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Discovering new drug molecules is a pivotal yet challenging process due to the near-infinitely large chemical space and notorious demands on time and resources.
The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service
Morgan, H. L · 1965
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Evolutionary design of molecules with desired properties using the genetic algorithm
Venkatasubramanian, V., Chan, K., and Caruthers, J. M · 1995
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The art and practice of structure-based drug design: a molecular modeling perspective
Bohacek, R. S., McMartin, C., and Guida, W. C · 1996
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Genetic optimization of combinatorial libraries
Gobbi, A. and Poppinger, D · 1998
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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
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Virtual screening of chemical libraries
Shoichet, B. K · 2004
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Hit discovery and hit-to-lead approaches
Keserű, G. M. and Makara, G. M · 2006
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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
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De novo drug design
Hartenfeller, M. and Schneider, G · 2011
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Chembl: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al · 2012
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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
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., and Aspuru-Guzik, A · 2018
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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
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Multi-objective de novo drug design with conditional graph generative model
Li, Y., Zhang, L., and Liu, Z · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
You, J., Liu, B., Ying, Z., Pande, V., and Leskovec, J · 2018
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A model to search for synthesizable molecules
Bradshaw, J., Paige, B., Kusner, M. J., Segler, M., and Hernández-Lobato, J. M · 2019
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Guacamol: benchmarking models for de novo molecular design
Brown, N., Fiscato, M., Segler, M. H., and Vaucher, A. C · 2019
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Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis
Button, A., Merk, D., Hiss, J. A., and Schneider, G · 2019
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A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jensen, J. H · 2019
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Differentiable scaffolding tree for molecule optimization
Fu, T., Gao, W., Xiao, C., Yasonik, J., Coley, C. W., and Sun, J · 2021
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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
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A 3d generative model for structure-based drug design
Luo, S., Guan, J., Ma, J., and Peng, J · 2021
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Retrosynthetic accessibility score (rascore)–rapid machine learned synthesizability classification from ai driven retrosynthetic planning
Thakkar, A., Chadimová, V., Bjerrum, E. J., Engkvist, O., and Reymond, J.-L · 2021
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Do transformers really perform badly for graph representation?
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T.-Y · 2021
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Ultra-large library docking for discovering new chemotypes
Lyu, J., Wang, S., Balius, T. E., Singh, I., Levit, A., Moroz, Y. S., O’Meara, M. J., Che, T., Algaa, E., Tolmachova, K., et al · 2019
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Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction
Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., and Lee, A. A · 2019
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Barking up the right tree: an approach to search over molecule synthesis dags
Bradshaw, J., Paige, B., Kusner, M. J., Segler, M., and Hernández-Lobato, J. M · 2020
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The synthesizability of molecules proposed by generative models
Gao, W. and Coley, C. W · 2020
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Learning to navigate the synthetically accessible chemical space using reinforcement learning
Gottipati, S. K., Sattarov, B., Niu, S., Pathak, Y., Wei, H., Liu, S., Blackburn, S., Thomas, K., Coley, C., Tang, J., et al · 2020
Cited alongside, same era.
Molecular design in synthetically accessible chemical space via deep reinforcement learning
Horwood, J. and Noutahi, E · 2020
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Chembo: Bayesian optimization of small organic molecules with synthesizable recommendations
Korovina, K., Xu, S., Kandasamy, K., Neiswanger, W., Poczos, B., Schneider, J., and Xing, E · 2020
Cited alongside, same era.
Fu, T., Gao, W., Coley, C., and Sun, J · 2022
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Sample efficiency matters: a benchmark for practical molecular optimization
Gao, W., Fu, T., Sun, J., and Coley, C · 2022
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Simple nearest-neighbour analysis meets the accuracy of compound potency predictions using complex machine learning models
Janela, T. and Bajorath, J · 2022
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Pocket2mol: Efficient molecular sampling based on 3d protein pockets
Peng, X., Luo, S., Guan, J., Xie, Q., Peng, J., and Ma, J · 2022
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Building blocks catalog
Enamine · 2023
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Benchmarking generated poses: How rational is structure-based drug design with generative models?
Harris, C., Didi, K., Jamasb, A. R., Joshi, C. K., Mathis, S. V., Lio, P., and Blundell, T · 2023
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Pubchem 2023 update
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al · 2023
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Computer-aided evaluation and exploration of chemical spaces constrained by reaction pathways
Levin, I., Fortunato, M. E., Tan, K. L., and Coley, C. W · 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
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SMARTS - A Language for Describing Molecular Patterns
Daylight Chemical Information Systems, Inc · 2024
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