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Searching through chemical space is an exceptionally challenging problem because the number of possible molecules grows combinatorially with the number of atoms.
Rank analysis of incomplete block designs: I. the method of paired comparisons
R. A. Bradley and M. E. Terry · 1952
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A Computer Program for Classifying Plants: The computer is programmed to simulate the taxonomic process of comparing each case with every other case
D. J. Rogers and T. T. Tanimoto · 1960
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Dendral: A case study of the first expert system for scientific hypothesis formation
R. K. Lindsay, B. G. Buchanan, E. A. Feigenbaum, and J. Lederberg · 1993
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Prediction of Physicochemical Parameters by Atomic Contributions
S. A. Wildman and G. M. Crippen · 1999
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Glide: A new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy
R. A. Friesner, J. L. Banks, R. B. Murphy, T. A. Halgren, J. J. Klicic, D. T. Mainz, M. P. Repasky, E. H. Knoll, M. Shelley, J. K. Perry, D. E. Shaw, P. Francis, and P. S. Shenkin · 2004
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The influence of lead discovery strategies on the properties of drug candidates
G. M. Keserü and G. M. Makara · 2009
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Gradient flows of the entropy for finite Markov chains
J. Maas · 2011
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Quantifying the chemical beauty of drugs
G. R. Bickerton, G. V. Paolini, J. Besnard, S. Muresan, and A. L. Hopkins · 2012
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Directed evolution of the tryptophan synthase β \beta -subunit for stand-alone function recapitulates allosteric activation
A. R. Buller, S. Brinkmann-Chen, D. K. Romney, M. Herger, J. Murciano-Calles, and F. H. Arnold · 2015
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Mutation effects predicted from sequence co-variation
T. A. Hopf, J. B. Ingraham, F. J. Poelwijk, C. P. I. Schärfe, M. Springer, C. Sander, and D. S. Marks · 2017
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{Euclidean, Metric, and Wasserstein} gradient flows: An overview
F. Santambrogio · 2017
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Proximal Policy Optimization Algorithms, Aug. 2017
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Excape-db: an integrated large scale dataset facilitating big data analysis in chemogenomics
J. Sun, N. Jeliazkov, V. Chupakhin, J.-F. Golib-Dzib, O. Engkvist, L. Carlsson, J. Wegner, H. Ceulemans, I. Georgiev, V. Jeliazkov, N. Kochev, T. J. Ashby, and H. Chen · 2017
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Attention is All you Need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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SCScore: Synthetic Complexity Learned from a Reaction Corpus
C. W. Coley, L. Rogers, W. H. Green, and K. F. Jensen · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, and A. Aspuru-Guzik · 2018
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Inverse molecular design using machine learning: Generative models for matter engineering
B. Sanchez-Lengeling and A. Aspuru-Guzik · 2018
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“Found in Translation”: Predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models
P. Schwaller, T. Gaudin, D. Lányi, C. Bekas, and T. Laino · 2018
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How to explore chemical space using algorithms and automation
P. S. Gromski, A. B. Henson, J. M. Granda, and L. Cronin · 2019
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Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction
P. Schwaller, T. Laino, T. Gaudin, P. Bolgar, C. A. Hunter, C. Bekas, and A. A. Lee · 2019
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SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction
S. Wang, Y. Guo, Y. Wang, H. Sun, and J. Huang · 2019
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ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
S. Chithrananda, G. Grand, and B. Ramsundar · 2020
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Nash Learning from Human Feedback, Dec. 2023
R. Munos, M. Valko, D. Calandriello, M. G. Azar, M. Rowland, Z. D. Guo, Y. Tang, M. Geist, T. Mesnard, A. Michi, M. Selvi, S. Girgin, N. Momchev, O. Bachem, D. J. Mankowitz, D. Precup, and B. Piot · 2023
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Preference Optimization for Molecular Language Models, Oct. 2023
R. Park, R. Theisen, N. Sahni, M. Patek, A. Cichońska, and R. Rahman · 2023
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Direct preference optimization: Your language model is secretly a reward model
R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn · 2023
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Decoil: Optimization of degenerate codon libraries for machine learning-assisted protein engineering
J. Yang, J. Ducharme, K. E. Johnston, F.-Z. Li, Y. Yue, and F. H. Arnold · 2023
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Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization, Dec. 2023
Z. Zhou, J. Liu, C. Yang, J. Shao, Y. Liu, X. Yue, W. Ouyang, and Y. Qiao · 2023
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Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
K. Huang, T. Fu, W. Gao, Y. Zhao, Y. Roohani, J. Leskovec, C. W. Coley, C. Xiao, J. Sun, and M. Zitnik · 2021
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Directed evolution: Methodologies and applications
Y. Wang, P. Xue, M. Cao, T. Yu, S. T. Lane, and H. Zhao · 2021
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MolGPT: Molecular Generation Using a Transformer-Decoder Model
V. Bagal, R. Aggarwal, P. K. Vinod, and U. D. Priyakumar · 2022
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Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback, Apr. 2022
Y. Bai, A. Jones, K. Ndousse, A. Askell, A. Chen, N. DasSarma, D. Drain, S. Fort, D. Ganguli, T. Henighan, N. Joseph, S. Kadavath, J. Kernion, T. Conerly, S. El-Showk, N. Elhage, Z. Hatfield-Dodds, D. Hernandez, T. Hume, S. Johnston, S. Kravec, L. Lovitt, N. Nanda, C. Olsson, D. Amodei, T. Brown, J. Clark, S. McCandlish, C. Olah, B. Mann, and J. Kaplan · 2022
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Sample efficiency matters: A benchmark for practical molecular optimization
W. Gao, T. Fu, and J. S. andConnor W. Coley · 2022
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Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. F. Christiano, J. Leike, and R. Lowe · 2022
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Direct Preference-based Policy Optimization without Reward Modeling
G. An, J. Lee, X. Zuo, N. Kosaka, K.-M. Kim, and H. O. Song · 2023
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Llama 3 model card
AI@Meta · 2024
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Acegen: Reinforcement learning of generative chemical agents for drug discovery
A. Bou, M. Thomas, S. Dittert, C. Navarro, M. Majewski, Y. Wang, S. Patel, G. Tresadern, M. Ahmad, V. Moens, W. Sherman, S. Sciabola, and G. D. Fabritiis · 2024
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Preference learning algorithms do not learn preference rankings
A. Chen, S. Malladi, L. H. Zhang, X. Chen, Q. Zhang, R. Ranganath, and K. Cho · 2024
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Simulating 500 million years of evolution with a language model, July 2024
T. Hayes, R. Rao, H. Akin, N. J. Sofroniew, D. Oktay, Z. Lin, R. Verkuil, V. Q. Tran, J. Deaton, M. Wiggert, R. Badkundri, I. Shafkat, J. Gong, A. Derry, R. S. Molina, N. Thomas, Y. Khan, C. Mishra, C. Kim, L. J. Bartie, M. Nemeth, P. D. Hsu, T. Sercu, S. Candido, and A. Rives · 2024
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Unsupervised evolution of protein and antibody complexes with a structure-informed language model
V. R. Shanker, T. U. J. Bruun, B. L. Hie, and P. S. Kim · 2024
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Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data, Apr. 2024
F. Tajwar, A. Singh, A. Sharma, R. Rafailov, J. Schneider, T. Xie, S. Ermon, C. Finn, and A. Kumar · 2024
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Aligning protein generative models with experimental fitness via direct preference optimization, May 2024
T. Widatalla, R. Rafailov, and B. Hie · 2024
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Active learning-assisted directed evolution, July 2024
J. Yang, R. G. Lal, J. C. Bowden, R. Astudillo, M. A. Hameedi, S. Kaur, M. Hill, Y. Yue, and F. H. Arnold · 2024
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The ChEMBL Database in 2023: A drug discovery platform spanning multiple bioactivity data types and time periods
B. Zdrazil, E. Felix, F. Hunter, E. J. Manners, J. Blackshaw, S. Corbett, M. de Veij, H. Ioannidis, D. M. Lopez, J. F. Mosquera, M. P. Magarinos, N. Bosc, R. Arcila, T. Kizilören, A. Gaulton, A. P. Bento, M. F. Adasme, P. Monecke, G. A. Landrum, and A. R. Leach · 2024
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Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning, Apr. 2024
R. Zhang, L. Lin, Y. Bai, and S. Mei · 2024
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Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates
G. Pesciullesi, P. Schwaller, T. Laino, and J.-L. Reymond · 2041
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