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Drug discovery is a complex, resource-intensive process requiring significant time and cost to bring new medicines to patients.
Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise
Koes, D. R., Baumgartner, M. P., and Camacho, C. J. (2013) · 1904
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The generation of a unique chemical fingerprint
Morgan, H. L. (1965) · 1965
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The markush challenge
Brown, L. J. (1991) · 1991
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Encoded combinatorial chemistry
Brenner, S. and Lerner, R. A. (1992) · 1992
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The properties of known drugs. 1. molecular frameworks
Bemis, G. W. and Murcko, M. A. (1996) · 1996
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Synopsis: Synthesize and optimize system in silico
Vinkers, H. M., de Jonge, M. R., Daeyaert, F. F., Heeres, J., Koymans, L. M., van Lenthe, J. H., Lewi, P. J., Timmerman, H., Van Aken, K., and Janssen, P. A. (2003) · 2003
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The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures
Wang, R., Fang, X., Lu, Y., Yang, C. Y., and Wang, S. (2004) · 2004
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Computer-based de novo design of drug-like molecules
Schneider, G. and Fechner, U. (2005) · 2005
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Hit discovery and hit-to-lead approaches
Keseru, G. M. and Makara, G. M. (2006) · 2006
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Rdkit: Open-source cheminformatics software
Landrum, G. (2006) · 2006
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Reporting data from high-throughput screening of small-molecule libraries
Inglese, J., Shamu, C., and Guy, R. K. (2007) · 2007
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Ertl, P. and Schuffenhauer, A. (2009) · 2009
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Open babel: An open chemical toolbox
O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., and Hutchison, G. R. (2011) · 2011
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Dogs: Reaction-driven de novo design of bioactive compounds
Hartenfeller, M., Zettl, H., Walter, M., Rupp, M., Reisen, F., Proschak, E., Weggen, S., Stark, H., and Schneider, G. (2012) · 2012
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Molecular sets (MOSES): A benchmarking platform for molecular generation models
Polykovskiy, D., Zhebrak, A., Sánchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., Kadurin, A., Nikolenko, S. I., Aspuru-Guzik, A., and Zhavoronkov, A. (2018) · 2018
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Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018
Wouters, O. J., McKee, M., and Luyten, J. (2020) · 2018
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Moleculenet: A benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V. (2018) · 2018
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GuacaMol: Benchmarking models for de novo molecular design
Brown, N., Fiscato, M., Segler, M. H., and Vaucher, A. C. (2019) · 2019
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A graph-convolutional neural network model for the prediction of chemical reactivity
Coley, C. W., Jin, W., Rogers, L., Jamison, T. F., Jaakkola, T. S., Green, W. H., Barzilay, R., and Jensen, K. F. (2019) · 2019
Cited alongside, same era.
ChemMedChem
Dash, R. C., Ozen, Z., McCarthy, K. R., Chatterjee, N., Harris, C. A., Rizzo, A. A., Walker, G. C., Korzhnev, D. M., and Hadden, M. K. (2019) · 2019
Cited alongside, same era.
Pocket2Mol: Efficient molecular sampling based on 3D protein pockets
Peng, X., Luo, S., Guan, J., Xie, Q., Peng, J., and Ma, J. (2022) · 2022
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Equibind: Geometric deep learning for drug binding structure prediction
Stärk, H., Ganea, O.-E., Pattanaik, L., Barzilay, R., and Jaakkola, T. (2022) · 2022
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Equivariant shape-conditioned generation of 3d molecules for ligand-based drug design
Adams, K. and Coley, C. W. (2023) · 2023
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Diffdock: Diffusion steps, twists, and turns for molecular docking
Corso, G., Stärk, H., Jing, B., Barzilay, R., and Jaakkola, T. (2023) · 2023
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3d equivariant diffusion for target-aware molecule generation and affinity prediction
Guan, J., Qian, W. W., Peng, X., Su, Y., Peng, J., and Ma, J. (2023) · 2023
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Discovery and lead-optimization of 4,5-dihydropyrazoles as mono-kinase selective, orally bioavailable and efficacious inhibitors of receptor interacting protein 1 (rip1) kinase
Harris, P. A. and Faucher, e. (2019) · 2019
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Shape-based generative modeling for de novo drug design
Skalic, M., Jiménez, J., Sabbadin, D., and De Fabritiis, G. (2019) · 2019
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Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design
Gao, W., Mercado, R., and Coley, C. W. (2021) · 2021
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Highly accurate protein structure prediction with AlphaFold
Jumper, J. e. a. (2021) · 2021
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De novo molecular design and generative models
Meyers, J., Fabian, B., and Brown, N. (2021) · 2021
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E(n) equivariant graph neural networks
Satorras, V. G., Hoogeboom, E., and Welling, M. (2021) · 2021
Cited alongside, same era.
Molgan: An implicit generative model for small molecular graphs
Cao, N. D. and Kipf, T. (2022) · 2022
Cited alongside, same era.
Levin, I., Fortunato, M. E., Tan, K. L., and Coley, C. W. (2023) · 2023
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Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores
Meyenburg, C., Dolfus, U., Briem, H., et al. (2023) · 2023
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Generative ai for designing and validating easily synthesizable and structurally novel antibiotics
Swanson, K., Liu, G., Catacutan, D., Zou, J., and Stokes, J. (2023) · 2023
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Activity cliff prediction: Dataset and benchmark
Zhang, Z., Zhao, B., Xie, A., Bian, Y., and Zhou, S. (2023) · 2023
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Synflownet: Towards molecule design with guaranteed synthesis pathways
Cretu, M., Harris, C., Roy, J., Bengio, E., and Liò, P. (2024) · 2024
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Drugpose: benchmarking 3d generative methods for early stage drug discovery
Jocys, Z., Grundy, J., and Farrahi, K. (2024) · 2024
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Projecting molecules into synthesizable chemical spaces
Luo, S., Gao, W., Wu, Z., Peng, J., Coley, C. W., and Ma, J. (2024) · 2024
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Boltz-1: Democratizing biomolecular interaction modeling
Wohlwend, J., Corso, G., Passaro, S., Reveiz, M., Leidal, K., Swiderski, W., Portnoi, T., Chinn, I., Silterra, J., Jaakkola, T., and Barzilay, R. (2024) · 2024
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Building blocks & screening compounds
Ltd., E. (2025) · 2025
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