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Predicting synergistic drug combinations can help accelerate discovery of cancer treatments, particularly therapies personalized to a patient's specific tumor via biopsied cells.
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Extracellular matrix-dependent pathways in colorectal cancer cell lines reveal potential targets for anticancer therapies
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Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Reza Bayat Mokhtari, Tina S Homayouni, Narges Baluch, Evgeniya Morgatskaya, Sushil Kumar, Bikul Das, and Herman Yeger · 2017
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Edmund A Mroz and James W Rocco · 2017
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Deepsynergy: predicting anti-cancer drug synergy with deep learning
Kristina Preuer, Richard PI Lewis, Sepp Hochreiter, Andreas Bender, Krishna C Bulusu, and Günter Klambauer · 2018
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Inverse molecular design using machine learning: Generative models for matter engineering
Benjamin Sanchez-Lengeling and Alán Aspuru-Guzik · 2018
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Scibert: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials
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Nils Reimers and Iryna Gurevych · 2019
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Mr-gnn: Multi-resolution and dual graph neural network for predicting structured entity interactions
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Kg-bert: Bert for knowledge graph completion
Liang Yao, Chengsheng Mao, and Yuan Luo · 2019
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Drugcomb: an integrative cancer drug combination data portal
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Language models are few-shot learners
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Chemberta: Large-scale self-supervised pretraining for molecular property prediction
Seyone Chithrananda, Gabe Grand, and Bharath Ramsundar · 2020
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Molecular representation learning with language models and domain-relevant auxiliary tasks
Natural language processing models that automate programming will transform chemistry research and teaching
Glen M Hocky and Andrew D White · 2022
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Pisces: A combo-wise contrastive learning approach to synergistic drug combination prediction
Jiacheng Lin, Hanwen Xu, Addie Woicik, Jianzhu Ma, and Sheng Wang · 2022
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Multi-modal molecule structure-text model for text-based retrieval and editing
Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, and Anima Anandkumar · 2022
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Megamolbart v0.2, 2022
NVIDIA Corporation · 2022
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In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al · 2022
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Benedek Fabian, Thomas Edlich, Héléna Gaspar, Marwin Segler, Joshua Meyers, Marco Fiscato, and Mohamed Ahmed · 2020
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Hierarchical generation of molecular graphs using structural motifs
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Multi-task learning for knowledge graph completion with pre-trained language models
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Self-referencing embedded strings (selfies): A 100% robust molecular string representation
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Predicting drug response and synergy using a deep learning model of human cancer cells
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Structure-based drug-drug interaction detection via expressive graph convolutional networks and deep sets (student abstract)
Mengying Sun, Fei Wang, Olivier Elemento, and Jiayu Zhou · 2020
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Transformers learn in-context by gradient descent
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Deepdds: deep graph neural network with attention mechanism to predict synergistic drug combinations
Jinxian Wang, Xuejun Liu, Siyuan Shen, Lei Deng, and Hui Liu · 2022
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Do large language models know chemistry?
Andrew D White, Glen M Hocky, Heta A Gandhi, Mehrad Ansari, Sam Cox, Geemi P Wellawatte, Subarna Sasmal, Ziyue Yang, Kangxin Liu, Yuvraj Singh, et al · 2022
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Protranslator: zero-shot protein function prediction using textual description
Hanwen Xu and Sheng Wang · 2022
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Linkbert: Pretraining language models with document links
Michihiro Yasunaga, Jure Leskovec, and Percy Liang · 2022
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Kglm: Integrating knowledge graph structure in language models for link prediction
Jason Youn and Ilias Tagkopoulos · 2022
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A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals
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Emergent autonomous scientific research capabilities of large language models
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