Autonomous chemical research with large language models
Brown, T. et al. in Advances in Neural Information Processing Systems Vol. 33 (eds Larochelle, H. et al.) 1877–1901 (Curran Associates, 2020).
Thoppilan, R. et al. LaMDA: language models for dialog applications. Preprint at (2022).
Touvron, H. et al. LLaMA: open and efficient foundation language models. Preprint at (2023).
Hoffmann, J. et al. Training compute-optimal large language models. In Advances in Neural Information Processing Systems 30016–30030 (NeurIPS, 2022).
Chowdhery, A. et al. PaLM: scaling language modeling with pathways. J. Mach. Learn. Res. 24, 1–113 (2022).
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
Google Scholar
Luo, R. et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief Bioinform. 23, bbac409 (2022).
Google Scholar
Irwin, R., Dimitriadis, S., He, J. & Bjerrum, E. J. Chemformer: a pre-trained transformer for computational chemistry. Mach. Learn. Sci. Technol. 3, 015022 (2022).
Google Scholar
Kim, H., Na, J. & Lee, W. B. Generative chemical transformer: neural machine learning of molecular geometric structures from chemical language via attention. J. Chem. Inf. Model. 61, 5804–5814 (2021).
Google Scholar
Jablonka, K. M., Schwaller, P., Ortega-Guerrero, A. & Smit, B. Leveraging large language models for predictive chemistry. Preprint at (2023).
Xu, F. F., Alon, U., Neubig, G. & Hellendoorn, V. J. A systematic evaluation of large language models of code. In Proc. 6th ACM SIGPLAN International Symposium on Machine Programming 1–10 (ACM, 2022).
Nijkamp, E. et al. CodeGen: an open large language model for code with multi-turn program synthesis. In Proc. 11th International Conference on Learning Representations (ICLR, 2022).
Kaplan, J. et al. Scaling laws for neural language models. Preprint at (2020).
OpenAI. GPT-4 Technical Report (OpenAI, 2023).
Ziegler, D. M. et al. Fine-tuning language models from human preferences. Preprint at (2019).
Ouyang, L. et al. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems 27730–27744 (NeurIPS, 2022).
Granda, J. M., Donina, L., Dragone, V., Long, D.-L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559, 377–381 (2018).
Google Scholar
Caramelli, D. et al. Discovering new chemistry with an autonomous robotic platform driven by a reactivity-seeking neural network. ACS Cent. Sci. 7, 1821–1830 (2021).
Google Scholar
Angello, N. H. et al. Closed-loop optimization of general reaction conditions for heteroaryl Suzuki–Miyaura coupling. Science 378, 399–405 (2022).
Google Scholar
Adamo, A. et al. On-demand continuous-flow production of pharmaceuticals in a compact, reconfigurable system. Science 352, 61–67 (2016).
Google Scholar
Coley, C. W. et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365, eaax1566 (2019).
Google Scholar
Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).
Google Scholar
Auto-GPT: the heart of the open-source agent ecosystem. GitHub (2023).
BabyAGI. GitHub (2023).
Chase, H. LangChain. GitHub (2023).
Bran, A. M., Cox, S., White, A. D. & Schwaller, P. ChemCrow: augmenting large-language models with chemistry tools. Preprint at (2023).
Liu, P. et al. Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55, 195 (2021).
Bai, Y. et al. Constitutional AI: harmlessness from AI feedback. Preprint at (2022).
Falcon LLM. TII (2023).
Open LLM Leaderboard. Hugging Face (2023).
Ji, Z. et al. Survey of hallucination in natural language generation. ACM Comput. Surv. 55, 248 (2023).
Google Scholar
Reaxys (2023).
SciFinder (2023).
Yao, S. et al. ReAct: synergizing reasoning and acting in language models. In Proc.11th International Conference on Learning Representations (ICLR, 2022).
Wei, J. et al. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems 24824–24837 (NeurIPS, 2022).
Long, J. Large language model guided tree-of-thought. Preprint at (2023).
Opentrons Python Protocol API. Opentrons (2023).
Tu, Z. et al. Approximate nearest neighbor search and lightweight dense vector reranking in multi-stage retrieval architectures. In Proc. 2020 ACM SIGIR on International Conference on Theory of Information Retrieval 97–100 (ACM, 2020).
Lin, J. et al. Pyserini: a python toolkit for reproducible information retrieval research with sparse and dense representations. In Proc. 44th International ACM SIGIR Conference on Research and Development in Information Retrieval 2356–2362 (ACM, 2021).
Qadrud-Din, J. et al. Transformer based language models for similar text retrieval and ranking. Preprint at (2020).
Paper QA. GitHub (2023).
Robertson, S. & Zaragoza, H. The probabilistic relevance framework: BM25 and beyond. Found. Trends Inf. Retrieval 3, 333–389 (2009).
Google Scholar
Data Mining. Mining of Massive Datasets (Cambridge Univ., 2011).
Johnson, J., Douze, M. & Jegou, H. Billion-scale similarity search with GPUs. IEEE Trans. Big Data 7, 535–547 (2021).
Google Scholar
Vechtomova, O. & Wang, Y. A study of the effect of term proximity on query expansion. J. Inf. Sci. 32, 324–333 (2006).
Google Scholar
Running experiments. Emerald Cloud Lab (2023).
Sanchez-Garcia, R. et al. CoPriNet: graph neural networks provide accurate and rapid compound price prediction for molecule prioritisation. Digital Discov. 2, 103–111 (2023).
Google Scholar
Bubeck, S. et al. Sparks of artificial general intelligence: early experiments with GPT-4. Preprint at (2023).
Ramos, M. C., Michtavy, S. S., Porosoff, M. D. & White, A. D. Bayesian optimization of catalysts with in-context learning. Preprint at (2023).
Perera, D. et al. A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science 359, 429–434 (2018).
Google Scholar
Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D. & Doyle, A. G. Predicting reaction performance in C–N cross-coupling using machine learning. Science 360, 186–190 (2018).
Google Scholar
Hickman, R. et al. Atlas: a brain for self-driving laboratories. Preprint at (2023).
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