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| @article{albergo2025stochasticinterpolants, | |
| author = {Michael Albergo and Nicholas M. Boffi and Eric Vanden-Eijnden}, | |
| title = {{Stochastic Interpolants: A Unifying Framework for Flows and Diffusions}}, | |
| journal = {Journal of Machine Learning Research}, | |
| year = {2025}, | |
| volume = {26}, | |
| number = {209}, | |
| pages = {1--80}, | |
| url = {http://jmlr.org/papers/v26/23-1605.html} | |
| } | |
| @inproceedings{austin2021d3pm, | |
| title = {{Structured Denoising Diffusion Models in Discrete State-Spaces}}, | |
| author = {Austin, Jacob and Johnson, Daniel D. and Ho, Jonathan and Tarlow, Daniel and van den Berg, Rianne}, | |
| year = {2021}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| volume = {34}, | |
| url = {https://proceedings.neurips.cc/paper_files/paper/2021/hash/958c530554f78bcd8e97125b70e6973d-Abstract.html}, | |
| pages = {17981--17993} | |
| } | |
| @inproceedings{backward, | |
| title = {{Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization}}, | |
| author = {Gritsaev, Timofei and Morozov, Nikita and Samsonov, Sergey and Tiapkin, Daniil}, | |
| year = {2025}, | |
| booktitle = {International Conference on Learning Representations}, | |
| url = {https://proceedings.iclr.cc/paper_files/paper/2025/hash/f3efbcfe76bed022a37c5aeb1daf2326-Abstract-Conference.html}, | |
| pages = {98281--98301} | |
| } | |
| @article{boffi2025flowmapmatching, | |
| title = {{Flow map matching with stochastic interpolants: A mathematical framework for consistency models}}, | |
| author = {Nicholas Matthew Boffi and Michael Samuel Albergo and Eric Vanden-Eijnden}, | |
| journal = {Transactions on Machine Learning Research}, | |
| year = {2025}, | |
| url = {https://openreview.net/forum?id=cqDH0e6ak2} | |
| } | |
| @inproceedings{boffi2025selfdistillation, | |
| title = {{How to build a consistency model: Learning flow maps via self-distillation}}, | |
| author = {Boffi, Nicholas and Albergo, Michael and Vanden-Eijnden, Eric}, | |
| year = {2025}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| volume = {38}, | |
| doi = {10.52202/085713-1121}, | |
| url = {https://proceedings.neurips.cc/paper_files/paper/2025/hash/2ff3605c9e442d55156d99fe4f5f228d-Abstract-Conference.html}, | |
| pages = {33346--33382} | |
| } | |
| @article{caco2, | |
| title = {{ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting}}, | |
| author = {Wang, Ning-Ning and Dong, Jie and Deng, Yin-Hua and Zhu, Min-Feng and Wen, Ming and Yao, Zhi-Jiang and Lu, Ai-Ping and Wang, Jian-Bing and Cao, Dong-Sheng}, | |
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| } | |
| @inproceedings{campbell2022continuoustimediscrete, | |
| title = {{A Continuous Time Framework for Discrete Denoising Models}}, | |
| author = {Campbell, Andrew and Benton, Joe and De Bortoli, Valentin and Rainforth, Thomas and Deligiannidis, George and Doucet, Arnaud}, | |
| year = {2022}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| volume = {35}, | |
| doi = {10.52202/068431-2049}, | |
| url = {https://proceedings.neurips.cc/paper_files/paper/2022/hash/b5b528767aa35f5b1a60fe0aaeca0563-Abstract-Conference.html}, | |
| pages = {28266--28279} | |
| } | |
| @inproceedings{campbell2024generativeflows, | |
| title = {{Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design}}, | |
| author = {Campbell, Andrew and Yim, Jason and Barzilay, Regina and Rainforth, Tom and Jaakkola, Tommi}, | |
| booktitle = {Proceedings of the 41st International Conference on Machine Learning}, | |
| pages = {5453--5512}, | |
| year = {2024}, | |
| volume = {235}, | |
| series = {Proceedings of Machine Learning Research}, | |
| publisher = {PMLR}, | |
| url = {https://proceedings.mlr.press/v235/campbell24a.html} | |
| } | |
| @inproceedings{cbas, | |
| title = {{Conditioning by adaptive sampling for robust design}}, | |
| author = {Brookes, David and Park, Hahnbeom and Listgarten, Jennifer}, | |
| booktitle = {Proceedings of the 36th International Conference on Machine Learning}, | |
| pages = {773--782}, | |
| year = {2019}, | |
| volume = {97}, | |
| series = {Proceedings of Machine Learning Research}, | |
| publisher = {PMLR}, | |
| url = {https://proceedings.mlr.press/v97/brookes19a.html} | |
| } | |
| @inproceedings{cgflow, | |
| title = {{Compositional Flows for 3{D} Molecule and Synthesis Pathway Co-design}}, | |
| author = {Shen, Tony and Seo, Seonghwan and Irwin, Ross and Didi, Kieran and Olsson, Simon and Kim, Woo Youn and Ester, Martin}, | |
| booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, | |
| pages = {54381--54409}, | |
| year = {2025}, | |
| volume = {267}, | |
| series = {Proceedings of Machine Learning Research}, | |
| publisher = {PMLR}, | |
| url = {https://proceedings.mlr.press/v267/shen25b.html} | |
| } | |
| @inproceedings{chembo, | |
| title = {{ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations}}, | |
| author = {Korovina, Ksenia and Xu, Sailun and Kandasamy, Kirthevasan and Neiswanger, Willie and Poczos, Barnabas and Schneider, Jeff and Xing, Eric}, | |
| booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, | |
| pages = {3393--3403}, | |
| year = {2020}, | |
| volume = {108}, | |
| series = {Proceedings of Machine Learning Research}, | |
| publisher = {PMLR}, | |
| url = {https://proceedings.mlr.press/v108/korovina20a.html} | |
| } | |
| @inproceedings{chen2018neuralode, | |
| author = {Chen, Ricky T. Q. and Rubanova, Yulia and Bettencourt, Jesse and Duvenaud, David K}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| publisher = {Curran Associates, Inc.}, | |
| title = {{Neural Ordinary Differential Equations}}, | |
| url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf}, | |
| volume = {31}, | |
| year = {2018} | |
| } | |
| @inproceedings{coms, | |
| title = {{Conservative Objective Models for Effective Offline Model-Based Optimization}}, | |
| author = {Trabucco, Brandon and Kumar, Aviral and Geng, Xinyang and Levine, Sergey}, | |
| booktitle = {Proceedings of the 38th International Conference on Machine Learning}, | |
| pages = {10358--10368}, | |
| year = {2021}, | |
| volume = {139}, | |
| series = {Proceedings of Machine Learning Research}, | |
| publisher = {PMLR}, | |
| url = {https://proceedings.mlr.press/v139/trabucco21a.html} | |
| } | |
| @inproceedings{designbench, | |
| title = {{Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization}}, | |
| author = {Trabucco, Brandon and Geng, Xinyang and Kumar, Aviral and Levine, Sergey}, | |
| booktitle = {Proceedings of the 39th International Conference on Machine Learning}, | |
| pages = {21658--21676}, | |
| year = {2022}, | |
| volume = {162}, | |
| series = {Proceedings of Machine Learning Research}, | |
| publisher = {PMLR}, | |
| url = {https://proceedings.mlr.press/v162/trabucco22a.html} | |
| } | |
| @inproceedings{dsb, | |
| title = {{Diffusion Schr\"odinger Bridge with Applications to Score-Based Generative Modeling}}, | |
| author = {De Bortoli, Valentin and Thornton, James and Heng, Jeremy and Doucet, Arnaud}, | |
| year = {2021}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| volume = {34}, | |
| url = {https://proceedings.neurips.cc/paper_files/paper/2021/hash/940392f5f32a7ade1cc201767cf83e31-Abstract.html}, | |
| pages = {17695--17709} | |
| } | |
| @article{extratrees, | |
| title = {{Extremely randomized trees}}, | |
| author = {Geurts, Pierre and Ernst, Damien and Wehenkel, Louis}, | |
| journal = {Machine Learning}, | |
| year = {2006}, | |
| volume = {63}, | |
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| doi = {10.1007/s10994-006-6226-1}, | |
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| } | |
| @inproceedings{frans2025shortcutmodels, | |
| title = {{One Step Diffusion via Shortcut Models}}, | |
| author = {Frans, Kevin and Hafner, Danijar and Levine, Sergey and Abbeel, Pieter}, | |
| year = {2025}, | |
| booktitle = {International Conference on Learning Representations}, | |
| url = {https://proceedings.iclr.cc/paper_files/paper/2025/hash/559a0998fab1d19b80e7e43a5852401c-Abstract-Conference.html}, | |
| pages = {34668--34684} | |
| } | |
| @inproceedings{gat2024discreteflowmatching, | |
| title = {{Discrete Flow Matching}}, | |
| author = {Gat, Itai and Remez, Tal and Shaul, Neta and Kreuk, Felix and Chen, Ricky T. Q. and Synnaeve, Gabriel and Adi, Yossi and Lipman, Yaron}, | |
| year = {2024}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| volume = {37}, | |
| doi = {10.52202/079017-4239}, | |
| url = {https://proceedings.neurips.cc/paper_files/paper/2024/hash/f0d629a734b56a642701bba7bc8bb3ed-Abstract-Conference.html}, | |
| pages = {133345--133385} | |
| } | |
| @inproceedings{gcpn, | |
| author = {You, Jiaxuan and Liu, Bowen and Ying, Zhitao and Pande, Vijay and Leskovec, Jure}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| publisher = {Curran Associates, Inc.}, | |
| title = {{Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation}}, | |
| url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/d60678e8f2ba9c540798ebbde31177e8-Paper.pdf}, | |
| volume = {31}, | |
| year = {2018} | |
| } | |
| @inproceedings{geng2025meanflows, | |
| title = {{Mean Flows for One-step Generative Modeling}}, | |
| author = {Geng, Zhengyang and Deng, Mingyang and Bai, Xingjian and Kolter, Zico and He, Kaiming}, | |
| year = {2025}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| volume = {38}, | |
| doi = {10.52202/085713-2534}, | |
| url = {https://proceedings.neurips.cc/paper_files/paper/2025/hash/6d13e085b79d454da5910e4ca82a3d9d-Abstract-Conference.html}, | |
| pages = {75460--75482} | |
| } | |
| @inproceedings{gfn, | |
| title = {{Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation}}, | |
| author = {Bengio, Emmanuel and Jain, Moksh and Korablyov, Maksym and Precup, Doina and Bengio, Yoshua}, | |
| year = {2021}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| volume = {34}, | |
| url = {https://proceedings.neurips.cc/paper_files/paper/2021/hash/e614f646836aaed9f89ce58e837e2310-Abstract.html}, | |
| pages = {27381--27394} | |
| } | |
| @article{gfnfoundations, | |
| author = {Yoshua Bengio and Salem Lahlou and Tristan Deleu and Edward J. Hu and Mo Tiwari and Emmanuel Bengio}, | |
| title = {{GFlowNet Foundations}}, | |
| journal = {Journal of Machine Learning Research}, | |
| year = {2023}, | |
| volume = {24}, | |
| number = {210}, | |
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| url = {http://jmlr.org/papers/v24/22-0364.html} | |
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| @inproceedings{grammarvae, | |
| title = {{Grammar Variational Autoencoder}}, | |
| author = {Matt J. Kusner and Brooks Paige and Jos{\'e} Miguel Hern{\'a}ndez-Lobato}, | |
| booktitle = {Proceedings of the 34th International Conference on Machine Learning}, | |
| pages = {1945--1954}, | |
| year = {2017}, | |
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| series = {Proceedings of Machine Learning Research}, | |
| publisher = {PMLR}, | |
| url = {https://proceedings.mlr.press/v70/kusner17a.html} | |
| } | |
| @article{guacamol, | |
| title = {{GuacaMol: Benchmarking Models for de Novo Molecular Design}}, | |
| author = {Brown, Nathan and Fiscato, Marco and Segler, Marwin H.S. and Vaucher, Alain C.}, | |
| journal = {Journal of Chemical Information and Modeling}, | |
| year = {2019}, | |
| volume = {59}, | |
| number = {3}, | |
| pages = {1096--1108}, | |
| doi = {10.1021/acs.jcim.8b00839}, | |
| url = {https://doi.org/10.1021/acs.jcim.8b00839} | |
| } | |
| @inproceedings{ho2020ddpm, | |
| title = {{Denoising Diffusion Probabilistic Models}}, | |
| author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter}, | |
| year = {2020}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| volume = {33}, | |
| url = {https://proceedings.neurips.cc/paper_files/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html}, | |
| pages = {6840--6851} | |
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| @inproceedings{holderrieth2025generatormatching, | |
| title = {{Generator Matching: Generative modeling with arbitrary Markov processes}}, | |
| author = {Holderrieth, Peter and Havasi, Marton and Yim, Jason and Shaul, Neta and Gat, Itai and Jaakkola, Tommi and Karrer, Brian and Chen, Ricky T. Q. and Lipman, Yaron}, | |
| year = {2025}, | |
| booktitle = {International Conference on Learning Representations}, | |
| url = {https://proceedings.iclr.cc/paper_files/paper/2025/hash/819aaee144cb40e887a4aa9e781b1547-Abstract-Conference.html}, | |
| pages = {52153--52219} | |
| } | |
| @inproceedings{holderrieth2026diamondmaps, | |
| title = {{Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps}}, | |
| author = {Peter Holderrieth and Douglas Chen and Luca Eyring and Ishin Shah and Giri Anantharaman and Yutong He and Zeynep Akata and Tommi Jaakkola and Nicholas Boffi and Max Simchowitz}, | |
| year = {2026}, | |
| booktitle = {International Conference on Machine Learning}, | |
| url = {https://icml.cc/virtual/2026/poster/61156} | |
| } | |
| @inproceedings{holderrieth2026glass, | |
| title = {{GLASS Flows: Efficient Inference for Reward Alignment of Flow and Diffusion Models}}, | |
| author = {Holderrieth, Peter and Singer, Uriel and Jaakkola, Tommi and Chen, Ricky T. Q. and Lipman, Yaron and Karrer, Brian}, | |
| year = {2026}, | |
| booktitle = {International Conference on Learning Representations}, | |
| url = {https://proceedings.iclr.cc/paper_files/paper/2026/hash/ad6ac52e02e14e0f2ddc0d4587ab47f1-Abstract-Conference.html}, | |
| pages = {106232--106266} | |
| } | |
| @inproceedings{imf, | |
| title = {{Diffusion Schr\"odinger Bridge Matching}}, | |
| author = {Shi, Yuyang and De Bortoli, Valentin and Campbell, Andrew and Doucet, Arnaud}, | |
| year = {2023}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| volume = {36}, | |
| doi = {10.52202/075280-2717}, | |
| url = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/c428adf74782c2092d254329b6b02482-Abstract-Conference.html}, | |
| pages = {62183--62223} | |
| } | |
| @inproceedings{jtvae, | |
| title = {{Junction Tree Variational Autoencoder for Molecular Graph Generation}}, | |
| author = {Jin, Wengong and Barzilay, Regina and Jaakkola, Tommi}, | |
| booktitle = {Proceedings of the 35th International Conference on Machine Learning}, | |
| pages = {2323--2332}, | |
| year = {2018}, | |
| volume = {80}, | |
| series = {Proceedings of Machine Learning Research}, | |
| publisher = {PMLR}, | |
| url = {https://proceedings.mlr.press/v80/jin18a.html} | |
| } | |
| @inproceedings{kim2024ctm, | |
| title = {{Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion}}, | |
| author = {Kim, Dongjun and Lai, Chieh-Hsin and Liao, WeiHsiang and Murata, Naoki and Takida, Yuhta and Uesaka, Toshimitsu and He, Yutong and Mitsufuji, Yuki and Ermon, Stefano}, | |
| year = {2024}, | |
| booktitle = {International Conference on Learning Representations}, | |
| url = {https://proceedings.iclr.cc/paper_files/paper/2024/hash/c204d12afa0175285e5aac65188808b4-Abstract-Conference.html}, | |
| pages = {44493--44525} | |
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| @inproceedings{kiyohara2025neuralstochasticflows, | |
| title = {{Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions}}, | |
| author = {Kiyohara, Naoki and Johns, Edward and Li, Yingzhen}, | |
| year = {2025}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| volume = {38}, | |
| doi = {10.52202/085713-0571}, | |
| url = {https://proceedings.neurips.cc/paper_files/paper/2025/hash/18abbeef8cfe9203fdf9053c9c4fe191-Abstract-Conference.html}, | |
| pages = {16917--16962} | |
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| @inproceedings{lipman2023flowmatching, | |
| title = {{Flow Matching for Generative Modeling}}, | |
| author = {Yaron Lipman and Ricky T. Q. Chen and Heli Ben-Hamu and Maximilian Nickel and Matthew Le}, | |
| year = {2023}, | |
| booktitle = {International Conference on Learning Representations}, | |
| url = {https://iclr.cc/virtual/2023/poster/11309} | |
| } | |
| @article{litpcba, | |
| title = {{LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening}}, | |
| author = {Tran-Nguyen, Viet-Khoa and Jacquemard, C\'elien and Rognan, Didier}, | |
| journal = {Journal of Chemical Information and Modeling}, | |
| year = {2020}, | |
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| @inproceedings{liu2023rectifiedflow, | |
| title = {{Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow}}, | |
| author = {Xingchao Liu and Chengyue Gong and Qiang Liu}, | |
| year = {2023}, | |
| booktitle = {International Conference on Learning Representations}, | |
| url = {https://iclr.cc/virtual/2023/poster/11266} | |
| } | |
| @inproceedings{lou2024sedd, | |
| title = {{Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution}}, | |
| author = {Lou, Aaron and Meng, Chenlin and Ermon, Stefano}, | |
| booktitle = {Proceedings of the 41st International Conference on Machine Learning}, | |
| pages = {32819--32848}, | |
| year = {2024}, | |
| volume = {235}, | |
| series = {Proceedings of Machine Learning Research}, | |
| publisher = {PMLR}, | |
| url = {https://proceedings.mlr.press/v235/lou24a.html} | |
| } | |
| @inproceedings{mccallum2026strongstochasticflowmaps, | |
| title = {{Strong Stochastic Flow Maps}}, | |
| author = {McCallum, Sam and Blasingame, Zander W. and Herschell, Timothy and Rindtorff, Niklas and Tong, Alexander and Foster, James}, | |
| year = {2026}, | |
| url = {https://openreview.net/forum?id=X0Bj1j4vbW}, | |
| booktitle = {ICML Workshop on Structured Probabilistic Inference and Generative Modeling} | |
| } | |
| @inproceedings{min, | |
| title = {{Model Inversion Networks for Model-Based Optimization}}, | |
| author = {Kumar, Aviral and Levine, Sergey}, | |
| year = {2020}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| volume = {33}, | |
| url = {https://proceedings.neurips.cc/paper_files/paper/2020/hash/373e4c5d8edfa8b74fd4b6791d0cf6dc-Abstract.html}, | |
| pages = {5126--5137} | |
| } | |
| @article{mmd, | |
| author = {Arthur Gretton and Karsten M. Borgwardt and Malte J. Rasch and Bernhard Sch{{\"o}}lkopf and Alexander Smola}, | |
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| @inproceedings{mogfn, | |
| title = {{Multi-Objective {GF}low{N}ets}}, | |
| author = {Jain, Moksh and Raparthy, Sharath Chandra and Hern\'{a}ndez-Garc\'{\i}a, Alex and Rector-Brooks, Jarrid and Bengio, Yoshua and Miret, Santiago and Bengio, Emmanuel}, | |
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| @article{moleculenet, | |
| title = {{MoleculeNet: a benchmark for molecular machine learning}}, | |
| author = {Wu, Zhenqin and Ramsundar, Bharath and Feinberg, Evan~N. and Gomes, Joseph and Geniesse, Caleb and Pappu, Aneesh S. and Leswing, Karl and Pande, Vijay}, | |
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| @inproceedings{molopt, | |
| title = {{{M}ol{D}iff: Addressing the Atom-Bond Inconsistency Problem in 3{D} Molecule Diffusion Generation}}, | |
| author = {Peng, Xingang and Guan, Jiaqi and Liu, Qiang and Ma, Jianzhu}, | |
| booktitle = {Proceedings of the 40th International Conference on Machine Learning}, | |
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| volume = {202}, | |
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| @article{morgan, | |
| title = {{Extended-Connectivity Fingerprints}}, | |
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| @article{moses, | |
| title = {{Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models}}, | |
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| @inproceedings{pmo, | |
| title = {{Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization}}, | |
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| booktitle = {Advances in Neural Information Processing Systems}, | |
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| @inproceedings{potaptchik2026metaflowmaps, | |
| title = {{Meta Flow Maps enable scalable reward alignment}}, | |
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| booktitle = {International Conference on Machine Learning}, | |
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| booktitle = {Advances in Neural Information Processing Systems}, | |
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| title = {{Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor}}, | |
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| title = {{Simple and Effective Masked Diffusion Language Models}}, | |
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| title = {{Simulation-Free {S}chr\"odinger Bridges via Score and Flow Matching}}, | |
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| @inproceedings{shaul2025generaldiscretepaths, | |
| title = {{Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective}}, | |
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| @inproceedings{shi2024simplifiedmasked, | |
| title = {{Simplified and Generalized Masked Diffusion for Discrete Data}}, | |
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| @inproceedings{sohldickstein2015nonequilibrium, | |
| title = {{Deep Unsupervised Learning using Nonequilibrium Thermodynamics}}, | |
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| @inproceedings{song2021scorebased, | |
| title = {{Score-Based Generative Modeling through Stochastic Differential Equations}}, | |
| author = {Yang Song and Jascha Sohl-Dickstein and Durk Kingma and Abhishek Kumar and Stefano Ermon and Ben Poole}, | |
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| @inproceedings{song2023consistencymodels, | |
| title = {{Consistency Models}}, | |
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| @inproceedings{stark2024dirichletflow, | |
| title = {{{D}irichlet Flow Matching with Applications to {DNA} Sequence Design}}, | |
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| title = {{Learning {GF}low{N}ets From Partial Episodes For Improved Convergence And Stability}}, | |
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| @inproceedings{synflownet, | |
| title = {{SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints}}, | |
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| title = {{Generative AI for designing and validating easily synthesizable and structurally novel antibiotics}}, | |
| author = {Swanson, Kyle and Liu, Gary and Catacutan, Denise B. and Arnold, Autumn and Zou, James and Stokes, Jonathan M.}, | |
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| title = {{Barking up the right tree: an approach to search over molecule synthesis DAGs}}, | |
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| author = {Bradshaw, John and Paige, Brooks and Kusner, Matt J and Segler, Marwin and Hern\'{a}ndez-Lobato, Jos\'{e} Miguel}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| publisher = {Curran Associates, Inc.}, | |
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| title = {{{D}ecomp{D}iff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design}}, | |
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| @inproceedings{tb, | |
| title = {{Trajectory balance: Improved credit assignment in GFlowNets}}, | |
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| booktitle = {Advances in Neural Information Processing Systems}, | |
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| title = {{Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development}}, | |
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| booktitle = {NeurIPS Datasets and Benchmarks}, | |
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| title = {{Improving and generalizing flow-based generative models with minibatch optimal transport}}, | |
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| title = {{AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading}}, | |
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| @article{vinaupdate, | |
| title = {{AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings}}, | |
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