition coefficients and their uses
Leo, A., Hansch, C., & Elkins, D. R. (1971) · Chemical Reviews · doi:10.1021/cr60274a001 · Cited by 4,253 · see citing works
ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
Irwin, J. J., & Shoichet, B. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049714+ · Cited by 4,058 · see citing works
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2012) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2012.09.019 · Cited by 3,720 · see citing works
A subset of NSAIDs lower amyloidogenic Aβ42 independently of cyclooxygenase activity
Weggen, S., Eriksen, J. L., Das, P., Sagi, S. A., Wang, R., Pietrzik, C. U., Findlay, K. A., Smith, T. E., Murphy, M. P., Bulter, T., et al. (2001) · Nature · doi:10.1038/35102591 · Cited by 1,388 · see citing works
Self-citationTrypsin Cleaves Exclusively C-terminal to Arginine and Lysine Residues
Olsen, J. V., Ong, S., & Mann, M. (2004) · Molecular & Cellular Proteomics · doi:10.1074/mcp.t400003-mcp200 · Cited by 1,258 · see citing works
The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo- and Bioinformatics
Steinbeck, C., Han, Y., Kühn, S., Horlacher, O., Luttmann, E., & Willighagen, E. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci025584y · Cited by 1,111 · see citing works
Alzheimer's disease: strategies for disease modification
Citron, M. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd2896 · Cited by 1,060 · see citing works
Computer-based de novo design of drug-like molecules
Schneider, G., & Fechner, U. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1799 · Cited by 956 · see citing works
Self-citationLewell, X. Q., Judd, D. B., Watson, S. P., & Hann, M. M. (1998) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci970429i · Cited by 695 · see citing works
Steinbeck, C., Hoppe, C., Kühn, S., Floris, M., Guha, R., & Willighagen, E. (2006) · Current Pharmaceutical Design · doi:10.2174/138161206777585274 · Cited by 463 · see citing works
A Potent and Selective Histamine H4 Receptor Antagonist with Anti-Inflammatory Properties
Thurmond, R. L., Desai, P. J., Dunford, P. J., Fung‐Leung, W., Hofstra, C. L., Jiang, W., Nguyen, S. M., Riley, J. P., Sun, S., Williams, K. N., et al. (2004) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.103.061754 · Cited by 354 · see citing works
Feature trees: A new molecular similarity measure based on tree matching
Rarey, M., & Dixon, J. S. (1998) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1008068904628 · Cited by 329 · see citing works
Diverse compounds mimic Alzheimer disease–causing mutations by augmenting Aβ42 production
Kukar, T. L., Murphy, M. P., Eriksen, J. L., Sagi, S. A., Weggen, S., Smith, T. E., Ladd, T. B., Khan, M. A., Kache, R., Beard, J. D., et al. (2005) · Nature Medicine · doi:10.1038/nm1235 · Cited by 284 · see citing works
Self-citationDe novo design of molecular architectures by evolutionary assembly of drug-derived building blocks
Schneider, G., Lee, M., Ståhl, M., & Schneider, P. (2000) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1008184403558 · Cited by 252 · see citing works
Self-citationBioisosteric Replacement and Scaffold Hopping in Lead Generation and Optimization
Langdon, S. R., Ertl, P., & Brown, N. (2010) · Molecular Informatics · doi:10.1002/minf.201000019 · Cited by 227 · see citing works
Strategic Pooling of Compounds for High-Throughput Screening
Hann, M. M., Hudson, B. D., Lewell, X. Q., Lifely, R., Miller, L., & Ramsden, N. G. (1999) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci990423o · Cited by 227 · see citing works
SYNOPSIS: SYNthesize and OPtimize System in Silico
Vinkers, H. M., de Jonge, M. R., Daeyaert, F. F. D., Heeres, J., Koymans, L. M. H., van Lenthe, J. H., Lewi, P. J., Timmerman, H., Aken, K. V., & Janssen, P. A. J. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm030809x · Cited by 225 · see citing works
Fragment Based Design of New H4Receptor−Ligands with Anti-inflammatory Properties in Vivo
Smits, R. A., Lim, H. D., Hanzer, A., Zuiderveld, O. P., Guaita, E., Adami, M., Coruzzi, G., Leurs, R., & de Esch, I. J. P. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm7014217 · Cited by 188 · see citing works
Lipophilicity in Molecular Modeling
Testa, B., Carrupt, P., Gaillard, P., Billois, F., & Weber, P. B. (1996) · Pharmaceutical Research · doi:10.1023/a:1016024005429 · Cited by 164 · see citing works
Hartenfeller, M., & Schneider, G. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-839-3_12 · Cited by 160 · see citing works
Self-citationHistamine H3and H4receptors as novel drug targets
Tiligada, E., Zampeli, E., Sander, K., & Stark, H. (2009) · Expert Opinion on Investigational Drugs · doi:10.1517/14728220903188438 · Cited by 141 · see citing works
Self-citationNew Developments in the Origins of the Homochirality of Biologically Relevant Molecules
Buschmann, H., Thede, R., & Heller, D. (2000) · Angewandte Chemie International Edition · doi:10.1002/1521-3773(20001117)39:22 · Cited by 111 · see citing works
Self-citationFlux (1): A Virtual Synthesis Scheme for Fragment-Based de Novo Design
Fechner, U., & Schneider, G. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci0503560 · Cited by 108 · see citing works
Self-citationSchneider, E. H., Schnell, D., Papa, D., & Seifert, R. (2009) · Biochemistry · doi:10.1021/bi802050d · Cited by 96 · see citing works
De novodesign: balancing novelty and confined chemical space
Kutchukian, P. S., & Shakhnovich, E. I. (2010) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2010.497534 · Cited by 92 · see citing works
Schneider, G., Clément‐Chomienne, O., Hilfiger, L., Schneider, P., Kirsch, S. F., Böhm, H., & Neidhart, W. L. (2000) · PubMed · Cited by 85 · see citing works
Self-citationRecent developments in de novo design and scaffold hopping.
Mauser, H., & Guba, W. (2008) · PubMed · Cited by 76 · see citing works
Sander, K., Kottke, T., Tanrikulu, Y., Proschak, E., Weizel, L., Schneider, E. H., Seifert, R., Schneider, G., & Stark, H. (2009) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2009.08.059 · Cited by 72 · see citing works
Self-citationConcept of Combinatorial De Novo Design of Drug‐like Molecules by Particle Swarm Optimization
Hartenfeller, M., Proschak, E., Schüller, A., & Schneider, G. (2008) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2008.00672.x · Cited by 69 · see citing works
Self-citationKutchukian, P. S., Lou, D., & Shakhnovich, E. I. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci9000458 · Cited by 69 · see citing works
Kernel Approach to Molecular Similarity Based on Iterative Graph Similarity
Rupp, M., Proschak, E., & Schneider, G. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700274r · Cited by 68 · see citing works
Self-citationCostanzo, M. J., Almond, H. R., Hecker, L. R., Schott, M. R., Yabut, S. C., Zhang, H., Andrade‐Gordon, P., Corcoran, T. W., Giardino, E. C., Kauffman, J. A., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0303857 · Cited by 68 · see citing works
Flux (2): Comparison of Molecular Mutation and Crossover Operators for Ligand-Based de Novo Design
Fechner, U., & Schneider, G. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci6005307 · Cited by 66 · see citing works
Self-citationPotential utility of histamine H3 receptor antagonist pharmacophore in antipsychotics
von Coburg, Y., Kottke, T., Weizel, L., Ligneau, X., & Stark, H. (2008) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2008.09.012 · Cited by 57 · see citing works
Self-citationFeng, D.-M., Gardell, S. J., Lewis, S. D., Bock, M. G., Chen, Z., Freidinger, R. M., Naylor-Olsen, A. M., Ramjit, H. G., Woltmann, R. F., Baskin, E. P., et al. (1997) · Journal of Medicinal Chemistry · doi:10.1021/jm970493r · Cited by 57 · see citing works
Rupp, M., Schroeter, T. S., Steri, R., Zettl, H., Proschak, E., Hansen, K., Rau, O., Schwarz, O., Müller‐Kuhrt, L., Schubert‐Zsilavecz, M., et al. (2009) · ChemMedChem · doi:10.1002/cmdc.200900469 · Cited by 54 · see citing works
Self-citationMolecular Complexity Analysis of de Novo Designed Ligands
Boda, K., & Johnson, A. P. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm050054p · Cited by 48 · see citing works
Identification of 2-arylbenzimidazoles as potent human histamine H4 receptor ligands
Lee-Dutra, A., Arienti, K. L., Buzard, D. J., Hack, M. D., Khatuya, H., Desai, P. J., Nguyen, S. M., Thurmond, R. L., Karlsson, L., Edwards, J. P., et al. (2006) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2006.08.117 · Cited by 46 · see citing works
Mathematical Basis of Ring-Finding Algorithms in CIDS
Plotkin, M. (1971) · Journal of Chemical Documentation · doi:10.1021/c160040a013 · Cited by 41 · see citing works
Piperazinyl pyrimidine derivatives as potent γ-secretase modulators
Rivkin, A., Ahearn, S. P., Chichetti, S. M., Kim, Y. R., Li, C., Rosenau, A., Kattar, S. D., Jung, J. O., Shah, S., Hughes, B. L., et al. (2009) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2009.11.101 · Cited by 41 · see citing works
Reaction-Driven De Novo Design, Synthesis and Testing of Potential Type II Kinase Inhibitors
Schneider, G., Geppert, T., Hartenfeller, M., Reisen, F., Klenner, A., Reutlinger, M., Hähnke, V. D., Hiss, J. A., Zettl, H., Keppner, S., et al. (2011) · Future Medicinal Chemistry · doi:10.4155/fmc.11.8 · Cited by 40 · see citing works
Self-citationHieke, M., Neß, J., Steri, R., Dittrich, M., Greiner, C., Werz, O., Baumann, K., Schubert‐Zsilavecz, M., Weggen, S., & Zettl, H. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm1003073 · Cited by 36 · see citing works
Self-citationTanrikulu, Y., Proschak, E., Werner, T., Geppert, T., Todoroff, N. K., Klenner, A., Kottke, T., Sander, K., Schneider, E. H., Seifert, R., et al. (2009) · ChemMedChem · doi:10.1002/cmdc.200800443 · Cited by 32 · see citing works
Self-citationNew Orally Active Serine Protease Inhibitors
Senokuchi, K., Nakai, H., Nakayama, Y., Odagaki, Y., Sakaki, K., Kato, M., Maruyama, T., Miyazaki, T., Ito, H., Kamiyasu, K., et al. (1995) · Journal of Medicinal Chemistry · doi:10.1021/jm00014a003 · Cited by 29 · see citing works
Exploring the chemical space of γ-secretase modulators
Zettl, H., Weggen, S., Schneider, P., & Schneider, G. (2010) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2010.05.007 · Cited by 26 · see citing works
Self-citationNavigation in Chemical Space: Ligand‐Based Design of Focused Compound Libraries
Schneider, G., & Schneider, P. (2004) · Methods and principles in medicinal chemistry · doi:10.1002/3527603948.ch13 · Cited by 21 · see citing works
Self-citationDirect thrombin inhibitors: current status and future prospects
Menear, K. A. (1999) · Expert Opinion on Investigational Drugs · doi:10.1517/13543784.8.9.1373 · Cited by 19 · see citing works
Mašič, L. P., Mlinšek, G., Šolmajer, T., Trampuš-Bakija, A., Stegnar, M., & Kikelj, D. (2003) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/s0960-894x(03)00030-1 · Cited by 17 · see citing works
Reaction-MQL: Line Notation for Functional Transformation
Reisen, F., Schneider, G., & Proschak, E. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800215t · Cited by 16 · see citing works
Self-citationBergner, A., Bauer, M., Brandstetter, H., Stürzebecher, J., & Bode, W. (1995) · Journal of enzyme inhibition · doi:10.3109/14756369509040684 · Cited by 16 · see citing works
Dichloroacetate and cerebral ischaemia therapeutics
Marangos, P. J., Turkel, C. C., Dziewanowska, Z. E., & Fox, A. W. (1999) · Expert Opinion on Investigational Drugs · doi:10.1517/13543784.8.4.373 · Cited by 12 · see citing works
Werner, T., Sander, K., Tanrikulu, Y., Kottke, T., Proschak, E., Stark, H., & Schneider, G. (2010) · ChemBioChem · doi:10.1002/cbic.201000180 · Cited by 11 · see citing works
Self-citationNOCHI, S., Shimomura, N., HATTORI, T., Sato, T., Miyake, Y., & Tanizawa, K. (1989) · Chemical and Pharmaceutical Bulletin · doi:10.1248/cpb.37.2855 · Cited by 7 · see citing works
Miyamoto, K., Hishinuma, I., Nagakawa, J., Nagaoka, N., Yamanaka, T., & Wakabayashi, T. (1988) · Folia Pharmacologica Japonica · doi:10.1254/fpj.91.285 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [36]Celanire S Lebon F Stark H 2009 Drug discovery: from hits to vlinical vandidates. Vohora D The Third Histamine Receptor: Selective Ligands as Potential Therapeutic Agents in CNS Disorders Boca Raton CRC Press 103 165
Cited by
Sorted by citations
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00512 · Cited by 1,537 · see citing works
Molecular de-novo design through deep reinforcement learning
Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0235-x · Cited by 1,358 · see citing works
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,045 · see citing works
Self-citationRethinking drug design in the artificial intelligence era
Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0050-3 · Cited by 894 · see citing works
Self-citationMuratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
Polypharmacology by Design: A Medicinal Chemist’s Perspective on Multitargeting Compounds
Proschak, E., Stark, H., & Merk, D. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b00760 · Cited by 498 · see citing works
Self-citationArtificial intelligence in drug discovery: recent advances and future perspectives
Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1909567 · Cited by 464 · see citing works
Self-citationIntegrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
Tropsha, A., Isayev, O., Varnek, A. A., Schneider, G., & Cherkasov, A. R. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00832-0 · Cited by 423 · see citing works
Self-citationA review of molecular representation in the age of machine learning
Wigh, D. S., Goodman, J. M., & Lapkin, A. A. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1603 · Cited by 398 · see citing works
Autonomous Discovery in the Chemical Sciences Part I: Progress
Coley, C. W., Eyke, N. S., & Jensen, K. F. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201909987 · Cited by 365 · see citing works
Advances in De Novo Drug Design: From Conventional to Machine Learning Methods
Mouchlis, V. D., Afantitis, A., Serra, A., Fratello, M., Papadiamantis, A. G., Aidinis, V., Lynch, I., Greco, D., & Melagraki, G. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22041676 · Cited by 367 · see citing works
Synthetic organic chemistry driven by artificial intelligence
Almeida, A. F., Moreira, R., & Rodrigues, T. (2019) · Nature Reviews Chemistry · doi:10.1038/s41570-019-0124-0 · Cited by 306 · see citing works
Vázquez, J., López, M., Gibert, E., Herrero, E., & Luque, F. J. (2020) · Molecules · doi:10.3390/molecules25204723 · Cited by 253 · see citing works
The next level in chemical space navigation: going far beyond enumerable compound libraries
Hoffmann, T., & Gastreich, M. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.02.013 · Cited by 245 · see citing works
Shape-Based Generative Modeling for de Novo Drug Design
Skalic, M., Jiménez, J., Sabbadin, D., & De Fabritiis, G. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00706 · Cited by 229 · see citing works
Bidirectional Molecule Generation with Recurrent Neural Networks
Grisoni, F., Moret, M., Lingwood, R., & Schneider, G. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00943 · Cited by 232 · see citing works
Self-citationReker, D., Rodrigues, T., Schneider, P., & Schneider, G. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320001111 · Cited by 226 · see citing works
Self-citationNatural product drug discovery in the artificial intelligence era
Saldívar‐González, F. I., Aldas-Bulos, V. D., Medina‐Franco, J. L., & Plisson, F. (2021) · Chemical Science · doi:10.1039/d1sc04471k · Cited by 227 · see citing works
Exploring the GDB-13 chemical space using deep generative models
Arús‐Pous, J., Blaschke, T., Ulander, S., Reymond, J., Chen, H., & Engkvist, O. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0341-z · Cited by 191 · see citing works
Generative Deep Learning for Targeted Compound Design
de Sousa, T. F., Correia, J. V. M., Pereira, V., & Rocha, M. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01496 · Cited by 193 · see citing works
Automated in Silico Design of Homogeneous Catalysts
Foscato, M., & Jensen, V. R. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04952 · Cited by 186 · see citing works
Computational Multitarget Drug Design
Zhang, W., Pei, J., & Lai, L. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00491 · Cited by 186 · see citing works
Generative machine learning for de novo drug discovery: A systematic review
Martinelli, D. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105403 · Cited by 177 · see citing works
Machine learning-aided generative molecular design
Du, Y., Jamasb, A. R., Guo, J., Fu, T., Harris, C., Wang, Y., Duan, C., Lió, P., Schwaller, P., & Blundell, T. L. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00843-5 · Cited by 193 · see citing works
Artificial Intelligence in Drug Discovery
Sellwood, M. A., Ahmed, M., Segler, M., & Brown, N. (2018) · Future Medicinal Chemistry · doi:10.4155/fmc-2018-0212 · Cited by 174 · see citing works
Cox, O. B., Krojer, T. J., Collins, P. M., Monteiro, O., Talon, R., Bradley, A. R., Fedorov, O., Amin, J., Marsden, B. D., Spencer, J., et al. (2015) · Chemical Science · doi:10.1039/c5sc03115j · Cited by 172 · see citing works
De Novo Design at the Edge of Chaos
Schneider, P., & Schneider, G. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01849 · Cited by 167 · see citing works
Self-citationMao, J., Akhtar, J., Zhang, X., Sun, L., Guan, S., Li, X., Chen, G., Liu, J., Jeon, H.-N., Kim, M. S., et al. (2021) · iScience · doi:10.1016/j.isci.2021.103052 · Cited by 173 · see citing works
Automated De Novo Drug Design: Are We Nearly There Yet?
Schneider, G., & Clark, D. E. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201814681 · Cited by 168 · see citing works
Self-citationCheminformatics in Natural Product‐based Drug Discovery
Chen, Y., & Kirchmair, J. (2020) · Molecular Informatics · doi:10.1002/minf.202000171 · Cited by 160 · see citing works
Exploration of Ultralarge Compound Collections for Drug Discovery
Warr, W. A., Nicklaus, M. C., Nicolaou, C. A., & Rarey, M. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00224 · Cited by 154 · see citing works
SYBA: Bayesian estimation of synthetic accessibility of organic compounds
Voršilák, M., Kolář, M., Čmelo, I., & Svozil, D. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00439-2 · Cited by 152 · see citing works
Konze, K. D., Bos, P. H., Dahlgren, M. K., Leswing, K., Tubert‐Brohman, I., Bortolato, A., Robbason, B., Abel, R., & Bhat, S. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00367 · Cited by 144 · see citing works
Sattarov, B., Baskin, I. I., Horvath, D., Marcou, G., Bjerrum, E. J., & Varnek, A. A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00751 · Cited by 148 · see citing works
The Advent of Generative Chemistry
Vanhaelen, Q., Lin, Y.-C., & Zhavoronkov, A. (2020) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.0c00088 · Cited by 151 · see citing works
Basith, S., Cui, M., Macalino, S. J. Y., Park, J., Clavio, N. A. B., Kang, S., & Choi, S. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.00128 · Cited by 140 · see citing works
Concepts and applications of chemical fingerprint for hit and lead screening
Yang, J., Cai, Y., Zhao, K., Xie, H., & Chen, X. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.103356 · Cited by 140 · see citing works
A Collection of Robust Organic Synthesis Reactions for In Silico Molecule Design
Hartenfeller, M., Eberle, M., Meier, P. J., Nieto-Oberhuber, C. M., Altmann, K., Schneider, G., Jacoby, E., & Renner, S. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200379p · Cited by 126 · see citing works
Self-citationDefining and Exploring Chemical Spaces
Coley, C. W. (2020) · Trends in Chemistry · doi:10.1016/j.trechm.2020.11.004 · Cited by 126 · see citing works
BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry
Tetko, I. V., Engkvist, O., Koch, U., Reymond, J., & Chen, H. (2016) · Molecular Informatics · doi:10.1002/minf.201600073 · Cited by 124 · see citing works
CReM: chemically reasonable mutations framework for structure generation
Полищук, П. Г. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00431-w · Cited by 119 · see citing works
Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor
Li, Y., Zhang, L., Wang, Y., Zou, J., Yang, R., Luo, X., Wu, C., Yang, W. M., Tian, C., Xu, H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34692-w · Cited by 106 · see citing works
RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design
Wang, M., Hsieh, C.-Y., Wang, J., Wang, D., Weng, G., Shen, C., Yao, X., Bing, Z., Li, H., Cao, D., et al. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00732 · Cited by 99 · see citing works
The impact of in silico screening in the discovery of novel and safer drug candidates
Rognan, D. (2017) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2017.02.034 · Cited by 99 · see citing works
The Proximal Lilly Collection: Mapping, Exploring and Exploiting Feasible Chemical Space
Nicolaou, C. A., Watson, I. A., Hu, H., & Wang, J. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00173 · Cited by 94 · see citing works
Multi‐Objective Molecular De Novo Design by Adaptive Fragment Prioritization
Reutlinger, M., Rodrigues, T., Schneider, P., & Schneider, G. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201310864 · Cited by 90 · see citing works
Self-citationSynthetic data in biomedicine via generative artificial intelligence
van Breugel, B., Liu, T., Oglić, D., & van der Schaar, M. (2024) · Nature Reviews Bioengineering · doi:10.1038/s44222-024-00245-7 · Cited by 97 · see citing works
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement LearningPreprint
Gottipati, S. K., Sattarov, B., Niu, S., Pathak, Y., Wei, H., Liu, S., Thomas, K. M. J., Blackburn, S., Coley, C. W., Tang, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.12485 · Cited by 82 · see citing works
Kumar, S. A., Kumar, T. D. A., Beeraka, N. M., Pujar, G. V., Singh, M., Akshatha, H. S., & Bhagyalalitha, M. (2021) · Future Medicinal Chemistry · doi:10.4155/fmc-2021-0243 · Cited by 71 · see citing works
OpenGrowth: An Automated and Rational Algorithm for Finding New Protein Ligands
Chéron, N., Jasty, N. M., & Shakhnovich, E. I. (2015) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b00886 · Cited by 67 · see citing works
Navigating large chemical spaces in early-phase drug discovery
Korn, M., Ehrt, C., Ruggiu, F., Gastreich, M., & Rarey, M. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102578 · Cited by 65 · see citing works
Artificial intelligence in synthetic chemistry: achievements and prospects
Baskin, I. I., Madzhidov, T., Antipin, I. S., & Varnek, A. A. (2017) · Russian Chemical Reviews · doi:10.1070/rcr4746 · Cited by 62 · see citing works
Firth, N. C., Atrash, B., Brown, N., & Blagg, J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00073 · Cited by 61 · see citing works
Artificial intelligence in the pharmaceutical sector: current scene and future prospect
Kalyane, D., Sanap, G., Paul, D., Shenoy, S., Anup, N., Polaka, S., Tambe, V., & Tekade, R. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814455-8.00003-7 · Cited by 61 · see citing works
Balancing novelty with confined chemical space in modern drug discovery
Medina‐Franco, J. L., Martínez‐Mayorga, K., & Meurice, N. (2013) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2014.872624 · Cited by 57 · see citing works
De Novo Design of Nurr1 Agonists via Fragment-Augmented Generative Deep Learning in Low-Data Regime
Ballarotto, M., Willems, S., Stiller, T., Nawa, F., Marschner, J. A., Grisoni, F., & Merk, D. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c00485 · Cited by 54 · see citing works
LiGen: A High Performance Workflow for Chemistry Driven de Novo Design
Beccari, A. R., Cavazzoni, C., Beato, C., & Costantino, G. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400078g · Cited by 52 · see citing works
Ghanakota, P., Bos, P. H., Konze, K. D., Staker, J., Marques, G. F. F., Marshall, K., Leswing, K., Abel, R., & Bhat, S. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00120 · Cited by 50 · see citing works
Kumar, N., Tomar, R., Pandey, A. K., Tomar, V., Singh, V. K., & Chandra, R. (2017) · Artificial Cells Nanomedicine and Biotechnology · doi:10.1080/21691401.2017.1369423 · Cited by 51 · see citing works
Merk, D., Grisoni, F., Friedrich, L., Gelžinytė, E., & Schneider, G. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b00494 · Cited by 50 · see citing works
Self-citationChemistry‐driven Hit‐to‐lead Optimization Guided by Structure‐based Approaches
Hoffer, L., Müller, C., Roche, P., & Morelli, X. (2018) · Molecular Informatics · doi:10.1002/minf.201800059 · Cited by 50 · see citing works
Stanley, M., & Segler, M. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102658 · Cited by 48 · see citing works
Automated Design of Realistic Organometallic Molecules from Fragments
Foscato, M., Occhipinti, G., Venkatraman, V., Alsberg, B. K., & Jensen, V. R. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci4007497 · Cited by 49 · see citing works
Fragment‐Based De Novo Design Reveals a Small‐Molecule Inhibitor of Helicobacter Pylori HtrA
Perna, A. M., Rodrigues, T., Schmidt, T. P., Böhm, M., Stutz, K., Reker, D., Pfeiffer, B., Altmann, K., Backert, S., Weßler, S., et al. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201504035 · Cited by 49 · see citing works
Self-citationMachine Learning in Chemoinformatics and Medicinal Chemistry
Rodríguez-Pérez, R., Miljković, F. N., & Bajorath, J. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-122120-124216 · Cited by 48 · see citing works
From Complex Natural Products to Simple Synthetic Mimetics by Computational De Novo Design
Friedrich, L., Rodrigues, T., Neuhaus, C. S., Schneider, P., & Schneider, G. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201601941 · Cited by 48 · see citing works
Self-citationOlğaç, A., Orhan, İ. E., & Banoğlu, E. (2017) · Future Medicinal Chemistry · doi:10.4155/fmc-2017-0124 · Cited by 47 · see citing works
Grebner, C., Matter, H., Plowright, A. T., & Heßler, G. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b02044 · Cited by 47 · see citing works
Inverse‐QSPR for de novo Design: A Review
Gantzer, P., Creton, B., & Nieto‐Draghi, C. (2019) · Molecular Informatics · doi:10.1002/minf.201900087 · Cited by 49 · see citing works
DENOPTIM: Software for Computational <i>de Novo</i> Design of Organic and Inorganic Molecules
Foscato, M., Venkatraman, V., & Jensen, V. R. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00516 · Cited by 45 · see citing works
Approaching Target Selectivity by De Novo Drug Design
Fischer, T., Gazzola, S., & Riedl, R. (2019) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2019.1615435 · Cited by 46 · see citing works
De novo drug design through artificial intelligence: an introduction
Crucitti, D., Míguez, C. P., Arias, J. Á. D., Prada, D. B. F., & Orgueira, A. M. (2024) · Frontiers in Hematology · doi:10.3389/frhem.2024.1305741 · Cited by 47 · see citing works
Generative AI for navigating synthesizable chemical space
Gao, W., Luo, S., & Coley, C. W. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2415665122 · Cited by 43 · see citing works
Hoffer, L., Renaud, J., & Horvath, D. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci4000163 · Cited by 41 · see citing works
Multidimensional De Novo Design Reveals 5‐HT2B Receptor‐Selective Ligands
Rodrigues, T., Hauser, N., Reker, D., Reutlinger, M., Wunderlin, T., Hamon, J., Koch, G., & Schneider, G. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201410201 · Cited by 41 · see citing works
Self-citationDe novo design – hop(p)ing against hope
Schneider, G. (2012) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2012.06.001 · Cited by 39 · see citing works
Self-citationHarnessing the potential of natural products in drug discovery from a cheminformatics vantage point
Rodrigues, T. (2017) · Organic & Biomolecular Chemistry · doi:10.1039/c7ob02193c · Cited by 38 · see citing works
Chemical Space Expansion of Bromodomain Ligands Guided by in Silico Virtual Couplings (AutoCouple)
Batiste, L., Unzue, A., Dolbois, A., Hassler, F., Wang, X., Deerain, N., Zhu, J., Spiliotopoulos, D., Nevado, C., & Caflisch, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00401 · Cited by 38 · see citing works
SAR by Space: Enriching Hit Sets from the Chemical Space
Klingler, F., Gastreich, M., Grygorenko, O. O., Savych, O., Borysko, P., Griniukova, A., Gubina, K. E., Lemmen, C., & Moroz, Y. S. (2019) · Molecules · doi:10.3390/molecules24173096 · Cited by 38 · see citing works
Discovery of SARS-CoV-2 main protease inhibitors using a synthesis-directed de novo design model
Morris, A., McCorkindale, W., The COVID Moonshot Consortium, Drayman, N., Chodera, J. D., Tay, S., London, N., & Lee, A. A. (2021) · Chemical Communications · doi:10.1039/d1cc00050k · Cited by 37 · see citing works
Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases
Müller, C., Rabal, O., & Gonzalez, C. D. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_16 · Cited by 37 · see citing works
Directly optimizing for synthesizability in generative molecular design using retrosynthesis models
Guo, J., & Schwaller, P. (2025) · Chemical Science · doi:10.1039/d5sc01476j · Cited by 36 · see citing works
LEADD: Lamarckian evolutionary algorithm for de novo drug design
Kerstjens, A., & Winter, H. L. D. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00582-y · Cited by 36 · see citing works
Schneider, G. (2014) · Molecular Informatics · doi:10.1002/minf.201400034 · Cited by 36 · see citing works
Self-citationDe Novo Fragment Design for Drug Discovery and Chemical Biology
Rodrigues, T., Reker, D., Welin, M., Caldera, M., Brunner, C., Gabernet, G., Schneider, P., Walse, B., & Schneider, G. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201508055 · Cited by 33 · see citing works
Self-citationSynthI: A New Open-Source Tool for Synthon-Based Library Design
Zabolotna, Y., Volochnyuk, D. M., Ryabukhin, S. V., Gavrylenko, K., Horvath, D., Klimchuk, O., Oksiuta, O., Marcou, G., & Varnek, A. A. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00754 · Cited by 33 · see citing works
Barking up the right tree: an approach to search over molecule synthesis DAGsPreprint
Bradshaw, J., Paige, B., Kusner, M. J., Segler, M., & Hernández-Lobato, J. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11522 · Cited by 32 · see citing works
Friedrich, L., Cingolani, G., Ko, Y., Iaselli, M., Miciaccia, M., Perrone, M. G., Neukirch, K., Bobinger, V., Merk, D., Hofstetter, R. K., et al. (2021) · Advanced Science · doi:10.1002/advs.202100832 · Cited by 31 · see citing works
Self-citationMoshawih, S., Goh, H. P., Kifli, N., Idris, A. C., Yassin, H., Kotra, V., Goh, K. W., Liew, K. B., & Ming, L. C. (2022) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14062 · Cited by 31 · see citing works
Meta Learning for Low-Resource Molecular Optimization
Wang, J., Zheng, S., Chen, J., & Yang, Y. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01416 · Cited by 31 · see citing works
Automated De Novo Drug Design: Are We Nearly There Yet?
Schneider, G., & Clark, D. E. (2019) · Angewandte Chemie · doi:10.1002/ange.201814681 · Cited by 37 · see citing works
Self-citationDrugs by Numbers: Reaction‐Driven De Novo Design of Potent and Selective Anticancer Leads
Spänkuch, B., Keppner, S., Lange, L. D., Rodrigues, T., Zettl, H., Koch, C. P., Reutlinger, M., Hartenfeller, M., Schneider, P., & Schneider, G. (2012) · Angewandte Chemie International Edition · doi:10.1002/anie.201206897 · Cited by 28 · see citing works
Self-citationMedicinal Chemistry Aware Database GDBMedChem
Awale, M., Sirockin, F., Stiefl, N., & Reymond, J. (2019) · Molecular Informatics · doi:10.1002/minf.201900031 · Cited by 28 · see citing works
Yoshimori, A., Kawasaki, E., Kanai, C., & Tasaka, T. (2020) · Chemical and Pharmaceutical Bulletin · doi:10.1248/cpb.c19-00625 · Cited by 28 · see citing works
Multi-targeted drug design strategies for the treatment of schizophrenia
Stępnicki, P., Kondej, M., Koszła, O., Żuk, J., & Kaczor, A. A. (2020) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2020.1816962 · Cited by 28 · see citing works
Exploring chemical space — Generative models and their evaluation
Vogt, M. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2023.100064 · Cited by 27 · see citing works
Deep Learning Applied to Ligand-Based De Novo Drug Design
Palazzesi, F., & Pozzan, A. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_12 · Cited by 30 · see citing works
Using deep neural networks to explore chemical space
Vogt, M. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.2019704 · Cited by 24 · see citing works
Meyenburg, C., Dolfus, U., Briem, H., & Rarey, M. (2022) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-022-00485-y · Cited by 25 · see citing works
AutoT&T v.2: An Efficient and Versatile Tool for Lead Structure Generation and Optimization
Li, Y., Zhao, Z., Liu, Z., Su, M., & Wang, R. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00691 · Cited by 25 · see citing works
- Authors
- Hartenfeller, M., Zettl, H., Walter, M., Rupp, M., Reisen, F., Proschak, E., Weggen, S., Stark, H., & Schneider, G.
- Affiliations
- ETH Zurich (CH)
- Goethe University Frankfurt (DE)
- Heinrich Heine University Düsseldorf (DE)
- Corresponding author
- Schneider, Gisbert
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-02-16
- Volume
- 8
- Issue
- 2
- Pages
- e1002380
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3280956
- OpenAlex
- W2052933929
- Cited by
- 251
- References
- 56
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 27 · 2023: 16 · 2022: 25 · 2021: 31 · 2020: 25 · 2019: 23 · 2018: 17 · 2017: 9 · 2016: 15 · 2015: 11 · 2014: 12 · 2013: 17 · 2012: 6
- FWCI
- 15.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (publishedVersion, cc-by)
- KOPS (University of Konstanz) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (BM0806)
- Ministry of Education, India
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Keywords
- de novo drug design, ligand-based drug design, synthetic building blocks, scaffold hopping, histamine H4 receptor, gamma-secretase, bioactive compounds, drug-likeness
- MeSH terms
- Receptors, Histamine H4, Algorithms, Automation, Computers, Humans, Ligands, Models, Chemical, Receptors, Histamine, Software, Technology, Pharmaceutical, Drug Design, Models, Statistical, Molecular Structure, Computational Biology, Receptors, G-Protein-Coupled, Amyloid Precursor Protein Secretases
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
