Fetching the paper…
Reading the bibliography…
The success of language models, especially transformer-based architectures, has trickled into other domains giving rise to "scientific language models" that operate on small molecules, proteins or polymers.
Ibm internal report
Taffee T Tanimoto · 1957
Earlier work this paper cites.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
Earlier work this paper cites.
Prediction of physicochemical parameters by atomic contributions
Scott A Wildman and Gordon M Crippen · 1999
Earlier work this paper cites.
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
Earlier work this paper cites.
Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
Earlier work this paper cites.
Quantifying the chemical beauty of drugs
G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins · 2012
Earlier work this paper cites.
Serendipity in anticancer drug discovery
Emily Hargrave-Thomas, Bo Yu, and Jóhannes Reynisson · 2012
Earlier work this paper cites.
Diagnosing the decline in pharmaceutical r&d efficiency
Jack W Scannell, Alex Blanckley, Helen Boldon, and Brian Warrington · 2012
Earlier work this paper cites.
Rdkit documentation
Greg Landrum · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Estimation of the size of drug-like chemical space based on gdb-17 data
Pavel G Polishchuk, Timur I Madzhidov, and Alexandre Varnek · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Open source and open data should be standard practices, 2015
J Daniel Gezelter · 2015
Earlier work this paper cites.
Inchi, the iupac international chemical identifier
Stephen R Heller, Alan McNaught, Igor Pletnev, Stephen Stein, and Dmitrii Tchekhovskoi · 2015
Earlier work this paper cites.
Inferring algorithmic patterns with stack-augmented recurrent nets
Armand Joulin and Tomas Mikolov · 2015
Earlier work this paper cites.
Smiles enumeration as data augmentation for neural network modeling of molecules
Esben Jannik Bjerrum · 2017
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
Earlier work this paper cites.
Molecular generative model based on conditional variational autoencoder for de novo molecular design
Jaechang Lim, Seongok Ryu, Jin Woo Kim, and Woo Youn Kim · 2018
Earlier work this paper cites.
Deep reinforcement learning for de novo drug design
Mariya Popova, Olexandr Isayev, and Alexander Tropsha · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Earlier work this paper cites.
“found in translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models
Philippe Schwaller, Theophile Gaudin, David Lanyi, Costas Bekas, and Teodoro Laino · 2018
Earlier work this paper cites.
Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller · 2018
Earlier work this paper cites.
Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
Earlier work this paper cites.
Gradio: Hassle-free sharing and testing of ml models in the wild
Abubakar Abid, Ali Abdalla, Ali Abid, Dawood Khan, Abdulrahman Alfozan, and James Zou · 2019
Earlier work this paper cites.
Randomized smiles strings improve the quality of molecular generative models
Josep Arús-Pous, Simon Viet Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, and Ola Engkvist · 2019
Earlier work this paper cites.
Guacamol: benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher · 2019
Earlier work this paper cites.
Pubchem 2019 update: improved access to chemical data
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, et al · 2019
Earlier work this paper cites.
Bigsmiles: a structurally-based line notation for describing macromolecules
Tzyy-Shyang Lin, Connor W Coley, Hidenobu Mochigase, Haley K Beech, Wencong Wang, Zi Wang, Eliot Woods, Stephen L Craig, Jeremiah A Johnson, Julia A Kalow, et al · 2019
Earlier work this paper cites.
Molecule-augmented attention transformer
Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, and S Jastrzkebski · 2019
Earlier work this paper cites.
Deep Learning for the Life Sciences
Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, Karl Leswing, and Zhenqin Wu · 2019
Earlier work this paper cites.
Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A Hunter, Costas Bekas, and Alpha A Lee · 2019
Cited alongside, same era.
Augmentation is what you need!
Igor V Tetko, Pavel Karpov, Eric Bruno, Talia B Kimber, and Guillaume Godin · 2019
Cited alongside, same era.
Deep learning enables rapid identification of potent ddr1 kinase inhibitors
Alex Zhavoronkov, Yan A Ivanenkov, Alex Aliper, Mark S Veselov, Vladimir A Aladinskiy, Anastasiya V Aladinskaya, Victor A Terentiev, Daniil A Polykovskiy, Maksim D Kuznetsov, Arip Asadulaev, et al · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Chemberta: large-scale self-supervised pretraining for molecular property prediction
Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar · 2020
Cited alongside, same era.
Towards artificial general intelligence via a multimodal foundation model
Nanyi Fei, Zhiwu Lu, Yizhao Gao, Guoxing Yang, Yuqi Huo, Jingyuan Wen, Haoyu Lu, Ruihua Song, Xin Gao, Tao Xiang, et al · 2022
Later among the works it cites.
Language models can learn complex molecular distributions
Daniel Flam-Shepherd, Kevin Zhu, and Alán Aspuru-Guzik · 2022
Later among the works it cites.
Huggingmolecules: An open-source library for transformer-based molecular property prediction (student abstract)
Piotr Gaiński, Łukasz Maziarka, Tomasz Danel, and Stanisław Jastrzebski · 2022
Later among the works it cites.
Melloddy: cross pharma federated learning at unprecedented scale unlocks benefits in qsar without compromising proprietary information
Wouter Heyndrickx, Lewis Mervin, Tobias Morawietz, Noé Sturm, Lukas Friedrich, Adam Zalewski, Anastasia Pentina, Lina Humbeck, Martijn Oldenhof, Ritsuya Niwayama, et al · 2022
Later among the works it cites.
A fully differentiable set autoencoder
Nikita Janakarajan, Jannis Born, and Matteo Manica · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Molecular representation learning with language models and domain-relevant auxiliary tasks
Benedek Fabian, Thomas Edlich, Héléna Gaspar, Marwin Segler, Joshua Meyers, Marco Fiscato, and Mohamed Ahmed · 2020
Cited alongside, same era.
Aizynthfinder: a fast, robust and flexible open-source software for retrosynthetic planning
Samuel Genheden, Amol Thakkar, Veronika Chadimová, Jean-Louis Reymond, Ola Engkvist, and Esben Bjerrum · 2020
Cited alongside, same era.
An open-source drug discovery platform enables ultra-large virtual screens
Christoph Gorgulla, Andras Boeszoermenyi, Zi-Fu Wang, Patrick D Fischer, Paul W Coote, Krishna M Padmanabha Das, Yehor S Malets, Dmytro S Radchenko, Yurii S Moroz, David A Scott, et al · 2020
Cited alongside, same era.
Self-referencing embedded strings (selfies): A 100% robust molecular string representation
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik · 2020
Cited alongside, same era.
Inductive transfer learning for molecular activity prediction: Next-gen qsar models with molpmofit
Xinhao Li and Denis Fourches · 2020
Cited alongside, same era.
Transfer learning enables the molecular transformer to predict regio-and stereoselective reactions on carbohydrates
Giorgio Pesciullesi, Philippe Schwaller, Teodoro Laino, and Jean-Louis Reymond · 2020
Cited alongside, same era.
Molecular sets (moses): a benchmarking platform for molecular generation models
Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, et al · 2020
Cited alongside, same era.
Unified deep learning model for multitask reaction predictions with explanation
Jieyu Lu and Yingkai Zhang · 2022
Later among the works it cites.
Learning to extend molecular scaffolds with structural motif
Krzysztof Maziarz, Henry Jackson-Flux, Pashmina Cameron, Finton Sirockin, Nadine Schneider, Nikolaus Stiefl, Marwin Segler, and Marc Brockschmidt · 2022
Later among the works it cites.
Biocatalysed synthesis planning using data-driven learning
Daniel Probst, Matteo Manica, Yves Gaetan Nana Teukam, Alessandro Castrogiovanni, Federico Paratore, and Teodoro Laino · 2022
Later among the works it cites.
Large-scale chemical language representations capture molecular structure and properties
Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, and Payel Das · 2022
Later among the works it cites.
Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al · 2022
Later among the works it cites.
Machine intelligence for chemical reaction space
Philippe Schwaller, Alain C Vaucher, Ruben Laplaza, Charlotte Bunne, Andreas Krause, Clemence Corminboeuf, and Teodoro Laino · 2022
Later among the works it cites.
Galactica: A large language model for science
Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic · 2022
Later among the works it cites.
Diffusers: State-of-the-art diffusion models, 10 2022
Patrick von Platen, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj, and Thomas Wolf · 2022
Later among the works it cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Later among the works it cites.
A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals
Zheni Zeng, Yuan Yao, Zhiyuan Liu, and Maosong Sun · 2022
Later among the works it cites.
Torchdrug: A powerful and flexible machine learning platform for drug discovery
Zhaocheng Zhu, Chence Shi, Zuobai Zhang, Shengchao Liu, Minghao Xu, Xinyu Yuan, Yangtian Zhang, Junkun Chen, Huiyu Cai, Jiarui Lu, et al · 2022
Later among the works it cites.
https://copilot.github.com/ , 2021
Github copilot · 2023
Closest in time.
Regression transformer enables concurrent sequence regression and generation for molecular language modelling
Jannis Born and Matteo Manica · 2023
Closest in time.
Chemical representation learning for toxicity prediction
Jannis Born, Greta Markert, Nikita Janakarajan, Talia B Kimber, Andrea Volkamer, María Rodríguez Martínez, and Matteo Manica · 2023
Closest in time.
Do large language models understand chemistry? a conversation with chatgpt
Cayque Monteiro Castro Nascimento and André Silva Pimentel · 2023
Closest in time.
Unifying molecular and textual representations via multi-task language modelling
Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, and Matteo Manica · 2023
Closest in time.
rxn4chemistry: Python wrapper for the IBM RXN for Chemistry API
IBM RXN for Chemistry team · 2023
Closest in time.
Chemical language models for de novo drug design: Challenges and opportunities
Francesca Grisoni · 2023
Closest in time.
Chemistry42: an ai-driven platform for molecular design and optimization
Yan A Ivanenkov, Daniil Polykovskiy, Dmitry Bezrukov, Bogdan Zagribelnyy, Vladimir Aladinskiy, Petrina Kamya, Alex Aliper, Feng Ren, and Alex Zhavoronkov · 2023
Closest in time.
Evolutionary-scale prediction of atomic-level protein structure with a language model
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, et al · 2023
Closest in time.
Accelerating material design with the generative toolkit for scientific discovery
Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gaetan Nana Teukam, Giorgio Giannone, Samuel C Hoffman, Matthew Buchan, et al · 2023
Closest in time.
Molecule generation using transformers and policy gradient reinforcement learning
Eyal Mazuz, Guy Shtar, Bracha Shapira, and Lior Rokach · 2023
Closest in time.
Foundation models for generalist medical artificial intelligence
Michael Moor, Oishi Banerjee, Zahra Shakeri Hossein Abad, Harlan M Krumholz, Jure Leskovec, Eric J Topol, and Pranav Rajpurkar · 2023
Closest in time.
Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language
Nathaniel H Park, Matteo Manica, Jannis Born, James L Hedrick, Tim Erdmann, Dmitry Yu Zubarev, Nil Adell-Mill, and Pedro L Arrechea · 2023
Closest in time.
Designing catalysts with deep generative models and computational data. a case study for suzuki cross coupling reactions
Oliver Schilter, Alain Vaucher, Philippe Schwaller, and Teodoro Laino · 2023
Closest in time.
Unbiasing retrosynthesis language models with disconnection prompts
Amol Thakkar, Alain C Vaucher, Andrea Byekwaso, Philippe Schwaller, Alessandra Toniato, and Teodoro Laino · 2023
Closest in time.
Improving the quality of chemical language model outcomes with atom-in-smiles tokenization
Umit V Ucak, Islambek Ashyrmamatov, and Juyong Lee · 2023
Closest in time.
Assessment of chemistry knowledge in large language models that generate code
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 · 2023
Closest in time.